A REVIEW OF VISION BASED DEFECT DETECTION USING IMAGE PROCESSING TECHNIQUES FOR BEVERAGE MANUFACTURING INDUSTRY
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paper_title: An automated inspection system for textile fabrics based on Gabor filters

paper_content:

This paper studies the application of advanced computer image processing techniques for solving the problem of automated defect detection for textile fabrics. A new defect detection scheme is proposed, which consists of an odd symmetric real-valued Gabor filter, an even symmetric real-valued Gabor filter and one smoothing filter. In developing the scheme, the Gabor filters are designed on the basis of the texture features extracted optimally from a non-defective fabric image by using a Gabor wavelet network (GWN). The performance of the proposed defect detection scheme is evaluated off-line by using a set of fabric images taken from a database consisting of a wide variety of homogeneous fabric images. The results exhibit accurate defect detection with low false alarms, thus showing the effectiveness and robustness of the proposed scheme. To evaluate the performance of the proposed defect detection scheme further, real-time tests are conducted by using a prototyped automated defect detection system. The experimental results obtained further confirm the efficiency, effectiveness and robustness of the proposed detection scheme.
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paper_title: Vision-based defect detection of scale-covered steel billet surfaces

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Vision-based inspection systems have been widely investigated for the detection and classification of defects in various industrial product. We present a new defect detection algorithm for scale-covered steel billet surfaces. Because of the availability of various kinds of steel, presence of scales, and manufacturing conditions, the features of billet surface images are not uniform. In particular, scales severely change the properties of defect-free surfaces. Moreover, the various kinds of possible defects make their detection difficult. In order to resolve these problems and to improve the detection performance, two methods are proposed. First, undecimated wavelet transform and vertical projection profile are presented. Second, a method for detecting the variations in the block features along the vertical direction is proposed. The former method can effectively detect vertical line defects, and the latter can efficiently detect the remaining defects, except the vertical line defects. The experimental results conducted on billet surface images obtained from actual steel production lines show that the proposed algorithm is effective for defect detection of scale-covered steel billet surfaces.
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paper_title: Machine vision system: a tool for quality inspection of food and agricultural products

paper_content:

Quality inspection of food and agricultural produce are difficult and labor intensive. Simultaneously, with increased expectations for food products of high quality and safety standards, the need for accurate, fast and objective quality determination of these characteristics in food products continues to grow. However, these operations generally in India are manual which is costly as well as unreliable because human decision in identifying quality factors such as appearance, flavor, nutrient, texture, etc., is inconsistent, subjective and slow. Machine vision provides one alternative for an automated, non-destructive and cost-effective technique to accomplish these requirements. This inspection approach based on image analysis and processing has found a variety of different applications in the food industry. Considerable research has highlighted its potential for the inspection and grading of fruits and vegetables, grain quality and characteristic examination and quality evaluation of other food products like bakery products, pizza, cheese, and noodles etc. The objective of this paper is to provide in depth introduction of machine vision system, its components and recent work reported on food and agricultural produce.
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paper_title: Automated visual inspection in the semiconductor industry: A survey

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Abstract Automated visual inspection is an image-processing technique for quality control and production line automation. This paper reviews various optical inspection approaches in the semiconductor industry and categorize the previous literatures by the inspection algorithm and inspected products. The vision-based algorithms that had been adopted in the visual inspection systems include projection methods, filtering-based approaches, learning-based approaches, and hybrid methods. To discuss about the practical applications, the semiconductor industry covers the manufacturing and production of wafer, thin-film transistor liquid crystal displays, and light-emitting diodes. To improve the yield rate and reduce manufacturing costs, the inspection devices are widely installed in the design, layout, fabrication, assembly, and testing processes of production lines. To achieve a high robustness and computational efficiency of automated visual inspection, interdisciplinary knowledge between precision manufacturing and advanced image-processing techniques is required in the novel system design. This paper reviews multiple defect types of various inspected products which can be referenced for further implementations and improvements.
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paper_title: Workforce development 'TIPS' (theory into practice strategies): a resource kit for the alcohol and other drugs field.

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This Resource Kit aims to provide straightforward and practical guidance, tools and resources to support workforce development activities and initiatives in the Alcohol and Other Drugs (AOD) field. The Resource Kit comprises 14 chapters: an introduction to workforce development and 13 workforce development topics relevant to the AOD field. Each chapter contains evidence-based strategies to address a particular workforce development issue, as well as resources and tools that can be used to implement the strategies. Each chapter can be treated as a stand alone section, however, as workforce development topics are inherently interrelated, links between chapters are identified throughout the Kit. Chapters: 1 An Introduction to workforce development 2 Clinical supervision 3 Developing effective teams 4 Evaluating AOD projects and programs 5 Goal setting 6 Mentoring 7 Organisational change 8 Performance appraisal 9 Professional development 10 Recruitment and selection 11 Retention 12 Worker performance 13 Worker wellbeing 14 Workplace support
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paper_title: Machine learning approach for automated visual inspection of machine components

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Visual inspection on the surface of components is a main application of machine vision. Visual inspection finds its application in identifying defects such as scratches, cracks bubbles and measurement of cutting tool wear and welding quality. Machine learning approach to machine vision helps in automating the design process of machine vision systems. This approach involves image acquisition, preprocessing, feature extraction and classification. Study shows a library of features, and classifiers are available to classify the data. However, only the best combination of them can yield the highest classification accuracy. In this study, images with different known conditions were acquired, preprocessed, and histogram features were extracted. The classification accuracies of C4.5 classifier algorithm and Naive Bayes algorithm were compared, and results are reported. The study shows that C4.5 algorithm performs better.
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paper_title: Human Factors in Aviation: An Overview

