A Survey on Hough Transform, Theory, Techniques and Applications
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paper_title: Use of the Hough transformation to detect lines and curves in pictures

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Hough has proposed an interesting and computationally efficient procedure for detecting lines in pictures. This paper points out that the use of angle-radius rather than slope-intercept parameters simplifies the computation further. It also shows how the method can be used for more general curve fitting, and gives alternative interpretations that explain the source of its efficiency.
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paper_title: A survey of the Hough transform

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We present a comprehensive review of the Hough transform, HT, in image processing and computer vision. It has long been recognized as a technique of almost unique promise for shape and motion analysis in images containing noisy, missing, and extraneous data but its adoption has been slow due to its computational and storage complexity and the lack of a detailed understanding of its properties. However, in recent years much progress has been made in these areas. In this review we discuss ideas for the efficient implementation of the HT and present results on the analytic and empirical performance of various methods. We also report the relationship of Hough methods and other transforms and consider applications in which the HT has been used. It seems likely that the HT will be an increasingly used technique and we hope that this survey will provide a useful guide to quickly acquaint researchers with the main literature in this research area.
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paper_title: A survey of efficient Hough transform methods

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The Hough Transform, HT, has long been recognised as a technique of almost unique promise for shape and motion analysis in images containing noisy, missing and extraneous data. However its widespread adoption in practical systems has been slow due to its computational complexity, the need for large storage arrays and the lack of a detailed understanding of its properties. The aims of Alvey project MMI/IP078 are • the investigation of the properties of HTs • the study of efficient implementations of HTs • the production of a HT hardware device for use in real time industrial inspection systems. • the development of general high level image interpretation strategies which use information derived from the HT. The first three items listed above are being studied at Surrey University in collaboration with Computer Recognition Systems while the final item is under investigation at Heriot Watt University. There has recently been much activity concerned with space and time efficient implementations of HT ideas and this paper presents a survey of this area. Topics covered include the development of new algorithms, the use of optical techniques and the implementation of HTs on novel hardware architectures.
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paper_title: Vanishing point detection in corridors: using hough transform and K-means clustering

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One of the main challenges in steering a vehicle or a robot is the detection of appropriate heading. Many solutions have been proposed during the past few decades to overcome the difficulties of intelligent navigation platforms. In this study, the authors try to introduce a new procedure for finding the vanishing point based on the visual information and K-Means clustering. Unlike other solutions the authors do not need to find the intersection of lines to extract the vanishing point. This has reduced the complexity and the processing time of our algorithm to a large extent. The authors have imported the minimum possible information to the Hough space by using only two pixels (the points) of each line (start point and end point) instead of hundreds of pixels that form a line. This has reduced the mathematical complexity of our algorithm while maintaining very efficient functioning. The most important and unique characteristic of our algorithm is the usage of processed data for other important tasks in navigation such as mapping and localisation.
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paper_title: A New Vanishing Point Detection Algorithm Based on Hough Transform

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Detecting vanishing points in digital image is important for 3D reconstruction of a scene using no calibrated cameras. A new vanishing point detection algorithm based on twice Hough Transform was presented in this paper. Hough Transform has been recognized as one more popular method for the detection of straight lines. Firstly, the straight lines that in the image are detected by first Hough Transform. Secondly, using a coordinate transform, points in a circle were translated to points in a line. Finally, for detection of the line, Hough Transform was used again. Thus, the positions of vanishing points can be calculated by corresponding to the line parameters in pole coordinate. Experimental results using real images of a scene demonstrate that the proposed algorithm is effective and can acquire the vanishing point.
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paper_title: Probabilistic and non-probabilistic Hough transforms: overview and comparisons

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Abstract A new and efficient version of the Hough transform for curve detection, the Randomized Hough Transform (RHT), has been recently suggested. The RHT selects n pixels from an edge image by random sampling to solve n parameters of a curve and then accumulates only one cell in a parameter space. In this paper, the RHT is related to other recent developments of the Hough transform. Hough transform methods are divided into two categories: probabilistic and non-probabilistic methods. An overview of these variants is given. Some novel extensions of the RHT are proposed to improve the RHT for complex and noisy images. These new versions of the RHT, called the Dynamic RHT, and the Window RHT with its variants, use local information of the edge image. They apply the RHT process to a limited neighbourhood of edge pixels. Tests in line detection with synthetic and real-world images demonstrate the high speed and low memory usage of the new extensions, as compared both to the basic RHT and other versions of the Hough transform.
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paper_title: Vanishing points in point-to-line mappings and other line parameterizations

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Some variants of the Hough transform can be used for detecting vanishing points and groups of concurrent lines. This article addresses a common misconception that in the polar line parameterization the vanishing point is represented by a line. The numerical error caused by this inaccuracy is then estimated. The article studies in detail point-to-line-mappings (PTLMs) - a class of line parameterizations which have the property that the vanishing point is represented by a line (and thus can be easily searched for). When a PTLM parameterization is used for the straight line detection by the Hough transform, a pair or a triplet of complementary PTLMs has to be used in order to obtain a limited Hough space. The complementary pairs and triplets of PTLMs are formalized and discussed in this article.
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paper_title: Detecting corners using the 'patchy' Hough transform

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Relative vehicle motion estimation continues to be difficult. Current approaches are generally based on either optical flow or tracking feature points. In heavily carpentered environments such as institutional settings, the feature-point tracking approach requires significantly less computational effort. Algorithms which result in reduced computational effort are very attractive for the autonomous, vision-guided wheelchair project currently underway in our laboratory. We present a new method for detecting corners as feature points using a 'patchy' Hough transform. Corners are detected as intersections of straight lines extracted with the Hough transform. The image is initially segmented into patches, and only the intersections that occur within a patch due to edges also within the patch are deemed to be corners. This helps to avoid the localization problem inherent in the normal Hough transform. The use of patches also reduces the tendency of the Hough transform to ignore short edges because they are 'swamped' by longer ones. Edge pixels are initially enhanced by the Sobel operator, then detected using a histogram-derived threshold. The thresholded images are 'thinned' to produce edges of single-pixel width so as to avoid the line orientation ambiguity that arises with thick edges. The Hough transform is then computed for each patch. Peaks in the Hough accumulator array are generally poorly defined, with many adjacent accumulator 'bins' having similar values. To sharpen peaks, the Hough accumulator array is convolved with an 'annulus' kernel. The peaks are then detected as the local maxima of the convolution result. Finally, the intersections of all possible combinations of lines corresponding to the detected peaks are found. If the points of intersection lay inside the current patch, the lines are deemed to intersect. Algorithm performance is demonstrated with both synthetic and real image data. >
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paper_title: A gridding Hough transform for detecting the straight lines in sports video

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A gridding Hough transform (GHT) is proposed to detect the straight lines in sports video, which is much faster and requires much less memory than the previous Hough transforms. The GHT uses the active gridding to replace the random point selection in the random Hough transforms because forming the linelets from the actively selected points is easier than from the randomly selected points. Existing straight-line Hough transforms require a lot of resources because they were designed for all kinds of straight lines. Considering the fact that the straight lines interested in sports video are long and sparse, this paper proposes two techniques: the active gridding and linelets process. On account of these two techniques, the proposed GHT is fast and uses little memory. The experimental results show that the proposed GHT is faster than the random Hough transform (RHT) and the standard Hough transform (SHT) by 30% and 700% respectively and achieves a 97.5% recall, higher than those achieved by either the SHT or the RHT.
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paper_title: Use of the Hough transformation to detect lines and curves in pictures

paper_content:

Hough has proposed an interesting and computationally efficient procedure for detecting lines in pictures. This paper points out that the use of angle-radius rather than slope-intercept parameters simplifies the computation further. It also shows how the method can be used for more general curve fitting, and gives alternative interpretations that explain the source of its efficiency.
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paper_title: Robust Detection of Lines Using the Progressive Probabilistic Hough Transform

paper_content:

In the paper we present the progressive probabilistic Hough transform (PPHT). Unlike the probabilistic HT, where the standard HT is performed on a preselected fraction of input points, the PPHT minimizes the amount of computation needed to detect lines by exploiting the difference in the fraction of votes needed to reliably detect lines with different numbers of supporting points. The fraction of points used for voting need not be specified ad hoc or using a priori knowledge, as in the probabilistic HT; it is a function of the inherent complexity of data. The algorithm is ideally suited for real-time applications with a fixed amount of available processing time, since voting and line detection are interleaved. The most salient features are likely to be detected first. While retaining its robustness, experiments show that the PPHT has, in many circumstances, advantages over the standard HT.
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paper_title: On improving the accuracy of the Hough transform: theory, simulations, and experiments

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The authors present two methods for very-high-precision estimation of straight-line parameters from the Hough transform and compare them with the standard method of taking the absolute peak in the Hough array and with least-squares fitting using both extensive simulation and a number of tests with real target images. Both methods use preprocessing and interpolation in the Hough array, and are based on compensating for effects that cause a spreading of the peak in Hough space. By interpolation, the authors achieve accuracy better than the accumulator cell size. A complete set of simulations show that the two methods produce similar results, which are much better than taking the absolute peak in Hough space. They also compare well with least-square fitting, which was considered optimal in the case of zero mean noise. Results of experiments with real images are reported, confirming that the Hough transform can yield very accurate results, almost as good as least-squares fitting for zero mean noise. >
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paper_title: Line detection in images showing significant lens distortion and application to distortion correction

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Lines are one of the basic primitives used by the perceptual system to analyze and interpret a scene. Therefore, line detection is a very important issue for the robustness and flexibility of Computer Vision systems. However, in the case of images showing a significant lens distortion, standard line detection methods fail because lines are not straight. In this paper we present a new technique to deal with this problem: we propose to extend the usual Hough representation by introducing a new parameter which corresponds to the lens distortion, in such a way that the search space is a three-dimensional space, which includes orientation, distance to the origin and also distortion. Using the collection of distorted lines which have been recovered, we are able to estimate the lens distortion, remove it and create a new distortion-free image by using a two-parameter lens distortion model. We present some experiments in a variety of images which show the ability of the proposed approach to extract lines in images showing a significant lens distortion.
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paper_title: The Cascaded Hough Transform as Support for Grouping and Finding Vanishing Points and Lines

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In the companion paper [7] a grouping strategy with a firm geometrical underpinning and without the problem of combinatorias is proposed. It is based on the exploitation of structures that remain fixed under the transformations that relate corresponding contour segments in regular patterns. In this paper we present a solution for the complementary task of extracting these fixed structures in an efficient and non-combinatorial way, based on the iterated application of the Hough transform. Apart from grouping, this ‘Cascaded Hough Transform’ or CHT for short can also be used for the detection of straight lines, vanishing points and vanishing lines.
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paper_title: Progressive probabilistic Hough transform for line detection

paper_content:

We present a novel Hough Transform algorithm referred to as Progressive Probabilistic Hough Transform (PPHT). Unlike the Probabilistic HT where Standard HT is performed on a pre-selected fraction of input points, PPHT minimises the amount of computation needed to detect lines by exploiting the difference an the fraction of votes needed to detect reliably lines with different numbers of supporting points. The fraction of points used for voting need not be specified ad hoc or using a priori knowledge, as in the probabilistic HT; it is a function of the inherent complexity of the input data. The algorithm is ideally suited for real-time applications with a fixed amount of available processing time, since voting and line detection is interleaved. The most salient features are likely to be detected first. Experiments show that in many circumstances PPHT has advantages over the Standard HT.
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paper_title: Hough-transform detection of lines in 3-D space

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Abstract Detecting straight lines in 3-D space using a Hough transform approach involves a 4-D parameter space, which is cumbersome. In this paper, we show how to detect families of parallel lines in 3-D space at a moderate computational cost by using a (2+2)-D Hough space. We first find peaks in the 2-D slope parameter space; for each of these peaks, we then find peaks in the intercept parameter space. Our experimental results on range images of boxes and blocks indicate that the method works quite well.
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paper_title: A windowing approach to detecting line segments using Hough transform

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Abstract A new approach for using the Hough transform to detect line segments is presented. This approach is efficient in both space and time. Strategies combining the features of the intersection point [Ben-Tzvi, Leavers and Sandler, Proc. 5th Intl. Conf. Image Anal. 152–159 1990); Xu, Oja and Kultannen, Pattern Recognition Lett. 11 , 331–338 (1990)] and dual plane [Conker, Comput. Vis. Graphics Image Process . 43 , 115–132 (1988)] methods are used to calculate the Hough transform. A dense set of small overlapping windows are used to restrict the pairs of inage pixels that are evaluated. Experimental results indicate that this method reduces the time and space requirements significantly.
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paper_title: Hough transform modified by line connectivity and line thickness

