10 An Overview of Advances of Pattern Recognition Systems in Computer Vision
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paper_title: Pattern Recognition: Human and Mechanical

paper_content:

{PG,1 This variety relates to a plum tree which is of medium to large size, medium dense, vigorous and upright in growth; and is a productive and regular bearer of medium to large, early ripening, firm yellow flesh, clingstone fruit having a red (9-C-8 to 10-D-8) skin color.
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paper_title: Statistical Pattern Recognition: A Review

paper_content:

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
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paper_title: Generalised Syntactiv Pattern Recognition as a Unifying Approach in Image Analysis

paper_content:

In this paper we analyse theoretical foundations of syntactic pattern recognition and its relationships with mathematical linguistics, structural pattern recognition, and statistical pattern recognition. We point out that all the above mentioned methods are based on two fundamental operations: the combination of parts into wholes and abstraction. We provide examples of those operations in different contexts. Finally, we note that at this level of generalisation, all three main approaches to pattern recognition (structural, statistical and syntactic) can be merged into one powerful method. We outline a system based on these principles and intended to be used for the recognition of unconstrained handwriting.
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paper_title: Image Processing, Analysis and Machine Vision

paper_content:

List of Algorithms. Preface. Possible Course Outlines. 1. Introduction. 2. The Image, Its Representations and Properties. 3. The Image, Its Mathematical and Physical Background. 4. Data Structures for Image Analysis. 5. Image Pre-Processing. 6. Segmentation I. 7. Segmentation II. 8. Shape Representation and Description. 9. Object Recognition. 10. Image Understanding. 11. 3d Geometry, Correspondence, 3d from Intensities. 12. Reconstruction from 3d. 13. Mathematical Morphology. 14. Image Data Compression. 15. Texture. 16. Motion Analysis. Index.
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paper_title: Independent Component Analysis: Principles and Practice

paper_content:

1. Introduction Stephen Roberts and Richard Everson 2. Fast ICA by a fixed-point algorithm that maximizes non-Gaussianity Aapo Hyvarinen 3. ICA, graphical models and variational methods Hagai Attias 4. Nonlinear independent component analysis Juha Karhunen 5. Separation of non-stationary natural signals Lucas Parra and Clay Spence 6. Separation of non-stationary sources: algorithms and performance Jean-Francois Cardoso and Dinh-Tuan Pham 7. Blind source separation by sparse decomposition in a signal dictionary Michael Zibulevsky, Barak Pearlmutter, Pau Bofill and Pavel Kisilev 8. Ensemble learning for blind source separation James Miskin and David MacKay 9. Image processing methods using ICA mixture models Te-Won Lee and Michael S. Lewicki 10. Latent class and trait models for data classification and visualisation Mark Girolami 11. Particle filters for non-stationary ICA Richard Everson and Stephen Roberts 12. ICA: model order selection and dynamic source models William Penny, Stephen Roberts and Richard Everson.
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paper_title: Template matching: matched spatial filters and beyond

paper_content:

Template matching by means of cross-correlation is common practice in pattern recognition. However, its sensitivity to deformations of the pattern and the broad and unsharp peaks it produces are significant drawbacks. This paper reviews some results on how these shortcomings can be removed. Several techniques (Matched Spatial Filters, Synthetic Discriminant Functions, Principal Components Projections and Reconstruction Residuals) are reviewed and compared on a common task: locating eyes in a database of faces. New variants are also proposed and compared: least squares Discriminant Functions and the combined use of projections on eigenfunctions and the corresponding reconstruction residuals. Finally, approximation networks are introduced in an attempt to improve filter design by the introduction of nonlinearity.
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paper_title: A Neural Network Primer