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Publisher Summary At any point in the history of human factors in aviation, one could characterize its current state at that time by the progress that had been made and by the opportunities that presented themselves for the future. Keeping this in mind, this chapter aims to provide a brief snapshot of aviation human factors. It first highlights a few brief themes that show the progress that has been made in aviation research. Following this, it discusses the opportunities that human factors professionals, pilots, instructors, maintenance personnel, air traffic controllers, and interested parties in general are presented with currently to drive future generations of aviation. The chapter orients the reader to the causes and effects that guide the cutting edge of the field now.
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paper_title: Visual inspection of products with geometrical quality characteristics of known tolerances

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Visual inspection; Image processing; Thresholding; Tolerance; Regression; ANOVA Abstract Image analysis techniques are being increasingly used to automate industrial inspection. The manual activity of inspection could be subjective and highly dependent on the experience of human personnel. In a previous work, the authors presented two approaches utilizing image anal- ysis to inspect products visually. The product is accepted or rejected based on its conformance to specified tolerances, where conformance is analogy measured through statistical indices like corre- lation and root mean square error. In this work, the authors introduce a novel visual inspection approach that can be used on line to test simultaneously multiple quality characteristics. The approach utilizes image processing tools to deal with the product image; and extract features of its geometrical characteristics. Based on toler- ance bands of each characteristic, an index is experimentally developed to reflect the deviation of a quality characteristic dimension from its nominal value; and one can decide whether a characteristic complies with the pre-specified tolerance. Statistical analysis proved that there is a strong associa- tion between the developed indices and the deviations of quality characteristics from their target values. Linear regression models are proved to model these associations; and are used to give the corresponding indices' values relative to the tolerance specifications. The developed approach is
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paper_title: A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

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Visual inspection of civil infrastructure is essential for condition assessment.We focus on concrete bridges, tunnels, underground pipes, and asphalt pavements.Accordingly, we review the latest computer vision based defect detection methods.Using computer vision most relevant types of defects can be automatically detected.Automatic defect properties retrieval and assessment has not been achieved yet. To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research.
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paper_title: Development of Intelligent Visual Inspection System (IVIS) for Bottling Machine

paper_content:

This paper presents a research on developing an intelligent visual inspection system (IVIS) for bottling machine, focusing on the development of image processing framework for defect detection. The objective of the research is to contribute a method on modeling, integrating and enhancing IVIS for the process of quality control in industrial area. IVIS application for quality control was studied using plastic bottles on a production line simulation. An experiment had done by using developed software and special equipments such as conveyor belt, lighting source, and a Web camera (Webcam) to capture the image. The experiment result shows that the system is accurate enough to detect moving object on the speed at 106 rpm with the accuracy of the image acquisition is 94.264%.
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paper_title: The visual inspection of product surfaces.

paper_content:

Many companies practice the human visual inspection of their production. In some cases, this inspection takes the aesthetics aspects of the product surface into account. However, only a few companies have developed methods for this type of inspection. In this article, how a sensory analysis test can be applied for the visual inspection of product surface is described. A synthesis of the specifications for an effective visual inspection of products with high added-value is detailed. These specifications can also be adapted for any type of product, including food products and packaging.
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paper_title: Online Machine Vision Inspection System for Detecting Coating Defects in Metal Lids

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Experts forecast that globalization and open economy will be the characteristic of the 21 st century. Industry in developing countries must be well prepared and competitive in order to have a vital role. These countries will not be able to access global markets unless they procure the needed technology to manufacture high quality, up to the standard products. Automated visual inspection is one of these emerging technologies. Higher production speeds require higher inspection speeds which can be implemented best by replacing human visual inspection procedures by machine vision inspection systems. This paper presents an online machine vision system design that is capable of detecting defects in rubber coating of metal lids during manufacturing. The system is simple and low cost consisting of a CCD camera mounted on a conveyer belt and connected to a PC through FireWire (IEEE 1394). Image acquisition, analysis, and inspection are implemented using National Instrument's (NI) Vision Builder tool. The inspection algorithm is realized through a four stage process: starting with acquisition to enhancement, thresholding and finally object matching.
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paper_title: AUTOMATED DEFORM DETECTION ON AUTOMOTIVE BODY PANELS USING GRADIENT FILTERING AND FUZZY C-MEAN SEGMENTATION

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Automatic deform detection on automotive body panel is challenging owing to its localization on a large surface, variation in appearance, and their rare occurrences. It is difficult to detect these deforms either by original models or by small-sample statistics using a single threshold. As a consequence, this problem is focussed to derive a lot of good-quality deform detected from the surface images. These detections should discriminate the various surface deforms when fed to suitable image processing algorithms. This paper used gradient filtering and background illumination correction to identify the deform area. An algorithm to segment the deform area has been developed. It segments the deformation by using Fuzzy C-Means (FCM) segmentation. The algorithm is being test on three samples which are car door model, curve and flat surface with two types of deformations which is ding and dent deformations that occur on the surface.
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paper_title: Analysis of energy saving potentials in intelligent manufacturing: A case study of bakery plants

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Abstract To address the global challenge of the climate change, more strict legislations worldwide on carbon emission reductions have put energy intensive industries under immense pressure to improve the energy efficiency. Due to the lack of technical support and financial incentives, a range of technical and economic barriers still exist for small-medium enterprises (SMEs). This paper first introduces a point energy technology, which is developed for SMEs to improve the insight of the energy usage in the manufacturing processes and installed in a local bakery. Statistical analysis of electricity consumption data over a seven-day period is conducted, including the identification of operational modes for individual processing units using an enhanced clustering method and the voltage unbalance conditions associated with these identified modes. Two technical strategies, namely electrical load allotment and voltage unbalance minimisation, are then proposed, which could attain more than 800 k w h energy saving during this period and the current unbalance could be reduced to less than 10%. In addition, the genetic algorithm is deployed to solve the job shop scheduling problem based upon the commercial electrical tariffs, and this reduces the electricity bill by £80 per day in the case study. Implementation of the recommendations based on the above analysis therefore may potentially yield significant financial and environmental benefits.
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paper_title: Machine vision system: a tool for quality inspection of food and agricultural products

paper_content:

Quality inspection of food and agricultural produce are difficult and labor intensive. Simultaneously, with increased expectations for food products of high quality and safety standards, the need for accurate, fast and objective quality determination of these characteristics in food products continues to grow. However, these operations generally in India are manual which is costly as well as unreliable because human decision in identifying quality factors such as appearance, flavor, nutrient, texture, etc., is inconsistent, subjective and slow. Machine vision provides one alternative for an automated, non-destructive and cost-effective technique to accomplish these requirements. This inspection approach based on image analysis and processing has found a variety of different applications in the food industry. Considerable research has highlighted its potential for the inspection and grading of fruits and vegetables, grain quality and characteristic examination and quality evaluation of other food products like bakery products, pizza, cheese, and noodles etc. The objective of this paper is to provide in depth introduction of machine vision system, its components and recent work reported on food and agricultural produce.
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paper_title: Research and implementation of machine vision technologies for empty bottle inspection systems

paper_content:

Abstract Key technologies in empty bottle inspection systems are studied in this paper to solve detecting error and poor adaptability problems. Those technologies have two different approaches: the ones in the first group locate and track bottle mouth, bottle bottom and walls while the other group technologies involve defect detecting. Such vision inspection systems are required to perform with high accuracy and adaptability under high speed and mechanical vibration working conditions. This study proposes distinctive algorithms for bottle locating, tracking and defect detecting based on inspection requirements and images of bottle mouth, bottom and walls. On the premise of satisfying the inspecting requirements, the simplicity, ease of implementation and universality of the algorithms are considered to improve the detection speed and the adaptability of the system to different kinds of bottles. Experiments showed that the system proposed in this paper can improve the detection accuracy and speed.
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paper_title: Development of Intelligent Visual Inspection System (IVIS) for Bottling Machine

paper_content:

This paper presents a research on developing an intelligent visual inspection system (IVIS) for bottling machine, focusing on the development of image processing framework for defect detection. The objective of the research is to contribute a method on modeling, integrating and enhancing IVIS for the process of quality control in industrial area. IVIS application for quality control was studied using plastic bottles on a production line simulation. An experiment had done by using developed software and special equipments such as conveyor belt, lighting source, and a Web camera (Webcam) to capture the image. The experiment result shows that the system is accurate enough to detect moving object on the speed at 106 rpm with the accuracy of the image acquisition is 94.264%.
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paper_title: The visual inspection of product surfaces.

paper_content:

Many companies practice the human visual inspection of their production. In some cases, this inspection takes the aesthetics aspects of the product surface into account. However, only a few companies have developed methods for this type of inspection. In this article, how a sensory analysis test can be applied for the visual inspection of product surface is described. A synthesis of the specifications for an effective visual inspection of products with high added-value is detailed. These specifications can also be adapted for any type of product, including food products and packaging.
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paper_title: A new machine vision real-time detection system for liquid impurities based on dynamic morphological characteristic analysis and machine learning

paper_content:

Abstract Impurity in transparent-bottled liquid is a serious production accident in the field of beverage and medicine industry. However, the existing detection systems are difficult to distinguish impurities with dynamic interference (bubbles and stains) and detect impurities located at the edge of the bottle. In order to solve the problems stated above, a new machine vision system for detecting tiny and dynamic impurities is proposed in this paper. In the system, circularity calculation, longitudinal frame-difference method, orthogonal-axis inspection and K-Nearest Neighbor (KNN) machine learning algorithm are combined together to realize the automatic and real-time detection. Experimental results demonstrate that, after completing machine learning, the weighted error of the proposed system for detecting impurities can be effectively controlled at about 0.9% even in dynamic interference environment, which is great significance to safety production in beverage and medicine industry.
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paper_title: Automated Visual Inspection System for Specifying Brick Quality

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Automated visual inspection system has been developed to specify brick quality and the accepting of the bricks in a production line. This system is based on CMOS web-camera placed in manufacture line. Depending on diameters, area, perimeter and cracks of a brick, a strong algorithm has been developed, and this algorithm is created to befit the required for measuring bricks quality. The quality is measured by fuzzy system which can give percent accepting to a brick under the test. Fuzzy reasoning gives the system more reliability than other inspection system.
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paper_title: An intelligent robot for flexible quality inspection

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Quality inspection is a task in every production concerned with a product's functionality, as well as its appearance. While dimensional or functional defects can easily be detected automatically, cosmetic defects are still found by skilled personnel only. For high-output productions companies cannot afford to inspect every part for its flawlessness, yet the ever growing demand for impeccable products asks for novel inspection solutions. We introduce an intelligent robot capable of performing visual quality inspection for geometric defects, but also for defects that fall short of any conventional description. A robot vision system is described that can intelligently analyze component's cosmetic quality along with an implementation scheme introducing the usage of the same. Depending on the object to be inspected and the level of detail, a constant processing time far below 1s can be achieved, allowing the implementation of an economic cosmetic inspection solution. Our intuitive inspection robot is reliable, highly adaptable, and fast, which can address cosmetic quality inspection utilizing an artificial neural network approach, yielding superior results where traditional template-based solutions lack functionality. An implemented case study is presented exhibiting an inspection success rate of 100% for two different scenarios.
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paper_title: Fault detection and localization in empty water bottles through machine vision