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A modified Hough transform based on a likelihood principle of connectivity and thickness is proposed for line detection. It makes short as well as thick line segments easier to detect in a noisy image. Certain desirable properties of the new method are justified by theory and simulations.
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paper_title: Statistical Hough Transform

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The standard Hough transform is a popular method in image processing and is traditionally estimated using histograms. Densities modeled with histograms in high dimensional space and/or with few observations, can be very sparse and highly demanding in memory. In this paper, we propose first to extend the formulation to continuous kernel estimates. Second, when dependencies in between variables are well taken into account, the estimated density is also robust to noise and insensitive to the choice of the origin of the spatial coordinates. Finally, our new statistical framework is unsupervised (all needed parameters are automatically estimated) and flexible (priors can easily be attached to the observations). We show experimentally that our new modeling encodes better the alignment content of images.
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paper_title: A comparative study of Hough transform methods for circle finding

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Abstract A variety of circle detection methods which are based on variations of the Hough Transform are investigated. The five methods considered are the standard Hough Transform, the Fast Hough Transform of Li et al. 1 , a two stage Hough method, and two space saving approaches based on the method devised by Gerig and Klein 2 . The performance of each of the methods has been compared on synthetic imagery and real images from a metallurgical application. Figures and comments are presented concerning the accuracy, reliability, computational efficiency and storage requirements of each of the methods.
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paper_title: A new concentric circle detection method based on Hough transform

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A new approach of concentric circle detection is proposed in this paper. Firstly, the image is preprocessed by denosing, edge detection, and then the circle centers are allocated by the gradient Hough transform, at last, the radius are detected by the improved one-dimensional Hough transform. The detection efficiency is enhanced by image discretization and reduced resolution ratio in the process of circle center detection, and proves that the circle center is on the gradient line of circle edge points; meanwhile, the radius detection accuracy is improved by merging the similar radius in the process of radius detection. Experimental results show that the method combined with gradient Hough transform and one-dimensional Hough transform has good reliability and is high adaptive to noise, distortion, area of incomplete, edge discontinuous. Analysis shows that this new concentric circle detection algorithm reduces time complexity and improves anti-interference compared with the traditional concentric circle detection algorithm based on chord mid-point Hough transforms.
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paper_title: Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities

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Recently, a new curve detection approach called the randomized Hough transform (RHT) was heuristically proposed by the authors, inspired by the efforts of using neural computation learning techniques for curve detection. The preliminary experimental results and some qualitative analysis showed that in comparison with the Hough transform (HT) and its variants, the RHT has advantages of fast speed, small storage, infinite range of the parameter space, and high parameter resolution, and it can overcome several difficulties encountered with the HT methods. In this paper, the basic ideas of RHT are further developed into a more systematic and theoretically supported new method for curve detection. The fundamental framework and the main components of this method are elaborated. The advantages of RHT are further confirmed. The basic mechanisms behind these advantages are exposed by both theoretical analysis and detailed experimental demonstrations. The main differences between RHT and some related techniques are elucidated. This paper also proposes several improved algorithms for implementing RHT for curve detection problems in noisy images. They are tested by experiments on images with various kinds of strong noise. The results show that the advantages of RHT are quite robust. Moreover, the implementations of these algorithms are modeled by a generalized Bernoulli process, allowing probability analysis on these algorithms to estimate their computational complexities and to decide some important parameters for their implementations. It is shown quantitatively that the complexities are considerably smaller than those of the HT.
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paper_title: Efficient randomized Hough transform for circle detection using novel probability sampling and feature points

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Abstract This paper presents an efficient randomized Hough transform algorithm for circle detection. It optimizes the methods for determining sample points and finding candidate circles. Due to these two optimizations, sampling validity is improved and many false circles are prevented from being regarded as candidate circles. Experimental results demonstrate that the proposed algorithm, which features a strong robustness and high resolution, can dramatically speed up circle detection as compared to other algorithms. It can also be applied to detect ellipses.
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paper_title: Detecting square-shaped objects using the Hough transform

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Finding squares or circles in an image by a camera is always very difficult because the projections from a conventional camera and or even a simple webcam, get clogged from environment which has boasts Noise and Shadow and Find Many images. This makes the aforementioned square bit larger Villa boasts fractures in edges and when we want to get to the center coordinates of the robot is unable to come and pick it up trouble because the square of the desired shape is obtained, the coordinates will be wrong. Therefore, this paper tries to find the square object in a camera image using Hough transform and convert it to a polygon which is smaller, then You can calculate the coordinates of the center of mass of the smaller polygon which is the square of the coordinates Because the polygon is obtained under the main square
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paper_title: Finding circles by an array of accumulators

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We describe an efficient procedure for detecting approximate circles and approximately circular arcs of varying gray levels in an edge-enhanced digitized picture. This procedure is an extension and improvement of the circle-finding concept sketched by Duda and Hart [2] as an extension of the Hough straight-line finder [6].
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paper_title: The randomized-Hough-transform-based method for great-circle detection on sphere

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We propose a randomized-Hough-transform-based method for the detection of great circles on a sphere. We first define transformations from images acquired by central cameras to images on the unit sphere, that is, spherical images. Using the transformations, it is possible to normalize all central-camera images to the spherical image. Therefore, spherical image analysis is a fundamental study for image analysis of central cameras. For geometrical analysis and reconstruction of a three-dimensional space from spherical images, great circles on a sphere are an essential feature since a great circle on a sphere corresponds to a line on a plane in a space. For great-circle detection, we formulate the randomized Hough transform on the basis of the geometric duality of a point and a great circle on a sphere. Finally, as an extension of the randomized Hough transform on a sphere, we propose a method for great-circle detection using a continuous spherical Hough space.
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paper_title: Circle recognition through a 2D Hough Transform and radius histogramming Abstract

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We present a two-step algorithm for the recognition of circles. The first step uses a 2D Hough Transform for the detection of the centres of the circles and the second step validates their existence by radius histogramming. The 2D Hough Transform technique makes use of the property that every chord of a circle passes through its centre. We present results of experiments with synthetic data demonstrating that our method is more robust to noise than standard gradient based methods. The promise of the method is demonstrated with its application on a natural image and on a digitized mammogram.
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paper_title: Size Invariant Circle Detection

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The Circle Hough Transform (CHT) has become a common method for circle detection in numerous image processing applications. Various modifications to the basic CHT operation have been suggested which include: the inclusion of edge orientation, simultaneous consideration of a range of circle radii, use of a complex accumulator array with the phase proportional to the log of radius, and the implementation of the CHT as filter operations. However, there has also been much work recently on the definition and use of invariance filters for object detection including circles. The contribution of the work presented here is to show that a specific combination of modifications to the CHT is formally equivalent to applying a scale invariant kernel operator. This work brings together these two themes in image processing which have herewith been quite separate. Performance results for applying various forms of CHT filters incorporating some or all of the available modifications, along with results from the invariance kernel, are included. These are in terms of an analysis of the peak width in the output detection array (with and without the presence of noise), and also an analysis of the peak position in terms of increasing noise levels. The results support the equivalence between the specific form of the CHT developed in this work and the invariance kernel.
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paper_title: Spherical parameter detection based on hierarchical Hough transform

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The paper is addressed to detect the parameters of a sphere-center coordinates and radius based on a stack of CT slices. It is proposing a new hierarchical Hough transform approach. In the first step, all slices are taken into consideration sequentially and a 2D accumulator array is used to obtain the coordinates (x"0,y"0), the projecting value of the sphere center into every X-Y-plane. In this step, also a new type of 2D Hough transform for circle or circular detection is proposed based on an effective point filtering. In the second step, the radii of the circles in the different slices are obtained using 1D accumulator arrays. In the last step, the coordinate z"0 and the radius R of the sphere are acquired using a 2D planar Hough transform based on the correlation between the radii of circles, the coordinates z of the slice and the sphere radius. The hierarchical Hough transform is applied to analyze the structure of femoral head of human hip joints. Compared to the established Hough transform techniques for 3D object detection, the hierarchical Hough transform reduces storage space and calculation time significantly and it has a good robustness to noise in the images.
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paper_title: A convolution approach to the circle Hough transform for arbitrary radius

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The Hough transform is a well-established family of algorithms for locating and describing geometric figures in an image. However, the computational complexity of the algorithm used to calculate the transform is high when used to target complex objects. As a result, the use of the Hough transform to find objects more complex than lines is uncommon in real-time applications. We describe a convolution method for calculating the Hough transform for finding circles of arbitrary radius. The algorithm operates by performing a three-dimensional convolution of the input image with an appropriate Hough kernel. The use of the fast Fourier transform to calculate the convolution results in a Hough transform algorithm with reduced computational complexity and thus increased speed. Edge detection and other convolution-based image processing operations can be incorporated as part of the transform, which removes the need to perform them with a separate pre-processing or post-processing step. As the Discrete Fourier Transform implements circular convolution rather than linear convolution, consideration must be given to padding the input image before forming the Hough transform.
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paper_title: Object Detection using Circular Hough Transform

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In this study we propose a new system to detect the object from an input image. The proposed system first uses the separability filter proposed by Fukui and Yamaguchi (Trans. IEICE Japan J80-D-II. 8, 2170-2177, 1997) to obtain the best object candidates and next, the system uses the Circular Hough Transform (CHT) to detect the presence of circular shape. The main contribution of this work consists of using together two different techniques in order to take advantages from the peculiarity of each of them. As the results of the experiments, the object detection rate of the proposed system was 96% for 25 images by moving the circle template every 20 pixels to right and down.
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paper_title: A FAST RANDOMIZED HOUGH TRANSFORM FOR CIRCLE/CIRCULAR ARC RECOGNITION

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The main drawbacks of the Hough transform (HT) are the heavy requirement of computation and storage. To improve the drawbacks of the HT, the randomized Hough transform (RHT) was proposed. But the RHT is not suitable for detecting the pattern with the complex image because the probability is too low. In this paper, we propose a fast randomized Hough transform for circle/circular arc detection. We pick one point at random to be the seed point. Then, we propose a checking rule to confirm if the seed point is on the true circle. Compared with the previous techniques, the proposed method requires less computational time and is more suitable for complex images. In the experiments, synthetic and real images are used to show the effect of the proposed method.
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paper_title: An Algebraic Approach to Hough Transforms

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Abstract The main purpose of this paper is to lay the foundations of a general theory which encompasses the features of the classical Hough transform and extend them to general algebraic objects such as affine schemes. The main motivation comes from problems of detection of special shapes in medical and astronomical images. The classical Hough transform has been used mainly to detect simple curves such as lines and circles. We generalize this notion using reduced Grobner bases of flat families of affine schemes. To this end we introduce and develop the theory of Hough regularity . The theory is highly effective and we give some examples computed with CoCoA (see [1] ).
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paper_title: Detection ellipses by finding lines of symmetry in the images via an hough transform applied to straight lines

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Abstract Through the use of a global geometric symmetry, detection ellipses are proposed in this paper. Based on the geometric symmetry, the proposed method first locates candidates of ellipses centers. In the meantime, according to these candidate centers, all feature points in an input image are grouped into several subimages. Then, for each subimage, by using geometric properties again, all ellipses are extracted. The method significantly reduces the time required to evaluate all possible parameters without using edge direction information. Experimental results are given to show the correctness and effectiveness of the proposed method.
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paper_title: Fast Ellipse Detection Algorithm Using Hough Transform on the GPU

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GPUs (Graphics Processing Units) are specialized microprocessors that accelerate 3D or 2D graphics operations. Recent GPUs, which have many processing units connected with a global memory, can be used for general purpose parallel computation. To utilize the powerful computing ability, GPUs are widely used for general purpose computing. The main purpose of this paper is an ellipse detection algorithm with Hough transform. The feature of our algorithm is that to reduce computational time and space, the parameter spaces in the Hough transform are decomposed for each parameter and each parameter is computed in series. Also, we implemented our algorithm on a modern GPU system. The experimental results show that, for an input image with size of 2040$\times$2040, our GPU implementation can achieve a speedup factor of approximately 64 times over the sequential implementation without the GPU support.
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paper_title: Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT)