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Neural networks are composed of basic units somewhat analogous to neurons. These units are linked to each other by connections whose strength is modifiable as a result of a learning process or algorithm. Each of these units integrates independently (in paral lel) the information provided by its synapses in order to evaluate its state of activation. The unit response is then a linear or nonlinear function of its activation. Linear algebra concepts are used, in general, to analyze linear units, with eigenvectors and eigenvalues being the core concepts involved. This analysis makes clear the strong similarity between linear neural networks and the general linear model developed by statisticians. The linear models presented here are the perceptron and the linear associator. The behavior of nonlinear networks can be described within the framework of optimization and approximation techniques with dynamical systems (e.g., like those used to model spin glasses). One of the main notions used with nonlinear unit networks is the notion of attractor. When the task of the network is to associate a response with some specific input patterns, the most popular nonlinear technique consists of using hidden layers of neurons trained with back-propagation of error. The nonlinear models presented are the Hopfield network, the Boltzmann machine, the back-propagation network and the radial basis function network.
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paper_title: Independent Component Analysis: Principles and Practice

paper_content:

1. Introduction Stephen Roberts and Richard Everson 2. Fast ICA by a fixed-point algorithm that maximizes non-Gaussianity Aapo Hyvarinen 3. ICA, graphical models and variational methods Hagai Attias 4. Nonlinear independent component analysis Juha Karhunen 5. Separation of non-stationary natural signals Lucas Parra and Clay Spence 6. Separation of non-stationary sources: algorithms and performance Jean-Francois Cardoso and Dinh-Tuan Pham 7. Blind source separation by sparse decomposition in a signal dictionary Michael Zibulevsky, Barak Pearlmutter, Pau Bofill and Pavel Kisilev 8. Ensemble learning for blind source separation James Miskin and David MacKay 9. Image processing methods using ICA mixture models Te-Won Lee and Michael S. Lewicki 10. Latent class and trait models for data classification and visualisation Mark Girolami 11. Particle filters for non-stationary ICA Richard Everson and Stephen Roberts 12. ICA: model order selection and dynamic source models William Penny, Stephen Roberts and Richard Everson.
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paper_title: Multiscale contour description for pattern recognition

paper_content:

In this paper, we present a new method and its preliminary results within the context of pattern analysis and recognition. This method is based on the multiscale analysis of a curve and deals with the contour of planar objects. Our method uses a low-pass Gaussian kernel to gradually smooth the contour by decreasing the filter bandwidth. Applying gain control to the smoothed contour stretches it to the same scale as the original one so that both contours intersect. By varying the bandwidth and marking all the intersection points between the smoothed contour and the original one we can generate the intersection points map (IPM) function. The initial results obtained by applying this method to various contours appears to indicate that the IPM function has some very interesting properties within the context of pattern recognition. It is translation and rotation insensitive and also scale change resistant for a large range of scaling. The IPM function generated when applied to noisy contours shows that the method is resistant to noise for a range of noise energy. Applying the features extracted by this method to retrieve a pattern from a database confirms the efficiency of the method.
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paper_title: State of the Art in Shape Matching

paper_content:

Large image databases are used in an extraordinary number of multimedia applications in fields such as entertainment, business, art, engineering, and science. Retrieving images by their content, as opposed to external features, has become an important operation. A fundamental ingredient for content-based image retrieval is the technique used for comparing images. There are two general methods for image comparison: intensity based (color and texture) and geometry based (shape). A recent user survey about cognition aspects of image retrieval shows that users are more interested in retrieval by shape than by color and texture [62]. However, retrieval by shape is still considered one of the most difficult aspects of content-based search. Indeed, systems such as IBM’s Query By Image Content, QBIC [57], perhaps one of the most advanced image retrieval systems to date, is relatively successful in retrieving by color and texture, but performs poorly when searching on shape. A similar behavior is exhibited in the new Alta Vista photo finder [10].
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paper_title: Classifier ensembles for changing environments

paper_content:

We consider strategies for building classifier ensembles for non-stationary environments where the classification task changes during the operation of the ensemble. Individual classifier models capable of online learning are reviewed. The concept of ”forgetting” is discussed. Online ensembles and strategies suitable for changing environments are summarized.
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paper_title: Pattern recognition : from classical to modern approaches

paper_content:

Pattern recognition - evolution of methodologies and data mining, A. Pal and S.K. Pal adaptive stochastic algorithms for pattern classification, M.A.L. Thathachar and P.S. Sastry shape in images, K.V. Mardia decision trees for classification - a review and some new results, R. Kothari and M. Dong syntactic pattern recognition, A.K. Majumder and A.K. Ray fuzzy sets as a logic canvas for pattern recognition, W. Pedrycz and N. Pizzi neural network based pattern recognition, V. David Sanchez networks of spiking neurons in data mining, K. Cios and D.M. Sala genetic algorithms, pattern classification and neural networks design, S. Bandyopadhyay et al rough sets in pattern recognition, A. Skowron and R. Swiniarski automated generation of qualitative representations of complex objects by hybrid soft-computing methods, E.H. Ruspini and I.S. Zwir) writing speed and writing sequence invariant on-line handwriting recognition, S-H Cha and S.N. Srihari tongue diagnosis based on biometric pattern recognition technology, K. Wang et al and other papers.
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paper_title: Knowledge Discovery in Databases: an Overview

paper_content:

After a decade of fundamental interdisciplinary research in machine learning, the spadework in this field has been done; the 1990s should see the widespread exploitation of knowledge discovery as an aid to assembling knowledge bases. The contributors to the AAAI Press book Knowledge Discovery in Databases were excited at the potential benefits of this research. The editors hope that some of this excitement will communicate itself to "AI Magazine readers of this article.
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paper_title: Image moments: a general and useful set of features for visual servoing

paper_content:

In this paper, we determine the analytical form of the interaction matrix related to any moment that can be computed from segmented images. The derivation method we present is based on Green's theorem. We apply this general result to classical geometrical primitives. We then consider using moments in image-based visual servoing. For that, we select six combinations of moments to control the six degrees of freedom of the system. These features are particularly adequate, if we consider a planar object and the configurations such that the object and camera planes are parallel at the desired position. The experimental results we present show that a correct behavior of the system is obtained if we consider either a simple symmetrical object or a planar object with complex and unknown shape.
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paper_title: Visual servoing using image features defined upon geometrical primitives

paper_content:

The image features used in visual servoing or tracking are generally the coordinates of image points. In this paper, we present how it is possible to define more complex visual features based on geometrical primitives such as lines, spheres and cylinders. Using such features enables one to realize a large variety of robotics tasks depending on the desired virtual linkage between the camera and the objects in the scene. We then describe a control scheme in closed loop with respect to these image features which is based on the task function approach. This scheme combines the regulation of the selected vision-based task with the minimization of a secondary cost function, such as a trajectory tracking using the robot degrees of freedom not constrained by the vision-based task. We finally present real time experimental results obtained with a camera mounted on the end-effector of a 6-DOF robot. >
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paper_title: An introduction to biometric recognition

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A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.
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paper_title: Biometrics: a grand challenge

paper_content:

Reliable person identification is an important problem in diverse businesses. Biometrics, identification based on distinctive personal traits, has the potential to become an irreplaceable part of any identification system. While successful in some niche markets, the biometrics technology has not yet delivered its promise of foolproof automatic identification. With the availability of inexpensive biometric sensors and computing power, it is becoming increasingly clear that widespread usage of biometric person identification is being stymied by our lack of understanding of three fundamental problems; (i) How to accurately and efficiently represent and recognize biometric patterns? (ii) How to guarantee that the sensed measurements are not fraudulent? and (iii) How to make sure that the application is indeed exclusively using pattern recognition for the expressed purpose (function creep (A. K. JAin et al., December 1998))? Solving these core problems will not only catapult biometrics into mainstream applications but will also stimulate adoption of other pattern recognition applications for providing effective automation of sensitive tasks without jeopardizing our individual freedoms. For these reasons, we view biometrics as a grand challenge - "a fundamental problem in science and engineering with broad economic and scientific impact".
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paper_title: The MPEG-7 Visual standard for content description - an overview

paper_content:

The MPEG-7 visual standard under development specifies content-based descriptors that allow users or agents (or search engines) to measure similarity in images or video based on visual criteria, and can be used to efficiently identify, filter, or browse images or video based on visual content. More specifically, MPEG-7 specifies color, texture, object shape, global motion, or object motion features for this purpose. This paper outlines the aim, methodologies, and broad details of the MPEG-7 standard development for visual content description.
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paper_title: MPEG-7 Visual Shape Descriptors

paper_content:

This paper describes techniques and tools for shape representation and matching, developed in the context of MPEG-7 standardization. The application domains for each descriptor are considered, and the contour-based shape descriptor is presented in some detail. Example applications are also shown.
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paper_title: State of the Art in Shape Matching

paper_content:

Large image databases are used in an extraordinary number of multimedia applications in fields such as entertainment, business, art, engineering, and science. Retrieving images by their content, as opposed to external features, has become an important operation. A fundamental ingredient for content-based image retrieval is the technique used for comparing images. There are two general methods for image comparison: intensity based (color and texture) and geometry based (shape). A recent user survey about cognition aspects of image retrieval shows that users are more interested in retrieval by shape than by color and texture [62]. However, retrieval by shape is still considered one of the most difficult aspects of content-based search. Indeed, systems such as IBM’s Query By Image Content, QBIC [57], perhaps one of the most advanced image retrieval systems to date, is relatively successful in retrieving by color and texture, but performs poorly when searching on shape. A similar behavior is exhibited in the new Alta Vista photo finder [10].
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paper_title: Textural Features for Image Classification

paper_content:

Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
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paper_title: Review of shape representation and description techniques

paper_content:

More and more images have been generated in digital form around the world. There is a growing interest in 1nding images in large collections or from remote databases. In order to 1nd an image, the image has to be described or represented by certain features. Shape is an important visual feature of an image. Searching for images using shape features has attracted much attention. There are many shape representation anddescription techniques in the literature. In this paper, we classify and review these important techniques. We examine implementation procedures for each technique and discuss its advantages and disadvantages. Some recent research results are also included and discussed in this paper. Finally, we identify some promising techniques for image retrieval according to standard principles.
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paper_title: Planar closed contour representation by invariant under a general affine transformation

paper_content:

This paper presents a new multiscale and zero-crossing, curvature-based shape representation technique for planar curves with general affine transformation. The method consists of the concept of describing a curve at varying levels of detail using features that are invariant with respect to transformations that do not change the shape of the curve. The process of describing a curve at increasing levels of abstraction is referred to as the affine evolution of that curve. This affine evolution does not change the physical interpretation of planar curves and characterize the behaviors of inflexion points of the curves during its evolution.
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paper_title: Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization

paper_content:

MPEG-7 is the first international standard which contains a number of key techniques from Computer Vision and Image Processing. The Curvature Scale Space technique was selected as a contour shape descriptor for MPEG-7 after substantial and comprehensive testing, which demonstrated the superior performance of the CSS-based descriptor. Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization is based on key publications on the CSS technique, as well as its multiple applications and generalizations. The goal was to ensure that the reader will have access to the most fundamental results concerning the CSS method in one volume. These results have been categorized into a number of chapters to reflect their focus as well as content. The book also includes a chapter on the development of the CSS technique within MPEG standardization, including details of the MPEG-7 testing and evaluation processes which led to the selection of the CSS shape descriptor for the standard. The book can be used as a supplementary textbook by any university or institution offering courses in computer and information science.
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paper_title: MPEG-7 Visual Shape Descriptors

paper_content:

This paper describes techniques and tools for shape representation and matching, developed in the context of MPEG-7 standardization. The application domains for each descriptor are considered, and the contour-based shape descriptor is presented in some detail. Example applications are also shown.
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paper_title: Silhouette-based occluded object recognition through curvature scale space

paper_content:

A complete and practical system for occluded object recognition has been developed which is very robust with respect to noise and local deformations of shape (due to weak perspective distortion, segmentation errors and non-rigid material) as well as scale, position and orientation changes of the objects. The system has been tested on a wide variety of free-form 3D objects. An industrial application is envisaged where a fixed camera and a light-box are utilized to obtain images. Within the constraints of the system, every rigid 3D object can be modeled by a limited number of classes of 2D contours corresponding to the object's resting positions on the light-box. The contours in each class are related to each other by a 2D similarity transformation. The Curvature Scale Space technique [26, 28] is then used to obtain a novel multi-scale segmentation of the image and the model contours. Object indexing [16, 32, 36] is used to narrow down the search space. An efficient local matching algorithm is utilized to select the best matching models.
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paper_title: MPEG-7 Visual Shape Descriptors

paper_content:

This paper describes techniques and tools for shape representation and matching, developed in the context of MPEG-7 standardization. The application domains for each descriptor are considered, and the contour-based shape descriptor is presented in some detail. Example applications are also shown.
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paper_title: Matching and Retrieval of Distorted and Occluded Shapes Using Dynamic Programming

paper_content:

We propose an approach for matching distorted and possibly occluded shapes using dynamic programming (DP). We distinguish among various cases of matching such as cases where the shapes are scaled with respect to each other and cases where an open shape matches the whole or only a part of another open or closed shape. Our algorithm treats noise and shape distortions by allowing matching of merged sequences of consecutive small segments in a shape with larger segments of another shape, while being invariant to translation, scale, orientation, and starting point selection. We illustrate the effectiveness of our algorithm in retrieval of shapes on two data sets of two-dimensional open and closed shapes of marine life species. We demonstrate the superiority of our approach over traditional approaches to shape matching and retrieval based on Fourier descriptors and moments. We also compare our method with SQUID, a well-known method which is available on the Internet. Our evaluation is based on human relevance judgments following a well-established methodology from the information retrieval field.
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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: An Overview of Multimedia Content-Based Retrieval Strategies

paper_content:

Recently, information retrieval for text and multimedia content has become an important research area. Content based retrieval in multimedia is a challenging problem since multimedia data needs detailed interpretation from pixel values. In this paper, an overview of the content based retrieval is presented along with the different strategies in terms of syntactic and semantic indexing for retrieval. The matching techniques used and learning methods employed are also analyzed, and key directions for future research are also presented.
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paper_title: The MPEG-7 Visual standard for content description - an overview

paper_content:

The MPEG-7 visual standard under development specifies content-based descriptors that allow users or agents (or search engines) to measure similarity in images or video based on visual criteria, and can be used to efficiently identify, filter, or browse images or video based on visual content. More specifically, MPEG-7 specifies color, texture, object shape, global motion, or object motion features for this purpose. This paper outlines the aim, methodologies, and broad details of the MPEG-7 standard development for visual content description.
---
paper_title: MPEG-7 Visual Shape Descriptors

paper_content:

This paper describes techniques and tools for shape representation and matching, developed in the context of MPEG-7 standardization. The application domains for each descriptor are considered, and the contour-based shape descriptor is presented in some detail. Example applications are also shown.
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paper_title: Photo tourism: exploring photo collections in 3D

paper_content:

We present a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end that automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo explorer uses image-based rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, and to annotate image details, which are automatically transferred to other relevant images. We demonstrate our system on several large personal photo collections as well as images gathered from Internet photo sharing sites.
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paper_title: CIRES: a system for content-based retrieval in digital image libraries

paper_content:

This paper presents CIRES, a new online system for a content-based retrieval in digital image libraries. Content-based image retrieval systems have traditionally used color and texture analyses. These analyses have not always achieved adequate level of performance and user satisfaction. The growing need for robust image retrieval systems has led to a need for additional retrieval methodologies. CIRES addresses this issue by using image structure in addition to color and texture. The efficacy of using structure in combination with color and texture is demonstrated.
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paper_title: Planar closed contour representation by invariant under a general affine transformation

paper_content:

This paper presents a new multiscale and zero-crossing, curvature-based shape representation technique for planar curves with general affine transformation. The method consists of the concept of describing a curve at varying levels of detail using features that are invariant with respect to transformations that do not change the shape of the curve. The process of describing a curve at increasing levels of abstraction is referred to as the affine evolution of that curve. This affine evolution does not change the physical interpretation of planar curves and characterize the behaviors of inflexion points of the curves during its evolution.
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paper_title: Multiscale contour description for pattern recognition

paper_content:

In this paper, we present a new method and its preliminary results within the context of pattern analysis and recognition. This method is based on the multiscale analysis of a curve and deals with the contour of planar objects. Our method uses a low-pass Gaussian kernel to gradually smooth the contour by decreasing the filter bandwidth. Applying gain control to the smoothed contour stretches it to the same scale as the original one so that both contours intersect. By varying the bandwidth and marking all the intersection points between the smoothed contour and the original one we can generate the intersection points map (IPM) function. The initial results obtained by applying this method to various contours appears to indicate that the IPM function has some very interesting properties within the context of pattern recognition. It is translation and rotation insensitive and also scale change resistant for a large range of scaling. The IPM function generated when applied to noisy contours shows that the method is resistant to noise for a range of noise energy. Applying the features extracted by this method to retrieve a pattern from a database confirms the efficiency of the method.
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paper_title: Distance sets for shape filters and shape recognition

paper_content:

We introduce a novel rich local descriptor of an image point, we call the (labeled) distance set, which is determined by the spatial arrangement of image features around that point. We describe a two-dimensional (2D) visual object by the set of (labeled) distance sets associated with the feature points of that object. Based on a dissimilarity measure between (labeled) distance sets and a dissimilarity measure between sets of (labeled) distance sets, we address two problems that are often encountered in object recognition: object segmentation, for which we formulate a distance sets shape filter, and shape matching. The use of the shape filter is illustrated on printed and handwritten character recognition and detection of traffic signs in complex scenes. The shape comparison procedure is illustrated on handwritten character classification, COIL-20 database object recognition and MPEG-7 silhouette database retrieval.
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paper_title: Shape descriptors for non-rigid shapes with a single closed contour

paper_content:

The Core Experiment CE-Shape-1 for shape descriptors performed for the MPEG-7 standard gave a unique opportunity to compare various shape descriptors for non-rigid shapes with a single closed contour. There are two main differences with respect to other comparison results reported in the literature: (1) For each shape descriptor the experiments were carried out by an institute that is in favor of this descriptor. This implies that the parameters for each system were optimally determined and the implementations were thoroughly rested. (2) It was possible to compare the performance of shape descriptors based on totally different mathematical approaches. A more theoretical comparison of these descriptors seems to be extremely hard. In this paper we report on the MPEG-7 Core Experiment CE-Shape.
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paper_title: Translation, rotation, and scale-invariant object recognition

paper_content:

A method for object recognition that is invariant under translation, rotation and scaling is addressed. The first step of the method (pre-processing) takes into account the invariant properties of the normalized moment of inertia and a novel coding that extracts topological object characteristics. The second step (recognition) is achieved by using a holographic nearest-neighbor (HHN) algorithm, in which vectors obtained in the pre-processing step are used as inputs to it. The algorithm is tested in character recognition, using the 26 upper-case letters of the alphabet. Only four different orientations and one size (for each letter) were used for training. Recognition was tested with 17 different sizes and 14 rotations. The results are encouraging, since we achieved 98% correct recognition. Tolerance to boundary deformations and random noise was tested. Results for character recognition in "real" images of car plates are presented as well.
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paper_title: Multiscale curvature-based shape representation using B-spline wavelets

paper_content:

This paper presents a new multiscale curvature-based shape representation technique with application to curve data compression using B-spline wavelets. The evolution of the curve is implemented in the B-spline scale-space, which enjoys a number of advantages over the classical Gaussian scale-space, for instance, the availability of fast algorithms. The B-spline wavelet transforms are used to efficiently estimate the multiscale curvature functions. Based on the curvature scale-space image, we introduce a coarse-to-fine matching algorithm which automatically detects the dominant points and uses them as knots for curve interpolation.
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paper_title: Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization

paper_content:

MPEG-7 is the first international standard which contains a number of key techniques from Computer Vision and Image Processing. The Curvature Scale Space technique was selected as a contour shape descriptor for MPEG-7 after substantial and comprehensive testing, which demonstrated the superior performance of the CSS-based descriptor. Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization is based on key publications on the CSS technique, as well as its multiple applications and generalizations. The goal was to ensure that the reader will have access to the most fundamental results concerning the CSS method in one volume. These results have been categorized into a number of chapters to reflect their focus as well as content. The book also includes a chapter on the development of the CSS technique within MPEG standardization, including details of the MPEG-7 testing and evaluation processes which led to the selection of the CSS shape descriptor for the standard. The book can be used as a supplementary textbook by any university or institution offering courses in computer and information science.
---
paper_title: Shape matching and object recognition using shape contexts

paper_content:

We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by: (1) solving for correspondences between points on the two shapes; (2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits, and the COIL data set.
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paper_title: 2-D object recognition using invariant contour descriptor and projective refinement

paper_content:

Abstract This paper presents an efficient model-based recognition method to recognize 2-D objects and to obtain correspondences between models and scene boundaries with a subpixel positioning error. As a shape signature for a contour, we propose a descriptor consisting of five-point invariants, which are used to index a hash table. Also, we propose a projective refinement as a verification method to compute exact correspondences between models and scene contour points. This method repeatedly computes projective transformation using a weighted pseudo inverse. We present an error model for five-point invariants, which are used to define a similarity between two descriptors, to determine a searching bound in indexing, and to obtain the weights in the projective refinement. In experiments using seriously distorted images of forty models, this method led to the recognition of planar curved objects. A transformation using the correspondence between the model and scene points on contours was also obtained.
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paper_title: Object descriptors invariant to affine distortions

paper_content:

The Trace transform is a generalisation of the Radon transform that allows us to construct image features that are invariant to a chosen group of image transformations. In this paper we propose functionals which can be computed from the image function, and which can be used to describe objects in a way that is invariant to the group of affine transforms. We demonstrate the usefulness of the constructed image descriptors in retrieving images from an image database and compare it with the moments based method.
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paper_title: Planar closed contour representation by invariant under a general affine transformation

paper_content:

This paper presents a new multiscale and zero-crossing, curvature-based shape representation technique for planar curves with general affine transformation. The method consists of the concept of describing a curve at varying levels of detail using features that are invariant with respect to transformations that do not change the shape of the curve. The process of describing a curve at increasing levels of abstraction is referred to as the affine evolution of that curve. This affine evolution does not change the physical interpretation of planar curves and characterize the behaviors of inflexion points of the curves during its evolution.
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paper_title: Multiscale curvature-based shape representation using B-spline wavelets

paper_content:

This paper presents a new multiscale curvature-based shape representation technique with application to curve data compression using B-spline wavelets. The evolution of the curve is implemented in the B-spline scale-space, which enjoys a number of advantages over the classical Gaussian scale-space, for instance, the availability of fast algorithms. The B-spline wavelet transforms are used to efficiently estimate the multiscale curvature functions. Based on the curvature scale-space image, we introduce a coarse-to-fine matching algorithm which automatically detects the dominant points and uses them as knots for curve interpolation.
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paper_title: Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization

paper_content:

MPEG-7 is the first international standard which contains a number of key techniques from Computer Vision and Image Processing. The Curvature Scale Space technique was selected as a contour shape descriptor for MPEG-7 after substantial and comprehensive testing, which demonstrated the superior performance of the CSS-based descriptor. Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization is based on key publications on the CSS technique, as well as its multiple applications and generalizations. The goal was to ensure that the reader will have access to the most fundamental results concerning the CSS method in one volume. These results have been categorized into a number of chapters to reflect their focus as well as content. The book also includes a chapter on the development of the CSS technique within MPEG standardization, including details of the MPEG-7 testing and evaluation processes which led to the selection of the CSS shape descriptor for the standard. The book can be used as a supplementary textbook by any university or institution offering courses in computer and information science.
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paper_title: Multiscale contour description for pattern recognition

paper_content:

In this paper, we present a new method and its preliminary results within the context of pattern analysis and recognition. This method is based on the multiscale analysis of a curve and deals with the contour of planar objects. Our method uses a low-pass Gaussian kernel to gradually smooth the contour by decreasing the filter bandwidth. Applying gain control to the smoothed contour stretches it to the same scale as the original one so that both contours intersect. By varying the bandwidth and marking all the intersection points between the smoothed contour and the original one we can generate the intersection points map (IPM) function. The initial results obtained by applying this method to various contours appears to indicate that the IPM function has some very interesting properties within the context of pattern recognition. It is translation and rotation insensitive and also scale change resistant for a large range of scaling. The IPM function generated when applied to noisy contours shows that the method is resistant to noise for a range of noise energy. Applying the features extracted by this method to retrieve a pattern from a database confirms the efficiency of the method.
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Title: An Overview of Advances of Pattern Recognition Systems in Computer Vision

Section 1: Introduction
Description 1: Introduce the concept of pattern recognition (PR), its importance, and briefly describe various methodologies employed in PR systems.

Section 2: Statistical Approach
Description 2: Explain the statistical approach to pattern recognition, how it uses statistics and probabilities, and its common application in PR systems.

Section 3: Syntactic Approach
Description 3: Discuss the syntactic approach, its reliance on the relationships between features, and its use for complex pattern structures.

Section 4: Template Matching
Description 4: Describe the template matching approach, its application in image processing, and its methods for identifying shapes within images.

Section 5: Neural Networks
Description 5: Provide an overview of neural network-based PR systems, describing how they simulate the biological brain to resolve complex problems.

Section 6: A Generic Scheme of a Pattern Recognition System
Description 6: Present a generic scheme of a PR system, detailing the analysis/description task and the classification/recognition task.

Section 7: Pattern Recognition Applications and an Overview of Advances
Description 7: Offer examples of various applications of pattern recognition and highlight advances in the field.

Section 8: Pattern Recognition in Robotics
Description 8: Discuss the application of PR systems in robotics, including examples of robots using visual pattern recognition.

Section 9: Pattern Recognition in Biometrics
Description 9: Describe the use of PR systems in biometric authentication, the types of biometric characteristics, and related challenges.

Section 10: Content-Based Image Retrieval
Description 10: Explain content-based image retrieval systems, their features, and their significance in managing large image databases.

Section 11: An Overview of the Advances in Pattern Recognition
Description 11: Provide an overview of recent advances in pattern recognition, including notable systems and emerging trends.

Section 12: Application Example Based on the MSGPR Method
Description 12: Introduce and illustrate a specific application example based on the MSGPR method for character recognition.

Section 13: Experimental Results
Description 13: Present the experimental results obtained from applying the MSGPR method to car plate character recognition.

Section 14: Conclusion
Description 14: Summarize the main points discussed in the paper and offer insights on the future directions and challenges in pattern recognition.