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The paper presents a vision-based approach for fault detection of empty water bottles. Our goal is to find all defects in the empty water bottles. The main constraint is the real time operation as the bottles move continuously on the conveyer belt. Using camera images, we first find the position of the bottle in the image by a generalized Hough transform. Then we check the bottles for any defects or the presence of alien objects using rectangular and circular area intensity inspection. Bottles with any defects are rejected and so separated from the main stream. In the statistics obtained, our inspection system successfully rejected all the faulty or defective bottles. The good bottle rejection rate was lower than 1% of the total number of bottles inspected. This system can be used for inspecting any type of empty bottles for defects or the presence of alien objects.
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paper_title: Defect Detection and Identification in Textile Fabric by SVM Method

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In this paper we use a support vector machine (SVM) for defects identification in textile. This new approach serves in the fast detection and extraction of fabric defects from the images of textile fabric based on geometrical analysis of the textile pattern images. Actually, most defects arising in the production process of textile material are still detected by human inspection (1) and the work of inspectors is very tedious and time consuming. They have to detect small details that can be located; the identification rate is about 70%. In addition, the effectiveness of visual inspection decreases quickly with fatigue. For these reasons, an algorithm has been proposed for the defect identification and classification. So, the images analyzed came from an artificial vision system that we used to acquire and memorize those images under jpeg format. The vision system is composed of a camera with a sensor 512X512 Pixels. The classifier SVM was manipulated to classify all defects in the defected fabric. I. INTRODUCTION The Automated visual inspection of industrial goods for quality control plays an ever-increasing role in the production process as the global market pressures put higher and higher demand on quality. In most cases, the quality of inspections through visual inspection is still carried out by humans. However, the reliability of manual inspection is limited by ensuing fatigue and inattentiveness. For example: in textile industry, the most highly trained inspectors can only detect about 70% of the defects (2). Therefore, the automation of visual inspection process is required to maintain high quality of products at a high-speed production. The analysis of images in an artificial vision system is an expensive process. SVM was applied to many problems, and has demonstrated its excellence over traditional methods. The methodology used in this investigation follows the pattern recognition schema: image segmentation, feature extraction and classification. The segmentation process is oriented towards the detection of edges and suspecting the area (defect). This technique seeks changes in the gray values of the image (edges) and then identifying zones delimited as suspect defect. However, only some of them are defects and the others are false alarms. Subsequently, the feature extraction is centered principally on the measurement of properties of the regions. Finally, classification orders segmented regions.
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paper_title: Automated detection of breast cancer lesions using adaptive thresholding and morphological operation

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One of the imaging modalities for early detection of breast cancer is ultrasonography (USG). The detection is based on lesions identification. Radiologists still manually conduct early detection of the lesions. Hence, the detection results tend to be subjective and may cause different interpretations due to the different level of knowledge and experience of the radiologists. In this research, the proposed method for the detection of automatic lesions uses 30 images consisting of 20 images benign lesions and 10 images of malignant lesions. At the pre-processing stage, adaptive median filter is applied and is followed by adaptive thresholding method at the segmentation process. The final stage uses morphological operations. The result shows that the proposed method successfully achieved the accuracy of 95.19%, sensitivity of 84.13% and specificity of 96.2%. These results indicate that the system can be used to assist the experts or operators from radiology team more objectively in breast cancer lession detection.
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paper_title: Plastic bottle shape classification using partial erosion-based approach

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In this paper, a work on representing plastic bottle shape using erosion based approach for an automated classification is reported. Morphological operations are used to describe the structure or form of an image. By using the two-dimensional description of plastic bottle silhouettes, edge detection of the object silhouette is performed followed by the erosion process. This work will compare two versions of erosion which are regular erosion, the Matlab function imerode and the improved version of erosion which is called partial erosion. Normalization procedure in which the sum pixel value after erosion is divided by the sum pixel of the whole silhouette is done. The normalized values are grouped into histograms of 9 bins of sum pixel value(9HbSPV), find its maximum number to form as a feature set and is then used as inputs to train a neural network for plastic bottle shape classification. Results obtained showed that the proposed feature extraction method can be applied to discriminate plastic bottles according to shape, either slim or broad bottles, efficiently.
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paper_title: Review of shape representation and description techniques

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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: Apple Defect Detection Using Statistical Histogram Based Fuzzy C-Means Algorithm

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NA
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paper_title: A color image segmentation algorithm based on region growing

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Image segmentation is a classic subject in the field of image processing and also is a hotspot and focus of image processing techniques. With the improvement of computer processing capabilities and the increased application of color image, the color image segmentation are more and more concerned by the researchers. Color image segmentation methods can be seen as an extension of the gray image segmentation method in the color images, but many of the original gray image segmentation methods can not be directly applied to color images. This requires to improve the method of original gray image segmentation method according to the color image which have the feature of rich information or research a new image segmentation method it specially used in color image segmentation. This article proposes a color image segmentation method of automatic seed region growing on basis of the region with the combination of the watershed algorithm with seed region growing algorithm which based on the traditional seed region growing algorithm.
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paper_title: Automatic classification of fruit defects based on co-occurrence matrix and neural networks

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Nowadays the effective and fast detection of fruit defects is one of the main concerns for fruit selling companies. This paper presents a new approach that classifies fruit surface defects in color and texture using Radial Basis Probabilistic Neural Networks (RBPNN). The texture and gray features of defect area are extracted by computing a gray level co-occurrence matrix and then defect areas are classified by the applied RBPNN solution.
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paper_title: Image processing based detection & classification of blood group using color images