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An iterative randomized Hough transform (IRHT) is developed for detection of incomplete ellipses in images with strong noise. The IRHT iteratively applies the randomized Hough transform (RHT) to a region of interest in the image space. The region of interest is determined from the latest estimation of ellipse parameters. The IRHT ''zooms in'' on the target curve by iterative parameter adjustments and reciprocating use of the image and parameter spaces. During the iteration process, noise pixels are gradually excluded from the region of interest, and the estimation becomes progressively close to the target. The IRHT retains the advantages of RHT of high parameter resolution, computational simplicity and small storage while overcoming the noise susceptibility of RHT. Indivisible, multiple instances of ellipse can be sequentially detected. The IRHT was first tested for ellipse detection with synthesized images. It was then applied to fetal head detection in medical ultrasound images. The results demonstrate that the IRHT is a robust and efficient ellipse detection method for real-world applications.
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paper_title: Randomized Hough Transform for Ellipse Detection with Result Clustering

paper_content:

Our research is focused on the development of robust machine vision algorithms for pattern recognition. We want to provide robotic systems the ability to understand more on the external real world. In this paper, we describe a method for detecting ellipses in real world images using the randomized Hough transform with result clustering. A preprocessing phase is used in which real world images are transformed - noise reduction, greyscale transform, edge detection and final binarization - in order to be processed by the actual ellipse detector. The ellipse detector filters out false ellipses that may interfere with the final results. Due to the fact that usually more "virtual" ellipses are detected for one "real" ellipse, a data clustering scheme is used, the clustering method, classifies all detected "virtual" ellipses into their corresponding "real" ellipses. The post processing phase is VQ similar and it also finds the actual number of classes unknown a priori
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paper_title: A new curve detection method: Randomized Hough transform (RHT)

paper_content:

Abstract A new method is proposed for curve detection. For a curve with n parameters, instead of transforming one pixel into a hypersurface of the n -D parameter space as the HT and its variants do, we randomly pick n pixels and map them into one point in the parameter space. In comparison with the HT and its variants, our new method has the advantages of small storage, high speed, infinite parameter space and arbitrarily high resolution. The preliminary experiments have shown that the new method is quite effective.
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paper_title: Genetically fine-tuning the Hough transform feature space, for the detection of circular objects

paper_content:

Despite certain inherent advantages of the Hough transform (HT), it may result in inaccurate estimates of the detected parameters, in the case of excessively noisy images. In this work, we present an original method for fine-tuning the feature space for the HT using genetic algorithms (GAs). The aim is to find a subset of features that best describe the instances of the sought shape, so that the HT accumulator is contaminated the least by noisy information. A hybrid GA/HT system is configured, by embedding the HT module into the GA, which simultaneously performs feature space fine-tuning and shape detection. Illustrative examples show that the system is capable of recovering instances with high accuracy from very noisy images where standard HT variations falter.
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paper_title: Generalized Hough Transform Using Regions with Homogeneous Color

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A novel generalized Hough transform algorithm which makes use of the color similarity between homogeneous segments as the voting criterion is proposed in this paper. The input of the algorithm is some regions with homogeneous color. These regions are obtained by first pre-segmenting the image using the morphological watershed algorithm and then refining the resultant outputs by a region merging algorithm. Region pairs belonging to the object are selected to generate entries of the reference table for the Hough transform. Every R-table entry stores a relative color between the selected region pairs. This is done in order to compute the color similarity and in turn generate votes during the voting process and some relevant information to recover the transformation parameters of the object. Based on the experimental results, our algorithm is robust to change of illumination, occlusion and distortion of the segmentation output. It recognizes objects which were translated, rotated, scaled and even located in a complex environment.
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paper_title: A New Generalized Hough Transform for the Detection of Irregular Objects

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Abstract In this paper, we introduce a new generalized Hough transform for the recognition of nonanalytic objects in a 2-D image. The main idea of our approach is to use pairs of boundary points with the same gradient angle to derive some rotation-invariant parameters to effect the fast Hough transform. Each voting to the Hough domain is contributed by a pair of edge pixels with the same gradient angle. The primary obstacle to using the Hough techniques, a large memory requirement, is overcome by our new voting approach. The conventional 4-D Hough domain is significantly reduced to a 2-D domain. This approach provides an easy method for determining the parameters of the object in question and an extremely effective solution for eliminating false votes in the transform.
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paper_title: ESPRIT-estimation of signal parameters via rotational invariance techniques

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An approach to the general problem of signal parameter estimation is described. The algorithm differs from its predecessor in that a total least-squares rather than a standard least-squares criterion is used. Although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise. It exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure. The technique, when applicable, manifests significant performance and computational advantages over previous algorithms such as MEM, Capon's MLM, and MUSIC. >
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paper_title: Gray-scale hough transform for thick line detection in gray-scale images

paper_content:

Abstract A new extension of the Hough transform, called gray-scale Hough transform (GSHT), is proposed for detecting thick lines (called bands) in a gray-scale image. The use of the conventional Hough transform (CHT) usually requires the preprocessing steps of thresholding and edge detection (or thinning) before the transform can be performed on a gray-scale image containing bands. This causes loss of useful gray and position relationship existing among the pixels of a band, and requires certain postprocessing steps to recover the band in the original image. The proposed GSHT with a gray-scale image as the direct input removes this shortcoming, requiring neither preprocessing nor postprocessing step in detecting bands in a gray-scale image. Experimental results, showing the feasibility of the proposed approach, are also included.
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paper_title: Spatial color histogram based center voting method for subsequent object tracking and segmentation

paper_content:

In this paper, we introduce an algorithm for object tracking in video sequences. In order to represent the object to be tracked, we propose a new spatial color histogram model which encodes both the color distribution and spatial information. Using this spatial color histogram model, a voting method based on the generalized Hough transform is employed to estimate the object location from frame to frame. The proposed voting based method, called the center voting method, requests every pixel near the previous object center to cast a vote for locating the new object center in the new frame. Once the location of the object is obtained, the back projection method is used to segment the object from the background. Experiment results show successful tracking of the object even when the object being tracked changes in size and shares similar color with the background.
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paper_title: Generalized Hough Transform for Shape Matching

paper_content:

In this paper we propose a novel approach towards shape matching for image retrieval. The system takes advantages of generalized Hough transform, as it works well in detecting arbitrary shapes even in the presence of gaps and in handling rotation, scaling and shift variations, and solves the heavy computational aspect by introducing a preliminary automatic selection of the appropriate contour points to consider in the matching phase. The numerical simulations and comparisons have confirmed the effectiveness and the efficiency of the method proposed.
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paper_title: Hough Transform for Region Extraction in Color Images

paper_content:

This article aims to propose a method to use the idea of Hough Transform (HT) implemented in grey scale images to color images for region extraction. A region in an image is seen as a union of pixels on several line segments having the homogeneity property. A line segment in an image is seen as a collection of pixels having the property of straight line in Euclidean plane and possessing the same property. The property ’homogeneity’ in a color image is based on the trace of the variance covariance matrix of the colors of the pixels on the straight line. As a possible application of the method, it is used to extract the homogeneous regions in the images taken from the Indian Remote Sensing Satellites.
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paper_title: A comparative study of efficient generalised Hough transform techniques

paper_content:

The generalised Hough transform (GHT) is useful for detecting or locating translated two-dimensional objects. However, a weakness of the GHT is its storage requirements and hence the increased computational complexity resulting from the four-dimensional parameter space. In this paper, we present the results of our work which involves investigation of the performance of several efficient GHT techniques including an extension of Thomas's rotation-invariant algorithm. It is shown that our extension of Thomas's algorithm has very low memory requirements and computational complexity, and produces the best results in various tests.
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paper_title: On Detection of Multiple Object Instances Using Hough Transforms

paper_content:

Hough transform-based methods for detecting multiple objects use nonmaxima suppression or mode seeking to locate and distinguish peaks in Hough images. Such postprocessing requires the tuning of many parameters and is often fragile, especially when objects are located spatially close to each other. In this paper, we develop a new probabilistic framework for object detection which is related to the Hough transform. It shares the simplicity and wide applicability of the Hough transform but, at the same time, bypasses the problem of multiple peak identification in Hough images and permits detection of multiple objects without invoking nonmaximum suppression heuristics. Our experiments demonstrate that this method results in a significant improvement in detection accuracy both for the classical task of straight line detection and for a more modern category-level (pedestrian) detection problem.
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paper_title: A new gray level based Hough transform for region extraction: An application to IRS images

paper_content:

A technique using the Hough transform is described for detection of homogeneous line segments directly from (i.e., without binarization of) gray level images. A definition of ''region'' in terms of these line segments, with constraints on its length and variance, is provided. The algorithm is able to extract gray level regions irrespective of their shape and size. The effectiveness of the method is demonstrated on Indian Remote-sensing Satellite (IRS) images.
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paper_title: On the gray-scale inverse Hough transform

paper_content:

This paper proposes a gray-scale inverse Hough transform (GIHT) algorithm which is combined with a modified gray-scale Hough transform (GHT). Given only the data of the Hough transform (HT) space and the dimensions of the image, the GIHT algorithm reconstructs correctly the original gray-scale image. As a first application, the GIHT is used for line detection and filtering according to conditions associated with the polar parameters, the size and the gray-scale values of the lines. The main advantage of the GIHT is the determination of the image lines exactly as they appear, i.e. pixel by pixel and with the correct gray-scale values. To avoid the quantization effects in the accumulator array of the GHT space, inversion conditions are defined which are associated only with the image size. The GIHT algorithm consists of two phases, which are the collection of gray-scale information stored in the accumulator array and the extraction of the final image according to the filtering conditions. Experimental results confirm the efficiency of the proposed method.q 2000 Elsevier Science B.V. All rights reserved.
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paper_title: Resource-Efficient FPGA Architecture and Implementation of Hough Transform

paper_content:

Hough transform is widely used for detecting straight lines in an image, but it involves huge computations. For embedded application, field-programmable gate arrays are one of the most used hardware accelerators to achieve real-time implementation of Hough transform. In this paper, we present a resource-efficient architecture and implementation of Hough transform on an FPGA. The incrementing property of Hough transform is described and used to reduce the resource requirement. In order to facilitate parallelism, we divide the image into blocks and apply the incrementing property to pixels within a block and between blocks. Moreover, the locality of Hough transform is analyzed to reduce the memory access. The proposed architecture is implement on an Altera EP2S180F1508C3 device and can operate at a maximum frequency of 200 MHz. It could compute the Hough transform of 512 × 512 test images with 180 orientations in 2.07-3.16 ms without using many FPGA resources (i.e., one could achieve the performance by adopting a low-cost low-end FPGA).
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paper_title: A Circular Hough Transform Hardware for Industrial Circle Detection Applications

paper_content:

The Hough transform (Hough, 1962) has been used to characterize analytic features. It was first applied to the recognition of straight lines, and later extended to circles, ellipses and arbitrary shaped objects. Its main disadvantage is the computational and storage requirements increase as a power of the dimensionality of the curve. It is not difficult to implement circular Hough transform (CHT) algorithm on modern personal computer. However, we intend to use FPGA or ASIC to perform CHT in this article. Modern FPGAs are capable of performing high speed operation and have large amount of embedded memory. The whole CHT circuitry with accumulator array excluded can be fitted into Alterareg Stratixreg 1S25 chip which has more than 1 Mb RAM embedded
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paper_title: Hierarchical Additive Hough Transform for Lane Detection

paper_content:

Detection of lanes is an important problem in the upcoming field of vehicle safety and navigation, for which linear Hough transform (HT) is widely used. In order to meet real-time requirements, various attempts to accelerate the HT have been proposed in the past, including hierarchical HT. Existing hierarchical approaches involve the overhead of recomputing HT values at every level in the hierarchy. In this letter, we propose a novel, computationally efficient hierarchical HT by extending and applying the additive property of HT to multiple levels of hierarchies. This proposed approach, called hierarchical additive Hough transform (HAHT) is shown to lead to significant computational savings of up to 98-99% in the Hough voting process. The HAHT has been validated on a wide range of straight lane images and it is shown to successfully detect lanes.
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paper_title: Parallelization Research of Circle Detection Based on Hough Transform

paper_content:

There is a problem of too long computation time in Circle detection of Hough transform. In this paper, two paralleled methods are given based on Threading Building Blocks (TBB) and CUDA, by utilizing multi-core and GPU, the most timeconsuming part of circle detection is coped with parallelization. Experimental results show that the circle detection algorithms proposed in this paper has extremely good result of acceleration.
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paper_title: A novel Hough transform based on eliminating particle swarm optimization and its applications

paper_content:

Hough transform (HT) is a well established method for curve detection and recognition due to its robustness and parallel processing capability. However, HT is quite time-consuming. In this paper, an eliminating particle swarm optimization (EPSO) algorithm is employed to improve the speed of a HT. The parameters of the solution after Hough transformation are considered as the particle positions, and the EPSO algorithm searches the optimum solution by eliminating the ''weakest'' particles to speed up the computation. An accumulation array in Hough transformation is utilized as a fitness function of the EPSO algorithm. The experiments on numerous images show that the proposed approach can detect curves or contours of both noise-free and noisy images with much better performance. Especially, for noisy images, it can archive much better results than that obtained by using the existing HT algorithms.
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paper_title: Crop-row detection algorithm based on Random Hough Transformation

paper_content:

It is important to detect crop rows accurately for field navigation. In order to spray on line, a variable rate spray system should detect the crop center line accurately. Most existing detection algorithms are slow to detect crop rows because of the complicated calculation. The gradient-based Random Hough Transform algorithm could improve the calculation speed and reduce the computation effectively by the more-to-one merger mapping method. In order to detect the center of the crop row rapidly and effectively, the detection algorithm with gradient-based Random Hough Transform was proposed to detect the center line of crop rows. We tested the center line of crop-row detection for three kinds of plant distribution, being sparse, general and intensive. The experimental results showed that the detection algorithm with gradient-based Random Hough Transform was adaptive to the difference of plant density in the crop row effectively. Contrasted with the detection algorithm based on the Hough transform, the detection algorithm based on the gradient-based Random Hough was faster and had a high detection correction rate.
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paper_title: Parallel GPU Implementation of Hough Transform for Circles

paper_content:

Hough transform is one of the most widely used algorithms in image processing. The major problems of Hough’s transform are its time consuming and its abundant requirement of computational resources. In this paper, we try to solve this problem by paralleling this algorithm and implementing it on GPUs(Graphic Process unit) using CUDA(Compute Unified Device Architecture) . We have introduced two methods for parallelization, each of which has been implemented on four different graphic cards using CUDA. After executing the proposed methods on GPUs, we have compared our results with sequential algorithm execution on CPU and it is observable that we have about 65 times more speedup toward the sequential algorithm.
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paper_title: Generalized Hough Transform for Shape Matching

paper_content:

In this paper we propose a novel approach towards shape matching for image retrieval. The system takes advantages of generalized Hough transform, as it works well in detecting arbitrary shapes even in the presence of gaps and in handling rotation, scaling and shift variations, and solves the heavy computational aspect by introducing a preliminary automatic selection of the appropriate contour points to consider in the matching phase. The numerical simulations and comparisons have confirmed the effectiveness and the efficiency of the method proposed.
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paper_title: Variations of a Hough-Voting Action Recognition System

paper_content:

This paper presents two variations of a Hough-voting framework used for action recognition and shows classification results for low-resolution video and videos depicting human interactions. For lowresolution videos, where people performing actions are around 30 pixels, we adopt low-level features such as gradients and optical flow. For group actions with human-human interactions, we take the probabilistic action labels from the Hough-voting framework for single individuals and combine them into group actions using decision profiles and classifier combination.
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paper_title: Combining Hough transform and contour algorithm for detecting vehicles' license-plates

paper_content:

Vehicle license plate (VLP) recognition is an interesting problem that has attracted many computer vision research groups. One of the most important and difficult tasks of this problem is VLP detecting. It is not only used in VLP recognition systems but also useful for many traffic management systems. Our method is used for a VLP recognition system that deals with Vietnamese VLPs and it can also be applied to other types of VLPs with minor changes. There are various approaches to this problem, such as texture-based, morphology-based and boundary line-based. In this paper, we present the boundary line-based method that optimizes speed and accuracy by combining the Hough transform and contour algorithm. The enhancement of applying the Hough transform to contour images is the much improved speed of the algorithm. In addition, the algorithm can be used on VLP images that have been taken from various distances and have inclined angles between /spl plusmn/30/spl deg/ from the camera. Especially, it can detect plates in images which have more than one VLP. The algorithm was evaluated in two image sets with an accuracy of about 99%.
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paper_title: Class-specific Hough forests for object detection

paper_content:

We present a method for the detection of instances of an object class, such as cars or pedestrians, in natural images. Similarly to some previous works, this is accomplished via generalized Hough transform, where the detections of individual object parts cast probabilistic votes for possible locations of the centroid of the whole object; the detection hypotheses then correspond to the maxima of the Hough image that accumulates the votes from all parts. However, whereas the previous methods detect object parts using generative codebooks of part appearances, we take a more discriminative approach to object part detection. Towards this end, we train a class-specific Hough forest, which is a random forest that directly maps the image patch appearance to the probabilistic vote about the possible location of the object centroid. We demonstrate that Hough forests improve the results of the Hough-transform object detection significantly and achieve state-of-the-art performance for several classes and datasets.
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paper_title: A comparative study of efficient generalised Hough transform techniques

paper_content:

The generalised Hough transform (GHT) is useful for detecting or locating translated two-dimensional objects. However, a weakness of the GHT is its storage requirements and hence the increased computational complexity resulting from the four-dimensional parameter space. In this paper, we present the results of our work which involves investigation of the performance of several efficient GHT techniques including an extension of Thomas's rotation-invariant algorithm. It is shown that our extension of Thomas's algorithm has very low memory requirements and computational complexity, and produces the best results in various tests.
---
paper_title: A Centroid based Hough Transformation for Indian license plate skew detection and correction of IR and color images

paper_content:

Skew Correction is a processing-stage between LP Localization and Character Segmentation in License Plate Recognition system used to identify a vehicle by its number plate. License plate is skewed in captured image due to the positioning of the vehicle with respect to the camera while capturing the LP image. The skewed license plate affects badly on the accurate character segmentation and recognition. After localization skew correction technique is applied in order to get correct character segmentation followed by character recognition. In this paper, a Centroid based Hough Transform technique is presented for skew correction of license plate which performs better than the other approaches of skew correction including Thresholding, Connected Component analysis, Hough Transform and Centroid method. The performance of the proposed algorithm has been tested on live captured LP images, yielding better performance in car license plate segmentation and hence the presented algorithm is good for all types of LP recognition applications due to its direct and simple approach with minimal computational time.
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paper_title: A Hough transform-based voting framework for action recognition

paper_content:

We present a method to classify and localize human actions in video using a Hough transform voting framework. Random trees are trained to learn a mapping between densely-sampled feature patches and their corresponding votes in a spatio-temporal-action Hough space. The leaves of the trees form a discriminative multi-class codebook that share features between the action classes and vote for action centers in a probabilistic manner. Using low-level features such as gradients and optical flow, we demonstrate that Hough-voting can achieve state-of-the-art performance on several datasets covering a wide range of action-recognition scenarios.
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paper_title: On Detection of Multiple Object Instances Using Hough Transforms

paper_content:

Hough transform-based methods for detecting multiple objects use nonmaxima suppression or mode seeking to locate and distinguish peaks in Hough images. Such postprocessing requires the tuning of many parameters and is often fragile, especially when objects are located spatially close to each other. In this paper, we develop a new probabilistic framework for object detection which is related to the Hough transform. It shares the simplicity and wide applicability of the Hough transform but, at the same time, bypasses the problem of multiple peak identification in Hough images and permits detection of multiple objects without invoking nonmaximum suppression heuristics. Our experiments demonstrate that this method results in a significant improvement in detection accuracy both for the classical task of straight line detection and for a more modern category-level (pedestrian) detection problem.
---
paper_title: A robust lane detection method for autonomous car-like robot

paper_content:

Due to illumination variation, view changes, and complex road conditions, automatic lane detection is crucial for path finding and planning for autonomous car-like robots. In this paper, a robust lane detection method is proposed. Firstly, in order to extract the edges of lanes in traffic scenarios, we adopt an adaptive thresholding strategy to binarize a gradient image and trace edges by using their local gradient information. Moreover, by integrating gradient constraints and introducing a reverse voting strategy to the standard Hough transform, we greatly improved speed and stability of line extraction. Further, through inverse perspective mapping the endpoints of extracted lines to the world coordination, we can combine the extracted lines from different cameras. Finally, the lane could be detected by matching two points instead of two parallel lines in parameter space. Extensive experiments and comparisons show the efficiency of the proposed method.
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paper_title: Automatic lumbar vertebrae segmentation in fluoroscopic images via optimised concurrent Hough transform

paper_content:

We show how a new approach can automatically detect the positions and borders of vertebrae concurrently, relieving many of the problems experienced in other approaches. First, we use phase congruency to relieve the difficulty associated with threshold selection in edge detection of the illumination variant DVF images. Then, our new Hough transform approach is applied to determine the moving vertebrae, concurrently. We include optimisation via a genetic algorithm (as without it the extraction of moving multiple vertebrae is computationally daunting). Our results show that this new approach can indeed provide extractions of position and rotation which appear to be of sufficient quality to aid therapy and diagnosis of spinal disorders.
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paper_title: Progressive probabilistic Hough transform for line detection

paper_content:

We present a novel Hough Transform algorithm referred to as Progressive Probabilistic Hough Transform (PPHT). Unlike the Probabilistic HT where Standard HT is performed on a pre-selected fraction of input points, PPHT minimises the amount of computation needed to detect lines by exploiting the difference an the fraction of votes needed to detect reliably lines with different numbers of supporting points. The fraction of points used for voting need not be specified ad hoc or using a priori knowledge, as in the probabilistic HT; it is a function of the inherent complexity of the input data. The algorithm is ideally suited for real-time applications with a fixed amount of available processing time, since voting and line detection is interleaved. The most salient features are likely to be detected first. Experiments show that in many circumstances PPHT has advantages over the Standard HT.
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paper_title: Parallel Hough Transform-Based Straight Line Detection and Its FPGA Implementation in Embedded Vision

paper_content:

Hough Transform has been widely used for straight line detection in low-definition and still images, but it suffers from execution time and resource requirements. Field Programmable Gate Arrays (FPGA) provide a competitive alternative for hardware acceleration to reap tremendous computing performance. In this paper, we propose a novel parallel Hough Transform (PHT) and FPGA architecture-associated framework for real-time straight line detection in high-definition videos. A resource-optimized Canny edge detection method with enhanced non-maximum suppression conditions is presented to suppress most possible false edges and obtain more accurate candidate edge pixels for subsequent accelerated computation. Then, a novel PHT algorithm exploiting spatial angle-level parallelism is proposed to upgrade computational accuracy by improving the minimum computational step. Moreover, the FPGA based multi-level pipelined PHT architecture optimized by spatial parallelism ensures real-time computation for 1,024 × 768 resolution videos without any off-chip memory consumption. This framework is evaluated on ALTERA DE2-115 FPGA evaluation platform at a maximum frequency of 200 MHz, and it can calculate straight line parameters in 15.59 ms on the average for one frame. Qualitative and quantitative evaluation results have validated the system performance regarding data throughput, memory bandwidth, resource, speed and robustness.
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paper_title: A novel Hough transform method for line detection by enhancing accumulator array

paper_content:

In this paper, an improved Hough transform (HT) method is proposed to robustly detect line segments in images with complicated backgrounds. The work focuses on detecting line segments of distinct lengths, totally independent of prior knowledge of the original image. Based on the characteristics of accumulation distribution obtained by conventional HT, a local operator is implemented to enhance the difference between the accumulation peaks caused by line segments and noise. Through analysis of the effect of the operator, a global threshold is obtained in the histogram of the enhanced accumulator to detect peaks. Experimental results are provided to demonstrate the efficiency and robustness of the proposed method.
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paper_title: Hierarchical Additive Hough Transform for Lane Detection

paper_content:

Detection of lanes is an important problem in the upcoming field of vehicle safety and navigation, for which linear Hough transform (HT) is widely used. In order to meet real-time requirements, various attempts to accelerate the HT have been proposed in the past, including hierarchical HT. Existing hierarchical approaches involve the overhead of recomputing HT values at every level in the hierarchy. In this letter, we propose a novel, computationally efficient hierarchical HT by extending and applying the additive property of HT to multiple levels of hierarchies. This proposed approach, called hierarchical additive Hough transform (HAHT) is shown to lead to significant computational savings of up to 98-99% in the Hough voting process. The HAHT has been validated on a wide range of straight lane images and it is shown to successfully detect lanes.
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paper_title: Lanes Detection Based on Unsupervised and Adaptive Classifier

paper_content:

This paper describes an algorithm to detect the road lanes based on an unsupervised and adaptive classifier. We have selected this classifier because in the road we do not know the parameters of lanes, although we know that lanes are there, only they need to be classified. First of all, we tested and measured the brightness of the lanes of the road in many videos. Generally, the lines on the road are white. We used the HSV image and we improved the region of study. Then, we used a Hough transform which yields a set of possible lines. These lines have to be classified. The classifier starts with initial parameters because we suppose that the vehicle is on road and in the center of the lane. There are two classes, the first one is the left road line and the second one is the right road line. Each line has two parameters that are: middle point of line and the line slope. These parameters will be changing in order to adjust to the real lanes. A tensor holds the two lines, so these lines will not separate more than the tensor allows. A Kalman filter estimates the new class's parameters and improves the tracking of the lanes. Finally, we use a mask in order to highlight the lane and show to the user a better image.
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paper_title: Circular road sign detection and recognition based on hough transform

paper_content:

In this paper, a new algorithm is proposed to detect and recognize common road signs. Firstly, color segmentation and connected domain analysis are used for region of interest (ROI) detection and then we used the improved two-time Hough transform to separately determinate the circle center and radium of the road sign. Then the Weighted Hausdorff Distance is applied in the recognition of extracted ROI. Compared to the conventional methods, the proposed algorithm has the advantage of small storage, lower time complexity and good robustness towards inclination and occlusion.
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paper_title: Lane Departure Detection and Transmisson using Hough Transform Method

paper_content:

Vehicles need to be re-advancing by video transmission among vehicles for safety and cooperative deriving. The video images captured from camera could help the driver to monitor the surroundings as well as transmit the compressed images over vehicular communication network. Video over wireless communication has a lot of potential applications in intelligent transportation systems (ITS).Video streaming utilizes high bandwidth data links to transmit information. The high-bandwidth systems required larger equipment, better line-of-sight, and more complex mechanism for reliable transmit ion over the network. The intended platform for the system described in this study, is to develop a software defined algorithm for automatic video compression and transmission. The proposed algorithm is able to robustly find the left and right boundary of the lane using Hough Transform method and transmit over the network. Therefore the limitations of high-bandwidth equipment become more significant in a tactical scenario. The results show that the proposed method works well on marked roads and transmission in various lighting conditions. Streszczenie. W artykule przedstawiono algorytm kompresji i transmisji obrazow do zastosowania w komunikacji bezprzewodowej w sieci samochodowej. Przy pomocy transformacji Hough algorytm określa lewą i prawą granice pasa i przesyla dane do sieci. Przeprowadzono badania weryfikujące dzialanie algorytmu, ktore wykazaly jego skutecznośc na oznaczonych drogach przy roznych warunkach oświetlenia. (Zastosowanie transformacji Hough w detekcji wyjazdu z pasa i transmisji danych).
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paper_title: A gridding Hough transform for detecting the straight lines in sports video

paper_content:

A gridding Hough transform (GHT) is proposed to detect the straight lines in sports video, which is much faster and requires much less memory than the previous Hough transforms. The GHT uses the active gridding to replace the random point selection in the random Hough transforms because forming the linelets from the actively selected points is easier than from the randomly selected points. Existing straight-line Hough transforms require a lot of resources because they were designed for all kinds of straight lines. Considering the fact that the straight lines interested in sports video are long and sparse, this paper proposes two techniques: the active gridding and linelets process. On account of these two techniques, the proposed GHT is fast and uses little memory. The experimental results show that the proposed GHT is faster than the random Hough transform (RHT) and the standard Hough transform (SHT) by 30% and 700% respectively and achieves a 97.5% recall, higher than those achieved by either the SHT or the RHT.
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paper_title: Driver assistance system for lane detection and vehicle recognition with night vision

paper_content:

The objective of this research is to develop a vision-based driver assistance system to enhance the driver's safety in the nighttime. The proposed system performs both lane detection and vehicle recognition. In lane detection, three features including lane markers, brightness, slenderness and proximity are applied to detect the positions of lane markers in the image. On the other hand, vehicle recognition is achieved by using an evident feature which is extracted through three four steps: taillight standing-out process, adaptive thresholding, centroid detection, and taillight pairing algorithm. Besides, an automatic method is also provided to calculate the tilt and the pan of the camera by using the position of vanishing point which is detected in the image by applying Canny edge detection, Hough transform, major straight line extraction and vanishing point estimation. Experimental results for thousands of images are provided to demonstrate the effectiveness of the proposed approach in the nighttime. The lane detection rate is nearly 99%, and the vehicle recognition rate is about 91%. Furthermore, our system can process the image in almost real time.
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paper_title: Combining Hough transform and contour algorithm for detecting vehicles' license-plates

paper_content:

Vehicle license plate (VLP) recognition is an interesting problem that has attracted many computer vision research groups. One of the most important and difficult tasks of this problem is VLP detecting. It is not only used in VLP recognition systems but also useful for many traffic management systems. Our method is used for a VLP recognition system that deals with Vietnamese VLPs and it can also be applied to other types of VLPs with minor changes. There are various approaches to this problem, such as texture-based, morphology-based and boundary line-based. In this paper, we present the boundary line-based method that optimizes speed and accuracy by combining the Hough transform and contour algorithm. The enhancement of applying the Hough transform to contour images is the much improved speed of the algorithm. In addition, the algorithm can be used on VLP images that have been taken from various distances and have inclined angles between /spl plusmn/30/spl deg/ from the camera. Especially, it can detect plates in images which have more than one VLP. The algorithm was evaluated in two image sets with an accuracy of about 99%.
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paper_title: Implementation of lane detection system using optimized hough transform circuit

paper_content:

This paper describes a vision-based lane detection system with the optimized Hough Transform circuit. The Hough Transform is a popular method to find the line features in an image. This is very robust to noises and changes in the illumination level, but it requires long computation time and large data storage for calculation. It needs large logic gates for implementation. It is difficult to apply in products that require real-time performance. In this paper, we propose the optimized Hough Transform circuit architecture and a lane departure warning system using vision device. We suggest the Hough Transform architecture to minimize the size of logic and the number of cycle time. Our implemented Hough Transform circuit show the good performance than other circuit architecture. We tested the Hough Transform circuit and lane departure warning system on the Xilinx FPGA board.
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paper_title: Robust lane localization using multiple cues on the road

paper_content:

This paper presents a method for estimating the current lane number in which the vehicle is traveling. An important component of visual automobile safety systems rely on knowledge of the lane number to know how far the vehicle is from the exit lane. The method uses taking the Inverse Perspective Map of an image to get a top view of the road and then detects multiple lanes using Hough transform line detection. Then the algorithm classifies the lines as solid or broken and also detects if the lane is branching or split using connected component analysis. This knowledge of the line type and whether the lane is splitting is the basis to identify if the vehicle is in the exit lane.
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paper_title: A robust lane detection method in the different scenarios

paper_content:

Road recognition is critical for safe driving and intelligent vehicles. In order to improve the real-time and stability of lane recognition, a robust road recognition algorithm based on improved Canny and Progressive Probabilistic Hough Transform (PPHT) is proposed in this paper, Experiments prove accuracy of the lane recognition and high real-time capability in the different scenarios.
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paper_title: A Centroid based Hough Transformation for Indian license plate skew detection and correction of IR and color images

paper_content:

Skew Correction is a processing-stage between LP Localization and Character Segmentation in License Plate Recognition system used to identify a vehicle by its number plate. License plate is skewed in captured image due to the positioning of the vehicle with respect to the camera while capturing the LP image. The skewed license plate affects badly on the accurate character segmentation and recognition. After localization skew correction technique is applied in order to get correct character segmentation followed by character recognition. In this paper, a Centroid based Hough Transform technique is presented for skew correction of license plate which performs better than the other approaches of skew correction including Thresholding, Connected Component analysis, Hough Transform and Centroid method. The performance of the proposed algorithm has been tested on live captured LP images, yielding better performance in car license plate segmentation and hence the presented algorithm is good for all types of LP recognition applications due to its direct and simple approach with minimal computational time.
---
paper_title: A robust lane detection method for autonomous car-like robot

paper_content:

Due to illumination variation, view changes, and complex road conditions, automatic lane detection is crucial for path finding and planning for autonomous car-like robots. In this paper, a robust lane detection method is proposed. Firstly, in order to extract the edges of lanes in traffic scenarios, we adopt an adaptive thresholding strategy to binarize a gradient image and trace edges by using their local gradient information. Moreover, by integrating gradient constraints and introducing a reverse voting strategy to the standard Hough transform, we greatly improved speed and stability of line extraction. Further, through inverse perspective mapping the endpoints of extracted lines to the world coordination, we can combine the extracted lines from different cameras. Finally, the lane could be detected by matching two points instead of two parallel lines in parameter space. Extensive experiments and comparisons show the efficiency of the proposed method.
---
paper_title: Road extraction from high-resolution remote sensing images using wavelet transform and hough transform

paper_content:

Road extraction from high-resolution remote sensing images has been a challenging problem in ground object detection field. In this paper, an effective and anti-noise road extraction method by using wavelet transform and Hough transform is proposed. Firstly, wavelet transform is used for image denoising and detecting edges of roads. Then the image is segmented into road area and non-road area. Hough transform is used to extract roads after segmentation. Finally, the effectiveness of the method is testified by the results of the experiment.
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paper_title: Real-Time Articulated Hand Detection and Pose Estimation

paper_content:

We propose a novel method for planar hand detection from a single uncalibrated image, with the purpose of estimating the articulated pose of a generic model, roughly adapted to the current hand shape. The proposed method combines line and point correspondences, associated to finger tips, lines and concavities, extracted from color and intensity edges. The method robustly solves for ambiguous association issues, and refines the pose estimation through nonlinear optimization. The result can be used in order to initialize a contour-based tracking algorithm, as well as a model adaptation procedure.
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paper_title: Hough Transform and Active Contour for Enhanced Iris Segmentation

paper_content:

Iris segmentation is considered as the most difficult and fundamental step in an iris recognition system. While iris boundaries are largely approximated by two circles or ellipses, other methods define more accurately the iris resulting in better recognition results. In this paper we propose an iris segmentation method using Hough transform and active contour to detect a circular approximation of the outer iris boundary and to accurately segment the inner boundary in its real shape motivated by the fact that richer iris textures are closer to the pupil than to the sclera. Normalization, encoding and matching are implemented according to Daugman‟s method. The method, tested on CASIA-V3 iris images database is compared to Daugman‟s iris recognition system. Recognition performance is measured in terms of decidability, accuracy at the equal error rate and ROC curves. Improved recognition performance is obtained using our segmentation model proposing its use for better iris
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paper_title: DETECTING PERSONS USING HOUGH CIRCLE TRANSFORM IN SURVEILLANCE VIDEO

paper_content:

Robust person detection in real-world images is interesting and important for a variety of applications, such as visual surveillance. We address the task of detecting persons in elevator surveillance scenes in this paper. To get more passengers in the lift car, the camera usually installed at the corner of ceiling. However, the high and space of lift car are limited, which makes person occluded by each other or some parts of body invisible in captured images. In this paper, we propose a novel approach to detect head contours, which includes three main steps: pre-processing, head contour detection and post-processing. Hough circle transform is adopted in the second stage, which is robust to discontinuous boundaries in circle detection. Proposed pre-processing and post-processing methods are efficient to remove false alarms on background or body part. Experimental results show our proposed approach is time saving and has better person detection results than some other methods.
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paper_title: Fast and Accurate Pupil Positioning Algorithm using Circular Hough Transform and Gray Projection

paper_content:

A fast pupil-positioning algorithm for real-time eye tracking is proposed in this paper. It is significant accurately locate the pupil positi2on in an eye tracking system. Commonly used method is combining edge detection algorithms and ellipse fitting. Edge detection algorithms are used to detect edges of the pupil while the ellipse fitting is supposed to find the optimum ellipse that finely fits the pupil and the centre of the ellipse is regarded as the location of the pupil. This algorithm is acceptable except that the definition of the pupil edge has a great influence on its efficiency and ellipse fitting is a time consuming method. This paper focuses on accuracy of the primary algorithm and some improvement on it and uses circular Hough transform to detect pupil area. Firstly, mainly localize the pupil position by gray projection. Secondly, fit a circle to pupil using circular Hough transform.
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paper_title: Eyes detection in facial images using Circular Hough Transform

paper_content:

This paper presents an eye detection approach using Circular Hough transform. Assuming the face region has already been detected by any of the accurate existing face detection methods, the search of eye pair relies primarily on the circular shape of the eye in two-dimensional image. The eyes detection process includes preprocessing that filtered and cropped the face images and Circular Hough Transform is used to detect the circular shape of the eye and to mark the eye pair on the image precisely. This eyes detection method was tested on Face DB database developed by Park Lab, University of Illinois at Urbana Champaign USA. Most of the faces are frontal with open eyes and some are tilted upwards or downwards. The detection accuracy of the proposed method is about 86%.
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paper_title: Reliable iris localization using Hough transform, histogram-bisection, and eccentricity

paper_content:

The iris technology recognizes individuals from their iris texture with great precision. However, it does not perform well for the non-ideal data, where the eye image may contain non-ideal issues such as the off-axis eye image, blurring, non-uniform illumination, hair, glasses, etc. It is because of their iris localization algorithms, which are developed for the ideal data. In this paper, we propose a reliable iris localization algorithm. It includes localizing a coarse iris location in the eye image using the Hough transform and image statistics; localizing the pupillary boundary using a bi-valued adaptive threshold and the two-dimensional (2D) shape properties; localizing the limbic boundary by reusing the Hough accumulator and image statistics; and finally, regularizing these boundaries using a technique based on the Fourier series and radial gradients. The proposed technique is tested on the public iris databases: CASIA V1, CASIA-IrisV3-Lamp, CASIA-IrisV4-Thousand, IITD V1.0, MMU V1.0, and MMU (new) V2.0. Experimental results obtained on these databases show superiority of the proposed technique over some state of the art iris localization techniques.
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paper_title: Turkish fingerspelling recognition system using Generalized Hough Transform, interest regions, and local descriptors

paper_content:

This paper presents a computer vision system that can recognize Turkish fingerspelling sign hand postures by a method based on the Generalized Hough Transform, interest regions, and local descriptors. A novel method for calculating the reference point for the Generalized Hough Transform, and a simpler but more effective Hough voting strategy are proposed. The stages of implementing a Generalized Hough Transform are examined in detail, and the issues that affect the method success are discussed. The system is tested on a data set with 29 classes of non-rigid hand postures signed by three different signers on non-uniform backgrounds. It attains a 0.93 success rate.
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paper_title: Latent Fingerprint Matching Using Descriptor-Based Hough Transform

paper_content:

Identifying suspects based on impressions of fingers lifted from crime scenes (latent prints) is a routine procedure that is extremely important to forensics and law enforcement agencies. Latents are partial fingerprints that are usually smudgy, with small area and containing large distortion. Due to these characteristics, latents have a significantly smaller number of minutiae points compared to full (rolled or plain) fingerprints. The small number of minutiae and the noise characteristic of latents make it extremely difficult to automatically match latents to their mated full prints that are stored in law enforcement databases. Although a number of algorithms for matching full-to-full fingerprints have been published in the literature, they do not perform well on the latent-to-full matching problem. Further, they often rely on features that are not easy to extract from poor quality latents. In this paper, we propose a new fingerprint matching algorithm which is especially designed for matching latents. The proposed algorithm uses a robust alignment algorithm (descriptor-based Hough transform) to align fingerprints and measures similarity between fingerprints by considering both minutiae and orientation field information. To be consistent with the common practice in latent matching (i.e., only minutiae are marked by latent examiners), the orientation field is reconstructed from minutiae. Since the proposed algorithm relies only on manually marked minutiae, it can be easily used in law enforcement applications. Experimental results on two different latent databases (NIST SD27 and WVU latent databases) show that the proposed algorithm outperforms two well optimized commercial fingerprint matchers. Further, a fusion of the proposed algorithm and commercial fingerprint matchers leads to improved matching accuracy.
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paper_title: Vanishing point detection in corridors: using hough transform and K-means clustering

paper_content:

One of the main challenges in steering a vehicle or a robot is the detection of appropriate heading. Many solutions have been proposed during the past few decades to overcome the difficulties of intelligent navigation platforms. In this study, the authors try to introduce a new procedure for finding the vanishing point based on the visual information and K-Means clustering. Unlike other solutions the authors do not need to find the intersection of lines to extract the vanishing point. This has reduced the complexity and the processing time of our algorithm to a large extent. The authors have imported the minimum possible information to the Hough space by using only two pixels (the points) of each line (start point and end point) instead of hundreds of pixels that form a line. This has reduced the mathematical complexity of our algorithm while maintaining very efficient functioning. The most important and unique characteristic of our algorithm is the usage of processed data for other important tasks in navigation such as mapping and localisation.
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paper_title: A New Vanishing Point Detection Algorithm Based on Hough Transform

paper_content:

Detecting vanishing points in digital image is important for 3D reconstruction of a scene using no calibrated cameras. A new vanishing point detection algorithm based on twice Hough Transform was presented in this paper. Hough Transform has been recognized as one more popular method for the detection of straight lines. Firstly, the straight lines that in the image are detected by first Hough Transform. Secondly, using a coordinate transform, points in a circle were translated to points in a line. Finally, for detection of the line, Hough Transform was used again. Thus, the positions of vanishing points can be calculated by corresponding to the line parameters in pole coordinate. Experimental results using real images of a scene demonstrate that the proposed algorithm is effective and can acquire the vanishing point.
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paper_title: Vanishing points in point-to-line mappings and other line parameterizations

paper_content:

Some variants of the Hough transform can be used for detecting vanishing points and groups of concurrent lines. This article addresses a common misconception that in the polar line parameterization the vanishing point is represented by a line. The numerical error caused by this inaccuracy is then estimated. The article studies in detail point-to-line-mappings (PTLMs) - a class of line parameterizations which have the property that the vanishing point is represented by a line (and thus can be easily searched for). When a PTLM parameterization is used for the straight line detection by the Hough transform, a pair or a triplet of complementary PTLMs has to be used in order to obtain a limited Hough space. The complementary pairs and triplets of PTLMs are formalized and discussed in this article.
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paper_title: Hough-transform and extended RANSAC algorithms for automatic detection of 3D building roof planes from Lidar data

paper_content:

Airborne laser scanner technique is broadly the most appropriate way to acquire rapidly and with high density 3D data over a city. Once the 3D Lidar data are available, the next task is the automatic data processing, with major aim to construct 3D building models. Among the numerous automatic reconstruction methods, the techniques allowing the detection of 3D building roof planes are of crucial importance. Three main methods arise from the literature: region growing, Hough-transform and Random Sample Consensus (RANSAC) paradigm. Since region growing algorithms are sometimes not very transparent and not homogenously applied, this paper focuses only on the Hough-transform and the RANSAC algorithm. Their principles, their pseudocode - rarely detailed in the related literature - as well as their complete analyses are presented in this paper. An analytic comparison of both algorithms, in terms of processing time and sensitivity to cloud characteristics, shows that despite the limitation encountered in both methods, RANSAC algorithm is still more efficient than the first one. Under other advantages, its processing time is negligible even when the input data size is very large. On the other hand, Hough-transform is very sensitive to the segmentation parameters values. Therefore, RANSAC algorithm has been chosen and extended to exceed its limitations. Its major limitation is that it searches to detect the best mathematical plane among 3D building point cloud even if this plane does not always represent a roof plane. So the proposed extension allows harmonizing the mathematical aspect of the algorithm with the geometry of a roof. At last, it is shown that the extended approach provides very satisfying results, even in the case of very weak point density and for different levels of building complexity. Therefore, once the roof planes are successfully detected, the automatic building modelling can be carried out.
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paper_title: 3D Implicit Shape Models Using Ray Based Hough Voting for Furniture Recognition

paper_content:

The recognition of object categories in 3D scenes is still a challenging problem in computer vision. Many state of the art approaches use Implicit Shape Models, as addressed in [8] and [14], to learn shapes of object categories and a probabilistic Hough Space Voting for the detection of instances of the learned category. In this paper we present a novel 3D Hough Space Voting approach for recognizing object categories, learned from artificial 3D models, in 3D scenes. The proposed method uses rays instead of points to vote for object reference points. The usage of ray voting allows a clustering of votes, showing in similar directions, to a single vote with an appropriate vote weight. The main advantage for the Implicit Shape Model is that it can be trained with an unlimited amount of training data, while keeping the upper bound of computation effort constant. In addition, it is also able to abstract from the model sizes which is very helpful when training with artificial models taken from different sources and modelled in different scales. We validate our approach in two tasks: an object categorization is performed on a standard 3D dataset of artificial models and a recognition of furniture categories is evaluated on a dataset of captured indoor room scenes.
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paper_title: The Cascaded Hough Transform as Support for Grouping and Finding Vanishing Points and Lines

paper_content:

In the companion paper [7] a grouping strategy with a firm geometrical underpinning and without the problem of combinatorias is proposed. It is based on the exploitation of structures that remain fixed under the transformations that relate corresponding contour segments in regular patterns. In this paper we present a solution for the complementary task of extracting these fixed structures in an efficient and non-combinatorial way, based on the iterated application of the Hough transform. Apart from grouping, this ‘Cascaded Hough Transform’ or CHT for short can also be used for the detection of straight lines, vanishing points and vanishing lines.
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paper_title: Hough-transform detection of lines in 3-D space

paper_content:

Abstract Detecting straight lines in 3-D space using a Hough transform approach involves a 4-D parameter space, which is cumbersome. In this paper, we show how to detect families of parallel lines in 3-D space at a moderate computational cost by using a (2+2)-D Hough space. We first find peaks in the 2-D slope parameter space; for each of these peaks, we then find peaks in the intercept parameter space. Our experimental results on range images of boxes and blocks indicate that the method works quite well.
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paper_title: Generalized Hough Transform Using Regions with Homogeneous Color

paper_content:

A novel generalized Hough transform algorithm which makes use of the color similarity between homogeneous segments as the voting criterion is proposed in this paper. The input of the algorithm is some regions with homogeneous color. These regions are obtained by first pre-segmenting the image using the morphological watershed algorithm and then refining the resultant outputs by a region merging algorithm. Region pairs belonging to the object are selected to generate entries of the reference table for the Hough transform. Every R-table entry stores a relative color between the selected region pairs. This is done in order to compute the color similarity and in turn generate votes during the voting process and some relevant information to recover the transformation parameters of the object. Based on the experimental results, our algorithm is robust to change of illumination, occlusion and distortion of the segmentation output. It recognizes objects which were translated, rotated, scaled and even located in a complex environment.
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paper_title: Distinctive Image Features from Scale-Invariant Keypoints

paper_content:

The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.
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paper_title: Generalized Hough Transform for Shape Matching

paper_content:

In this paper we propose a novel approach towards shape matching for image retrieval. The system takes advantages of generalized Hough transform, as it works well in detecting arbitrary shapes even in the presence of gaps and in handling rotation, scaling and shift variations, and solves the heavy computational aspect by introducing a preliminary automatic selection of the appropriate contour points to consider in the matching phase. The numerical simulations and comparisons have confirmed the effectiveness and the efficiency of the method proposed.
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paper_title: Unsupervised clustering in Hough space for recognition of multiple instances of the same object in a cluttered scene

paper_content:

We describe an active binocular vision system that is capable of localising multiple instances of objects of the same-class in different settings within a covert, pre-attentive, visual search strategy. By clustering SIFT-feature matches that have been projected into a non-quantised (i.e. continuous) Hough space we are able to detect up to 6 same-class object instances simultaneously while tolerating up to ~66% of each object's surface being occluded by another object instance of the same-class. Our findings are based on using a database of ~2300 images of synthetically composited and real-world images.
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paper_title: Class-specific Hough forests for object detection

paper_content:

We present a method for the detection of instances of an object class, such as cars or pedestrians, in natural images. Similarly to some previous works, this is accomplished via generalized Hough transform, where the detections of individual object parts cast probabilistic votes for possible locations of the centroid of the whole object; the detection hypotheses then correspond to the maxima of the Hough image that accumulates the votes from all parts. However, whereas the previous methods detect object parts using generative codebooks of part appearances, we take a more discriminative approach to object part detection. Towards this end, we train a class-specific Hough forest, which is a random forest that directly maps the image patch appearance to the probabilistic vote about the possible location of the object centroid. We demonstrate that Hough forests improve the results of the Hough-transform object detection significantly and achieve state-of-the-art performance for several classes and datasets.
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paper_title: Recognition of multibreak patterns by 8-neighborhood-based General Hough Transform

paper_content:

In this paper, an 8-neighborhood-based Generalized Hough Transform (ENGHT) for the recognition of multibreak patterns is proposed, which is different from the Generalized Hough Transform (GHT). The difference between ENGHT and GHT appears in the process for Reference-table (R-table) configuration and for pattern recognition. The theoretic and experimental results show that ENGHT can be employed in the recognition of multibreak patterns with a high precision and a high recognition speed, and reduce the difficulty during the recognition that arose from the broken patterns to be processed by the conventional GHT. ENGHT can be employed in pattern recognition, especially in the multibreak pattern recognition.
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paper_title: On Detection of Multiple Object Instances Using Hough Transforms

paper_content:

Hough transform-based methods for detecting multiple objects use nonmaxima suppression or mode seeking to locate and distinguish peaks in Hough images. Such postprocessing requires the tuning of many parameters and is often fragile, especially when objects are located spatially close to each other. In this paper, we develop a new probabilistic framework for object detection which is related to the Hough transform. It shares the simplicity and wide applicability of the Hough transform but, at the same time, bypasses the problem of multiple peak identification in Hough images and permits detection of multiple objects without invoking nonmaximum suppression heuristics. Our experiments demonstrate that this method results in a significant improvement in detection accuracy both for the classical task of straight line detection and for a more modern category-level (pedestrian) detection problem.
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paper_title: Unsupervised moving object detection with on-line generalized hough transform

paper_content:

Generalized Hough Transform-based methods have been successfully applied to object detection. Such methods have the following disadvantages: (i) manual labeling of training data ; (ii) the off-line construction of codebook. To overcome these limitations, we propose an unsupervised moving object detection algorithm with on-line Generalized Hough Transform. Our contributions are two-fold: (i) an unsupervised training data selection algorithm based on Multiple Instance Learning (MIL); (ii) an on-line Extremely Randomized Trees construction algorithm for on-line codebook adaptation. We evaluate the proposed algorithm on three video datasets. The experimental results show that the proposed algorithm achieves comparable performance to the supervised detection method with manual labeling. They also show that the proposed algorithm outperforms the previously proposed unsupervised learning algorithm.
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paper_title: 2D into 3D Hough-space mapping for planar object pose estimation

paper_content:

A novel approach is proposed that relates the classical two-dimensional Hough space to a different Hough space embedding 3D information about the poses of planar objects in a single gray-level image. The Hough transform is used to detect rectilinear segments that, suitably grouped into a bounded figure, constitute a planar surface. Then, a pure geometrical mechanism is used to map the numerical Hough space representation of the image into a similar representation in a reference system that is fixed with respect to the surface. The object pose to be estimated is computed by comparing the numerical representations of the test and model images (usually in a fronto-parallel view) in the same space invariant to the object pose.
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paper_title: Rectangular object tracking based on standard hough transform

paper_content:

Object tracking in computer vision is important. Numerous research papers have been published about this problem. Few references relating to tracking shaped objects via the Hough Transforms exist. This paper provides a method based on the Standard Hough Transform to track rectangular objects. Our method works on edge images obtained by applying the Canny edge detector to the source image. The rectangular object in the edge image is then extracted based on the Standard Hough Transform and related conditions of the rectangle. Afterwards, we shift the region of interest to the new position of the tracked object and continue to detect the rectangle in successive frames. Our technique can be run in real-time due to its low computation time and simplicity.
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paper_title: Efficient tracking with the Bounded Hough Transform

paper_content:

The Bounded Hough Transform is introduced to track objects in a sequence of sparse range images. The method is based upon a variation of the General Hough Transform that exploits the coherence across image frames that results from the relationship between known bounds on the object's velocity and the sensor frame rate. It is extremely efficient, running in O(N) for N range data points, and effectively trades off localization precision for runtime efficiency. The method has been implemented and tested on a variety of objects, including freeform surfaces, using both simulated and real data from Lidar and stereovision sensors. The motion bounds allow the inter-frame transformation space to be reduced to a reasonable, and indeed small size, containing only 729 possible states. In a variation, the rotational subspace is projected onto the translational subspace, which further reduces the transformation space to only 54 states. Experimental results confirm that the technique works well with very sparse data, possibly comprising only tens of points per frame, and that it is also robust to measurement error and outliers.
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paper_title: Dam wall detection and tracking using a Mechanically Scanned Imaging Sonar

paper_content:

In Dam inspection tasks, an underwater robot has to grab images while surveying the wall meanwhile maintaining a certain distance and relative orientation. This paper proposes the use of an MSIS (Mechanically Scanned Imaging Sonar) for relative positioning of a robot with respect to the wall. An imaging sonar gathers polar image scans from which depth images (Range & Bearing) are generated. Depth scans are first processed to extract a line corresponding to the wall (with the Hough Transform), which is then tracked by means of an EKF (Extended Kalman Filter) using a static motion model and an implicit measurement equation associating the sensed points to the candidate line. The line estimate is referenced to the robot fixed frame and represented in polar coordinates (ρ&θ) which directly corresponds to the actual distance and relative orientation of the robot with respect to the wall. The proposed system has been tested in simulation as well as in water tank conditions.
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paper_title: Implementation of Hough Transform as track detector

paper_content:

Hough Transform is a convenient tool for features extraction from images. In this paper an implementation of Hough Transform is considered for automatic track initiation in the surveillance radar space. The need of track initiation arises when there are many moving objects in the sensor surveillance volume and when it is not clear which measurement to which target belongs. If the type of target trajectories is known, the trajectories can be easily detected by performing corresponding Hough transform on the track image. Here, the effectiveness of the Hough transform track initiator is discussed. The influence of Hough parameter space granularity upon probability of track detection is analyzed. Analytical expressions for probability of track detection using Hough Transform are derived in the presence of normal distributed additive system noise, measurement noise and without any noises. A new parameter space structure, matching with measurement errors is proposed. The Monte Carlo simulation confirms received analytical result.
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paper_title: Spatial color histogram based center voting method for subsequent object tracking and segmentation

paper_content:

In this paper, we introduce an algorithm for object tracking in video sequences. In order to represent the object to be tracked, we propose a new spatial color histogram model which encodes both the color distribution and spatial information. Using this spatial color histogram model, a voting method based on the generalized Hough transform is employed to estimate the object location from frame to frame. The proposed voting based method, called the center voting method, requests every pixel near the previous object center to cast a vote for locating the new object center in the new frame. Once the location of the object is obtained, the back projection method is used to segment the object from the background. Experiment results show successful tracking of the object even when the object being tracked changes in size and shares similar color with the background.
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paper_title: Multi-Hough Transform Track Initiation for Detecting Target with Constant Acceleration

paper_content:

Track initiation is an important part in multi-target tracking, especially for the lower observable targets under the condition of heavy clutter. Multi-Hough transform track initiation for detecting target with constant acceleration in heavy clutter environment is proposed. In this algorithm, standard Hough transform is used to filter clutter first. Then sift the candidate tracks by using randomized Hough transform and minimum distance method. Finally, the true track number and parameters are given by subtractive clustering. Simulation results show that this algorithm can keep higher true track initiation probability but lower false track initiation probability. By means of multi-Hough transform and subtractive clustering, the heavy clutter is filtered, the probability of sifting true track from the candidate tracks is increased, and the time for track initiation is shorten. This method show better performance than other methods.
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paper_title: A shape-based voting algorithm for pedestrian detection and tracking

paper_content:

This paper presents the MOUGH (mixture of uniform and Gaussian Hough) Transform for shape-based object detection and tracking. We show that the edgels of a rigid object at a given orientation are approximately distributed according to a Gaussian mixture model (GMMs). A variant of the generalized Hough transform is proposed, voting using GMMs and optimized via Expectation-Maximization, that is capable of searching images for a mildly-deformable shape, based on a training dataset of (possibly noisy) images with only crude estimates of scale and centroid of the object in each image. Further modifications are proposed to optimize the algorithm for tracking. The method is able to locate and track objects reliably even against complex backgrounds such as dense moving foliage, and with a moving camera. Experimental results indicate that the algorithm is superior to previously published variants of the Hough transform and to active shape models in tracking pedestrians from a side view.
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paper_title: Underwater Cable Tracking by Visual Feedback

paper_content:

Nowadays, the surveillance and inspection of underwater installations, such as power and telecommunication cables and pipelines, is carried out by trained operators who, from the surface, control a Remotely Operated Vehicle (ROV) with cameras mounted over it. This is a tedious, time-consuming and expensive task, prone to errors mainly because of loss of attention or fatigue of the operator and also due to the typical low quality of seabed images. In this study, the development of a vision system guiding an Autonomous Underwater Vehicle (AUV) able to detect and track automatically an underwater power cable laid on the seabed has been the main concern. The system has been tested using sequences from a video tape obtained by means of a ROV during several tracking sessions of various real cables. The average success rate that has been achieved is about 90% for a frame rate higher than 25 frames/second.
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paper_title: Automatic Inspection of Cage Integrity with Underwater Vehicle

paper_content:

This thesis is a preliminary study for the initiative to develop an autonomous AUV for use in aquaculture. A micro-ROV is used to test the concept of inspecting net integrity in sh cages by analyzing video feed from a video camera. Software is developed, including an interactive GUI, thruster control, and the analysis algorithms. A laser module is also developed to measure the distance between the ROV and sh cage using two laser line generators. The conclusion is that checking for net integrity with a camera and computer vision software is a viable option in a future AUV. The laser module works well, providing reliable distance measurements for the ROV.
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paper_title: Image shape extraction using interval methods

paper_content:

Abstract This paper proposes a new method for recognition of geometrical shapes (such as lines, circles or ellipsoids) in an image. The main idea is to transform the problem into a bounded error estimation problem and then to use an interval-based method which is robust with respect to outliers. The approach is illustrated on an image taken by an underwater robot where a spheric buoy has to be detected. The results will then be compared to those obtained by the more classical generalized Hough transform.
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paper_title: Computer vision techniques for underwater navigation

paper_content:

In the world of autonomous underwater vehicles (AUV) the prominent form of sensing has been sonar due to cloudy water conditions and dispersion of light. Although underwater conditions are highly suitable for sonar, this does not mean that vision techniques should be completely ignored. There are situations where visibility is high, such is in calm waters, and where light dispersion is not an issue, such as shallow water or near the surface. In addition, even when visibility is low, once a certain proximity to an object exists, visibility can increase. The focus of this project is this gap in capability for AUVs, with an emphasis on computer-aided detection through machine learning and computer vision techniques. All experimentation utilizes the Stingray AUV, a small and unique vehicle designed by San Diego iBotics. The first experiment is detection of an anchored buoy, which mimics the real world application of mine detection for the Navy. The second experiment is detection of a pipe, which mimics pipes in bays and harbors. The current algorithm for this application uses boosting machine learning on hue, saturation, value (HSV) to create a classifier followed by post processing techniques to clean the resulting binary image. There are many further applications for computer- aided detection and classification of objects underwater, from environmental to military
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paper_title: An Active Contour and Kalman Filter for Underwater Target Tracking and Navigation

paper_content:

The underwater survey and inspection are mandatory step for offshore industry and for mining organization from onshore-offshore structures installations to operations (Whitcomb, 2000). There are two main areas where underwater target tracking are presently employed for offshore and mining industry. First, sea floor survey and inspection and second is subsea installation, inspection and maintenance. This paper takes second area into account and AUV vision system is developed that can detect and track underwater installation such as oil or gas pipeline, and power or telecommunication cables for inspection and maintenance application. The usage of underwater installations has increased many folds. It is desirable to do routine inspection and maintenance to protect them from marine traffic, such as fishery and anchoring (Asakawa, et al., 2000). Detection and tracking of underwater pipeline in complex marine environment is fairly difficult task to achieve, due to the frequent presence of noise in a subsea surface. Noise is commonly introduced in underwater images by sporadic marine growth and dynamic lighting condition. Traditionally, vigilances, inspections and maintenances of underwater man made structures are carried out by using the remotely operated vehicle (ROV) controlled from the mother ship by a trained operator (Whitcomb, 2000). The use of ROV’s for underwater inspections are expensive and time consuming job. Furthermore controlling the ROV’s from the surface by trained operators required continuous attention and concentration to keep the vehicle in the desired position and orientation. During long mission, this become a tedious task and highly prone to errors due to lack of attention and weariness. Moreover, tethering the vehicle limits both the operation range and the vehicle movements (Ortiz, 2002). The autonomous underwater vehicle’s do not have such limitation and essentially present better capabilities to those of ROV’s. AUV’s have a wider range of operations as there is no physical link between the control station on the surface and the vehicle, as they carry their power supply onboard. The usage of AUV for underwater pipeline or cable inspection and maintenance become very popular area of research for mining and offshore industries (Griffiths & Birch 2000). During the last decade lots of efforts have been done for design and development of different AUV tracking system, to do routine inspection and maintenance for underwater installation (Asif and Arshad 2006). Conventionally, the literatures on underwater pipeline or cable tracking can be categorized according to the sensors used for detection and tracking. There are mainly three types of sensors which used for that purpose. The first two types of sensors are the sonar and the pair of magnetometers (Petillot, et al.,
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paper_title: Combining spectral signals and spatial patterns using multiple Hough transforms: An application for detection of natural gas seepages

paper_content:

Object detection in remote sensing studies can be improved by incorporating spatial knowledge of an object in an image processing algorithm. This paper presents an algorithm based on sequential Hough transforms, which aims to detect botanical and mineralogical alterations that result from natural seepage of carbon dioxide and light hydrocarbons. As the observed alterations are not unique for gas seepages, these halos can only be distinguished from the background by their specific spatial pattern: the alterations are present as halos that line up along geological lineaments in the shallow subsurface. The algorithm is deployed in three phases: a prior spectral classification followed by two serialized Hough transforms. The first Hough transform fits circles through spectrally optimal matching pixels. Next, the centers of the detected circles are piped into the second Hough transform that detects points that are located on a line. Results show that our algorithm is successful in detecting the alteration halos. The number of false anomalies is sufficiently reduced to allow an objective detection based on field observations and spectral measurements.
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paper_title: Knowledge-based power line detection for UAV surveillance and inspection systems

paper_content:

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in the automatic surveillance of electrical power infrastructure. For an automatic vision based power line inspection system, detecting power lines from cluttered background an important and challenging task. In this paper, we propose a knowledge-based power line detection method for a vision based UAV surveillance and inspection system. A PCNN filter is developed to remove background noise from the images prior to the Hough transform being employed to detect straight lines. Finally knowledge based line clustering is applied to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective.
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paper_title: Detection and tracking of pipe joints in noisy images

paper_content:

T. A. Clarke, & T. J. Ellis.Centre for Digital Image Measurement and Analysis, School of Engineering,City University, Northampton Square, London. EC1V OHB. U. K.Email: X.Pan@city.ac.ukABSTRACTThe remote and automatic inspection of the inside of pipes and tunnels is an important industrial application area. The maincharacteristics of the environment found in commonly used pipes such as sewers are: limitations on the camera spatialposition; a large variety of surface features; a wide range of surface reflectivity due to the orientation ofparts of the pipe, e.g.thejoints; and many disturbances to the environment due for example to: mist; water spray; or hanging debris. The objectiveof this research is defect detection and classification; however, a first stage is the construction of a model of the pipe structureby pipe joint tracking. This paper describes work to exploit the knowledge of the environment to: build a model of defects,reflectivity characteristics and pipe characteristics; develop appropriate methods for grouping the pipe joint features withineach image from edge information; fit a pipe joint model (a circle, or connected arcs) to the grouped features; and to trackthese features in sequential images. Each stage in these processes has been analysed to optimise the performance in terms ofreliability and speed of operation. The methods that have been developed are described and results of robust pipe jointtracking over a large sequence of images are presented. The paper also presents results of experiments of applying severalcommon edge detectors to images which have been corrupted by JPEG encoding and spatial sub-sampling. The subsequentrobustness of a Hough based method for the detection of circular images features is also reported.Keywords: pipe joints, tracking, Hough transform, edge detection, JPEG.1. INTRODUCTIONThere has been considerable research into the inspection of industrial objects using computer vision techniques"2. Anincreasing area of interest occurs when either the camera or the objects are moving. In this case, two different proceduresmay be involved in the inspection process: (i) tracking to determine the position of the camera with respect to the subject ofinterest and (ii) feature detection. In this paper, the inspection of pipe joints is discussed. The current method of pipeinspection is by manually controlling a remote TV camera and classifying defects from images displayed on a monitor. Thisis both time consuming and costly. The inspection is also stored on video tape for subsequent archiving and analysis. Thesevideo tapes are used in this research to formulate strategies and develop software for analysis which may, in the future, beused in the field. The ultimate objective of the research is to provide an objective measurement of pipe defects whichmatches or exceeds the performance of a human operator. What makes this research particularly challenging is the high levelof noise encountered. The origins of this noise are many. For example: camera instability; poor illumination; occlusion;gross distortions in the pipe; the build up of extraneous matter on the walls and joints of the pipe; and the environment thatthe images are acquired in. As a first step in the inspection process the pipe joints, which are readily visible, are identified.The subsequent extraction of information about deformation, the build up of extraneous matter, or a large number of otherfeatures will be overlaid on the basic pipe model that will be constructed from the pipe joints. Alternative methods ofinspection by more direct means have been suggested3 and may be used in conjunction with the proposed method in thefuture. One aspect of the overall project is to investigate efficient methods of minimising the amount of data typically storedduring a sewer inspection. Many thousands of video tapes are used to store the images, and JPEG image compression hasbeen employed to significantly reduce the storage requirement for digital images.
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paper_title: Cell detection for bee comb images using Circular Hough Transformation

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Bee colony has to be at optimum health in order to produce optimum production of honey or to act as pollination agents for crop plantations. Current methods in the evaluation of bee colony health through trained personnel visual judgement and estimation and manual counting using wire grid as guidance are time consuming and accuracy individual dependent. This study explored the use of image processing methods to improve the existing methods. Although the cells in bee comb were in hexagonal shape, the spaces in the cells were observed to be circular that made it possible for the application of Circulation Hough Transformation (cHT), a common technique of image processing for detecting lines and circles, on the counting of number of cells in the bee comb. In addition, the cells had the attributes of no overlapping on each other, making the cells clearly visible. A prototype for cell detection algorithm using cHT detecting the number of cells in bee comb from a digital image was developed. The accuracy of the result obtained which was based on the number of the cells counted from the prototype compared to the manual counting showed an average cell detection rate of more than 80%. The difference in the output of cell counting through cHT and manual count was caused by detection errors. Detection errors were divided into two types: false acceptance and false rejection. False acceptance referred to the error of counting non-cell while false rejection referred to the error of undetected cell.
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paper_title: Label Inspection using the Hough Transform on Transputer Networks

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Abstract A variation of the Hough transform, the special normal/angle Hough transform (RθsHT) is described detailing its characteristics and advantages and suitability for a parallel implementation. The transputer is introduced and the application of the RθsHT on two popular types of transputer networks, a control-driven array and a demand-driven farm of transputers, is described. The methods employed for communication and the breakdown of the algorithms are described for each type of network. Timing results for the implementation on both networks are provided. The use of the RθsHT is then further examined with application to the problem of label inspection, and two types of labels are used to demonstrate how to pass or fail labels under inspection.
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paper_title: Automatic lumbar vertebrae segmentation in fluoroscopic images via optimised concurrent Hough transform

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We show how a new approach can automatically detect the positions and borders of vertebrae concurrently, relieving many of the problems experienced in other approaches. First, we use phase congruency to relieve the difficulty associated with threshold selection in edge detection of the illumination variant DVF images. Then, our new Hough transform approach is applied to determine the moving vertebrae, concurrently. We include optimisation via a genetic algorithm (as without it the extraction of moving multiple vertebrae is computationally daunting). Our results show that this new approach can indeed provide extractions of position and rotation which appear to be of sufficient quality to aid therapy and diagnosis of spinal disorders.
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paper_title: Warped Wigner-Hough Transform for Defect Reflection Enhancement in Ultrasonic Guided Wave Monitoring

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To improve the defect detectability of Lamb wave inspection systems, the application of nonlinear signal processing was investigated. The approach is based on a Warped Frequency Transform (WFT) to compensate the dispersive behavior of ultrasonic guided waves, followed by a Wigner-Ville time-frequency analysis and the Hough Transform to further improve localization accuracy. As a result, an automatic detection procedure to locate defect-induced reflections was demonstrated and successfully tested by analyzing numerically simulated Lamb waves propagating in an aluminum plate. The proposed method is suitable for defect detection and can be easily implemented for real-world structural health monitoring applications.
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paper_title: Robust Feature Matching with Alternate Hough and Inverted Hough Transforms

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We present an algorithm that carries out alternate Hough transform and inverted Hough transform to establish feature correspondences, and enhances the quality of matching in both precision and recall. Inspired by the fact that nearby features on the same object share coherent homographies in matching, we cast the task of feature matching as a density estimation problem in the Hough space spanned by the hypotheses of homographies. Specifically, we project all the correspondences into the Hough space, and determine the correctness of the correspondences by their respective densities. In this way, mutual verification of relevant correspondences is activated, and the precision of matching is boosted. On the other hand, we infer the concerted homographies propagated from the locally grouped features, and enrich the correspondence candidates for each feature. The recall is hence increased. The two processes are tightly coupled. Through iterative optimization, plausible enrichments are gradually revealed while more correct correspondences are detected. Promising experimental results on three benchmark datasets manifest the effectiveness of the proposed approach.
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paper_title: Distinctive Image Features from Scale-Invariant Keypoints

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The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.
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paper_title: Scene classification using color and structure-based features

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Study of scene understanding is a significant challenge. Many conventional methods proposed by these studies have been used or applied for many fields, for instance, scene recognition system for digital camera, similar image retrieval system on websites, and robot vision for autonomous or assist robots. From above, scene understanding is important, however it is as difficult as generic object recognition due to the diversity of categories. Many conventional methods have been proposed, and these focus on color or spatial frequency features in images. Especially, scene classification using features of spatial frequency show efficacy. Seen from the results of these studies, it seems that there is common features within a same scene. In this paper we proposed scene classification method with a focus on the structure of scene. We define the structure of scene as a set of lines in images and calculate these features using Hough space acquired by applying Hough transform to images. In addition, we calculate color features and combine those features. By using these two features we generate two strong classifiers with Boosting algorithm, and combine the results of each strong classifier. To test our approach, we executed two classes classification of scenes for each category using scene classification dataset. The results show that our approach is effective for several scenes especially the scene with artifacts.
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paper_title: Global Correlation Clustering Based on the Hough Transform

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In this article, we propose an efficient and effective method for finding arbitrarily oriented subspace clusters by mapping the data space to a parameter space defining the set of possible arbitrarily oriented subspaces. The objective of a clustering algorithm based on this principle is to find those among all the possible subspaces that accommodate many database objects. In contrast to existing approaches, our method can find subspace clusters of different dimensionality even if they are sparse or are intersected by other clusters within a noisy environment. A broad experimental evaluation demonstrates the robustness and effectiveness of our method. Copyright © 2008 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 1: 000-000, 2008
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Title: A Survey on Hough Transform, Theory, Techniques and Applications

Section 1: Introduction
Description 1: This section introduces the Hough Transform (HT), its development, and its fundamental principles and advantages. It also outlines the main contributions and the structure of the paper.

Section 2: Shape Detection
Description 2: This section discusses the use of the Hough Transform in detecting both analytical and irregular shapes, detailing the various techniques and modifications for line, circle, and ellipse detection, as well as other analytical and arbitrary shapes.

Section 3: Tone and Color
Description 3: This section explores the application of the Hough Transform to gray and color images, highlighting methods that work on images with higher information content without conversion to binary images.

Section 4: Speedup and Memory Saving
Description 4: This section covers various approaches for accelerating the Hough Transform and reducing its memory requirements, including hardware implementations, parallel computing, and memory optimization techniques.

Section 5: Hough Transform Applications
Description 5: This section categorizes and describes the wide range of applications of the Hough Transform, including traffic and transport, biometrics and man-machine interaction, 3D applications, object recognition and tracking, underwater applications, industrial and commercial applications, medical applications, and unconventional applications.

Section 6: Conclusions
Description 6: This section summarizes the survey, reflecting on the Hough Transform's advantages, its wide array of applications, and suggesting future research directions and potential advancements.