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Domain of image processing is progressing a lot and has achieved tremendous milestones. Image processing is helping in many ways for the researchers to achieve their goals especially in security and medical fields. Detection of blood group in disaster or remote areas where expert is unavailable is challenge. In this paper we have proposed a system which will detect blood group using image processing techniques. Steps to detect the type of blood group using image processing techniques are discussed. Successful results have been obtained and accuracy of the proposed system is optimal
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paper_title: Infected Fruit Part Detection using K-Means Clustering Segmentation Technique

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Nowadays, overseas commerce has increased drastically in many countries. Plenty fruits are imported from the other nations such as oranges, apples etc. Manual identification of defected fruit is very time consuming. This work presents a novel defect segmentation of fruits based on color features with K-means clustering unsupervised algorithm. We used color images of fruits for defect segmentation. Defect segmentation is carried out into two stages. At first, the pixels are clustered based on their color and spatial features, where the clustering process is accomplished. Then the clustered blocks are merged to a specific number of regions. Using this two step procedure, it is possible to increase the computational efficiency avoiding feature extraction for every pixel in the image of fruits. Although the color is not commonly used for defect segmentation, it produces a high discriminative power for different regions of image. This approach thus provides a feasible robust solution for defect segmentation of fruits. We have taken apple as a case study and evaluated the proposed approach using defected apples. The experimental results clarify the effectiveness of proposed approach to improve the defect segmentation quality in aspects of precision and computational time. The simulation results reveal that the proposed approach is promising.
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paper_title: A SIMPLE DIGITAL IMAGING METHOD FOR MEASURING AND ANALYZING COLOR OF FOOD SURFACES

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Abstract This paper presents a simple method that uses a combination of digital camera, computer, and graphics software to measure and analyze the surface color of food products. The method has also the advantages of being versatile and affordable. The images of the food products can be displayed on computer screen or printed on paper for qualitative analysis of color and structure. Quantitative information such as color distribution and averages (in terms of L ∗ , a ∗ and b ∗ values) can also be determined readily.
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paper_title: Color image segmentation using pixel wise support vector machine classification

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Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a color image segmentation using pixel wise support vector machine (SVM) classification. Firstly, the pixel-level color feature and texture feature of the image, which is used as input of SVM model (classifier), are extracted via the local homogeneity model and Gabor filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.
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paper_title: Capacitive detection of filling levels in a cup

paper_content:

This contribution presents a smart cup that uses a capacitive sensor to detect its filling level in order to monitor fluid intake over time. Dehydration is frequently diagnosed in hospitals among the elderly and connected to numerous sequelae and deaths. An automated monitoring system that detects daily fluid intake of a patient could reduce the vulnerability to dehydration and therefore the vast expenses that are associated with this condition. The smart cup obtains the current filling level from a capacitive sensor consisting of multiple serially arranged discrete electrodes. It is placed on the outside surface of its wall and shielded against external disturbances. Sensor data is processed applying multiple signal filters and error correction methods. Drinking volume of beverages at room temperature is detected accurately and reliably but error rate rises for very cold or hot liquids. The prototype integrates all components in a compact way, is dishwasher-safe and can be charged inductively. Data is transmitted to a base station via Bluetooth Low Energy. This way, a monitoring device is presented which will help preventing dehydration of elderly people.
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paper_title: Feature extraction algorithm for fill level and cap inspection in bottling machine

paper_content:

Automated Visual Inspection Systems (AVIS) have a strong ability to improve bottle manufacturing quality control by means of inspecting products automatically instead of through manual inspections. AVIS automatically tends to make a suitable decision in process and results classification according to the images of the products via image processing and Artificial Intelligence techniques. Since bottling is one of the most common packaging styles in the food and medical industries, in this paper we will concentrate on the visual inspection of bottles. Checking the quality of the cap closure and over-filling/under-filling checks for the level of the liquid in the bottle have been investigated to reach an optimized bottle product. Therefore, in this research general hardware and modules for these systems are investigated. Besides, new techniques of bottle inspection are reviewed along with presenting previous work of other researchers. Subsequently we will propose a feature extraction algorithm to inspect cap closure and level of the liquid in the bottle together, in the same system. According to the new proposed method, our system classifies three situations for cap condition and three situations for the condition of the level of the liquid. As a result the system has investigated 9 situations. The algorithm of the system will accept its system when the liquid level is in the correct position and the cap is in the normal condition. Other situations will be rejected. The proper algorithm which is proposed here using bottle visual inspection techniques leads our system to reach an optimized liquid level with a high quality of the cap closure.
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paper_title: Self-Organizing Map based Extended Fuzzy C-Means (SEEFC) algorithm for image segmentation

paper_content:

Display OmittedSOM and EFCM Integration in a Glance View. The motivation for this integration was the difficulties faced in using SOM for Image segmentation. The number of clusters needs to be specified for improving the performance.For different images the map sizes have to be optimized. It was also observed that SOM has limited capabilities for the representation of hierarchical relation of mapped data making interpretation difficult.In this work a novel attempt taking advantage of capabilities of self-organizing map and extended fuzzy clustering for Image segmentation is reported.Incorporating SOM we can find the essential information by mapping image data to a two dimensional space and with the help of EFCM we estimate the number of clusters.Finally the cluster centers are used for clustering purpose and image segmentation. A Novel hybrid algorithm based on Self-Organizing-Map (SOM) and Extended Fuzzy C-Means (EFCM) named self-organizing-map based extended fuzzy c-means (SEEFC) has been designed and implemented for image segmentation. The proposed algorithm works in three stages. At first, the images are decomposed by discrete wavelet transform (DWT) into various frequencies and gradient, pixel value and statistical parameters are obtained to form a feature prototype. The feature prototypes are input to the Self Organizing Map (SOM). Finally, the codebook vectors of the trained SOM have been clustered automatically using Extended Fuzzy C-Means (EFCM). The clustered codebook vector centers are used for image segmentation based on minimum distance criterion. The proposed method has been tested with images from Berkeleys database and results obtained are promising. The segmentation results are evaluated against the ground truth. Comparisons with another state of the art approaches indicate advantages of the SOM based EFCM algorithm.
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paper_title: Fast segmentation of bone in CT images using 3D adaptive thresholding

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Abstract Fast bone segmentation is often important in computer-aided medical systems. Thresholding-based techniques have been widely used to identify the object of interest (bone) against dark backgrounds. However, the darker areas that are often present in bone tissue may adversely affect the results obtained using existing thresholding-based segmentation methods. We propose an automatic, fast, robust and accurate method for the segmentation of bone using 3D adaptive thresholding. An initial segmentation is first performed to partition the image into bone and non-bone classes, followed by an iterative process of 3D correlation to update voxel classification. This iterative process significantly improves the thresholding performance. A post-processing step of 3D region growing is used to extract the required bone region. The proposed algorithm can achieve sub-voxel accuracy very rapidly. In our experiments, the segmentation of a CT image set required on average less than 10 s per slice. This execution time can be further reduced by optimizing the iterative convergence process.
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paper_title: Fuzzy model-based clustering and its application in image segmentation

paper_content:

A fuzzy generalized Gaussian density (GGD) segmentation model is proposed.A GGD-based agglomerative fuzzy algorithm is developed.A two-stage fuzzy GGD segmentation algorithm is proposed. This paper proposes a novel fuzzy model-based unsupervised learning algorithm with boundary correction for image segmentation. We propose a fuzzy Generalized Gaussian Density (GGD) segmentation model and the GGD-based agglomerative fuzzy algorithm for grouping image pixels. The merits of algorithm are that it is not sensitive to initial parameters and that the number of groups can be estimated via the validation technique. To minimize the objective function of the model, we define a dissimilarity measure based on the KullbackLeibler divergence of the GGDs that computes the discrepancy between GGDs in the space of generalized probability distributions. To effectively segment images with various textures, we propose a two-stage fuzzy GGD segmentation algorithm. The first stage adopts the proposed fuzzy algorithm to obtain initial segmentation and the second stage improves initial segmentation by image boundary correction. Experimental results show that our proposed method has a promising performance compared with existing approaches.
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paper_title: Automatic Brain Lesion Detection And Classification Based On Diffusion-Weighted Imaging Using Adaptive Thresholding And A Rule-Based Classifier

paper_content:

In this paper,a brain lesion detection and classification approach using thresholding and a rule-based classifier is proposed.Four types of brain lesions based on diffusion-weighted imaging i.e. acute stroke,solid tumor, chronic stroke,and necrosis are analyzed.The analysis is divided into four stages:pre-processing,segmentation, feature extraction,and classification.In the detection and segmentation stage,the image is divided into 8x8 macro-block regions.Adaptive thresholding technique is ::: applied to segment the lesion’s region.Statistical features are measured on the region of interest.A rulebased classifier is used to classify four types of lesions.Jaccard’s similarity index of the segmentation ::: results for acute stroke,solid tumor,chronic stroke,and necrosis are 0.8, 0.55, 0.27, and 0.42,respectively.The classification accuracy is 93% for acute stroke,73% for solid tumor,84% for chronic stroke,and 60% for necrosis. Overall,adaptive thresholding provides high segmentation performance for hyper-intensity lesions.The best segmentation and classification performance is achieved for acute stroke.The establishment of the technique could be used to automate the diagnosis and to clearly understand major brain lesions.
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paper_title: Problem structuring using computer-aided morphological analysis

paper_content:

General morphological analysis (GMA) is a method for structuring and investigating the total set of relationships contained in multidimensional, usually non-quantifiable, problem complexes. Pioneered by Fritz Zwicky at the California Institute of Technology in the 1930s and 1940s, it relies on a constructed parameter space, linked by way of logical relationships, rather than on causal relationships and a hierarchal structure. During the past 10 years, GMA has been computerized and extended for structuring and analysing complex policy spaces, developing futures scenarios and modelling strategy alternatives. This article gives a historical and theoretical background to GMA as a problem structuring method, compares it with a number of other ‘soft-OR’ methods, and presents a recent application in structuring a complex policy issue. The issue involves the development of an extended producer responsibility (EPR) system in Sweden.
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paper_title: Shape segmentation using local slippage analysis

paper_content:

We propose a method for segmentation of 3D scanned shapes into simple geometric parts. Given an input point cloud, our method computes a set of components which possess one or more slippable motions: rigid motions which, when applied to a shape, slide the transformed version against the stationary version without forming any gaps. Slippable shapes include rotationally and translationally symmetrical shapes such as planes, spheres, and cylinders, which are often found as components of scanned mechanical parts. We show how to determine the slippable motions of a given shape by computing eigenvalues of a certain symmetric matrix derived from the points and normals of the shape. Our algorithm then discovers slippable components in the input data by computing local slippage signatures at a set of points of the input and iteratively aggregating regions with matching slippable motions. We demonstrate the performance of our algorithm for reverse engineering surfaces of mechanical parts.
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paper_title: A COMPREHENSIVE METHOD FOR IMAGE CONTRAST ENHANCEMENT BASED ON GLOBAL -LOCAL CONTRAST AND LOCAL STANDARD DEVIATION

paper_content:

Image contrast enhancement is used to increase the detailed visibility of an image by performing some transformation on input image. Global contrast enhancement and local contrast enhancement both are the very popular method for contrast enhancement. But these two methods are also not sufficient in order to enhance an image when image has some constant area and it is not possible to perform any kind of transformation to neighbor hooding pixels of the central pixel. Due to that some information may be lost. This paper proposed a method to remove the divided by zero condition, arises due to the local standard deviation of that constant area in order to enhance an image. This method enhances an image without any loss in detailed visibility of an image.
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paper_title: A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape

paper_content:

Since their introduction as a means of front propagation and their first application to edge-based segmentation in the early 90's, level set methods have become increasingly popular as a general framework for image segmentation. In this paper, we present a survey of a specific class of region-based level set segmentation methods and clarify how they can all be derived from a common statistical framework. ::: ::: Region-based segmentation schemes aim at partitioning the image domain by progressively fitting statistical models to the intensity, color, texture or motion in each of a set of regions. In contrast to edge-based schemes such as the classical Snakes, region-based methods tend to be less sensitive to noise. For typical images, the respective cost functionals tend to have less local minima which makes them particularly well-suited for local optimization methods such as the level set method. ::: ::: We detail a general statistical formulation for level set segmentation. Subsequently, we clarify how the integration of various low level criteria leads to a set of cost functionals. We point out relations between the different segmentation schemes. In experimental results, we demonstrate how the level set function is driven to partition the image plane into domains of coherent color, texture, dynamic texture or motion. Moreover, the Bayesian formulation allows to introduce prior shape knowledge into the level set method. We briefly review a number of advances in this domain.
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paper_title: Modified adaptive thresholding using integral image

paper_content:

Adaptive thresholding, the simple way to perform image segmentation, is a form of image thresholding used to classify pixels as dark and light. Taking grayscale image as an input for this task is only good in case that text appears in low intensity area. In case that text appears in high intensity area, it leads to lower recall rate for text detection process. However, it can be fixed by taking an inverted-grayscale image instead. The problem is how to determine automatically whether it is better to take the normal-grayscale or inverted-grayscale image for each original image. This paper proposes the simple way to do that by means of the adaptive thresholding using the integral image itself with some additional steps based on its principle. The proposed method consists of two main process, low/high intensity area segmentation and modified adaptive thresholding.
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paper_title: Gradient based adaptive thresholding

paper_content:

For images with poor and non-uniform illumination, adaptive thresholding is required to separate the objects of interest from the background. In this paper a new approach to create an adaptive threshold surface is proposed to segment an image. The technique is inspired by the Yanowitz's method and is improved upon by the introduction of a simpler and more accurate threshold surface. The method is tested on several images of different patterns with varying illumination and the results are compared to the ones produced by a number of adaptive thresholding algorithms. In order to demonstrate the effectiveness, the proposed method had been implemented in medical and document images. The proposed method compares favorably against those using watershed and morphology in medical image and favorably against variable threshold and adaptive Otsu's N-thresholding for document image.
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paper_title: Optimized Adaptive Thresholding based Edge Detection Method for MRI Brain Images

paper_content:

Edge detection is one of the fundamental tool in image processing, machine vision and computer vision, which aim at identifying points in a digital image. It is an important tool for medical image segmentation and 3D reconstruction. Generally, edge has detected according to some early brought forward algorithms such as gradient-based algorithm and templatebased algorithm, but they are not so good for noisy medical image edge detection. In order to overcome this problem, adaptive threshold using ACO has proposed. Ant colony optimization technique is used for computing an optimal threshold value used by adaptive threshold for edge detection. The various edge detection algorithms are compared with the proposed algorithm and their performance are evaluated using the evaluation metrics. From the experimental results, the proposed algorithm was better than the adaptive threshold method.
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paper_title: Automatic Brain Lesion Detection And Classification Based On Diffusion-Weighted Imaging Using Adaptive Thresholding And A Rule-Based Classifier

paper_content:

In this paper,a brain lesion detection and classification approach using thresholding and a rule-based classifier is proposed.Four types of brain lesions based on diffusion-weighted imaging i.e. acute stroke,solid tumor, chronic stroke,and necrosis are analyzed.The analysis is divided into four stages:pre-processing,segmentation, feature extraction,and classification.In the detection and segmentation stage,the image is divided into 8x8 macro-block regions.Adaptive thresholding technique is ::: applied to segment the lesion’s region.Statistical features are measured on the region of interest.A rulebased classifier is used to classify four types of lesions.Jaccard’s similarity index of the segmentation ::: results for acute stroke,solid tumor,chronic stroke,and necrosis are 0.8, 0.55, 0.27, and 0.42,respectively.The classification accuracy is 93% for acute stroke,73% for solid tumor,84% for chronic stroke,and 60% for necrosis. Overall,adaptive thresholding provides high segmentation performance for hyper-intensity lesions.The best segmentation and classification performance is achieved for acute stroke.The establishment of the technique could be used to automate the diagnosis and to clearly understand major brain lesions.
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paper_title: Automated detection of breast cancer lesions using adaptive thresholding and morphological operation

paper_content:

One of the imaging modalities for early detection of breast cancer is ultrasonography (USG). The detection is based on lesions identification. Radiologists still manually conduct early detection of the lesions. Hence, the detection results tend to be subjective and may cause different interpretations due to the different level of knowledge and experience of the radiologists. In this research, the proposed method for the detection of automatic lesions uses 30 images consisting of 20 images benign lesions and 10 images of malignant lesions. At the pre-processing stage, adaptive median filter is applied and is followed by adaptive thresholding method at the segmentation process. The final stage uses morphological operations. The result shows that the proposed method successfully achieved the accuracy of 95.19%, sensitivity of 84.13% and specificity of 96.2%. These results indicate that the system can be used to assist the experts or operators from radiology team more objectively in breast cancer lession detection.
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paper_title: Adaptive thresholding method for binarization blueprint images

paper_content:

This paper describes a method to threshold blueprint images based on geometrical features. Scanned blueprint images often contain a lot of noise, which was produced in the process when the original map was printed on the blue paper and smudged due to long time storage. To remove the noise and extract the lines and numbers are a difficult task. We propose a new method which utilizes geometrical features combined with gray level analysis to threshold the blueprint image. The result shows a good performance compared with Otsu's (1979) method, Nilblack's method and other methods.
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paper_title: G.: Adaptive thresholding using the integral image

paper_content:

Image thresholding is a common task in many computer vision and graphics applications. The goal of thresholding an image is to classify pixels as either "dark" or "light." Adaptive thresholding is a form of thresholding that takes into account spatial variations in illumination. We present a technique for real-time adaptive thresholding using the integral image of the input. Our technique is an extension of a previous method. However, our solution is more robust to illumination changes in the image. Additionally, our method is simple and easy to implement. Our technique is suitable for processing live video streams at a real-time frame-rate, making it a valuable tool for interactive applications such as augmented reality. Source code is available online.
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paper_title: Multiscale morphological segmentation of gray-scale images

paper_content:

In this paper, the authors have proposed a method of segmenting gray level images using multiscale morphology. The approach resembles the watershed algorithm in the sense that the dark (respectively bright) features which are basically canyons (respectively mountains) on the surface topography of the gray level image are gradually filled (respectively clipped) using multiscale morphological closing (respectively opening) by reconstruction with isotropic structuring element. The algorithm detects valid segments at each scale using three criteria namely growing, merging and saturation. Segments extracted at various scales are integrated in the final result. The algorithm is composed of two passes preceded by a preprocessing step for simplifying small scale details of the image that might cause over-segmentation. In the first pass feature images at various scales are extracted and kept in respective level of morphological towers. In the second pass, potential features contributing to the formation of segments at various scales are detected. Finally the algorithm traces the contours of all such contributing features at various scales. The scheme after its implementation is executed on a set of test images (synthetic as well as real) and the results are compared with those of few other standard methods. A quantitative measure of performance is also formulated for comparing the methods.
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paper_title: Problem structuring using computer-aided morphological analysis

paper_content:

General morphological analysis (GMA) is a method for structuring and investigating the total set of relationships contained in multidimensional, usually non-quantifiable, problem complexes. Pioneered by Fritz Zwicky at the California Institute of Technology in the 1930s and 1940s, it relies on a constructed parameter space, linked by way of logical relationships, rather than on causal relationships and a hierarchal structure. During the past 10 years, GMA has been computerized and extended for structuring and analysing complex policy spaces, developing futures scenarios and modelling strategy alternatives. This article gives a historical and theoretical background to GMA as a problem structuring method, compares it with a number of other ‘soft-OR’ methods, and presents a recent application in structuring a complex policy issue. The issue involves the development of an extended producer responsibility (EPR) system in Sweden.
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paper_title: Quantitative analysis of morphological techniques for automatic classification of micro-calcifications in digitized mammograms

paper_content:

In this paper we present an evaluation of four different algorithms based on Mathematical Morphology, to detect the occurrence of individual micro-calcifications in digitized mammogram images from the mini-MIAS database. A morphological algorithm based on contrast enhancement operator followed by extended maxima thresholding retrieved most of micro-calcifications. In order to reduce the number of false positives produced in that stage, a set of features in the spatial, texture and spectral domains was extracted and used as input in a support vector machine (SVM). Results provided by TMVA (Toolkit for Multivariate Analysis) produced the ranking of features that allowed discrimination between real micro-calcifications and normal tissue. An additional parameter, that we called Signal Efficiency*Purity (denoted SE*P), is proposed as a measure of the number of micro-calcifications with the lowest quantity of noise. The SVM with Gaussian kernel was the most suitable for detecting micro-calcifications. Sensitivity was obtained for the three types of breast. For glandular, it detected 137 of 163 (84.0%); for dense tissue, it detected 74 of 85 (87.1%) and for fatty breast, it detected 63 of 71 (88.7%). The overall sensitivity was 85.9%. The system also was tested in normal images, producing an average of false positives per image of 13 in glandular tissue, 11 in dense tissue and 15 in fatty tissue.
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paper_title: Image feature extraction for application of biometric identification of iris - a morphological approach

paper_content:

We present a new approach based on morphological operators for application of biometric identification of individuals by segmentation and analysis of the iris. Algorithms based on morphological operators are developed to segment the iris region from the eye image and also to highlight chosen iris patterns. The extracted features are used to represent and characterize the iris. In order to properly extract the desired patterns, an algorithm is proposed to produce skeletons with unique paths among end-points and nodes. The representation obtained by the morphological processing is stored for identification purposes. To illustrate the efficiency of the morphological approach, some results are presented. The proposed system was derived to present low complexity implementation and low storage requirements.
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paper_title: A new approach for the morphological segmentation of high-resolution satellite imagery

paper_content:

A new segmentation method based on the morphological characteristic of connected components in images is proposed. Theoretical definitions of morphological leveling and morphological spectrum are used in the formal definition of a morphological characteristic. In multiscale segmentation, this characteristic is formalized through the derivative of the morphological profile. Multiscale segmentation is particularly well suited for complex image scenes such as aerial or fine resolution satellite images, where very thin, enveloped and/or nested regions must be retained. The proposed method performs well in the presence of both low radiometric contrast and relatively low spatial resolution. Those factors may produce a textural effect, a border effect, and ambiguity in the object/background distinction. Segmentation examples for satellite images are given.
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paper_title: Surface defects detection for ceramic tiles using image processing and morphological techniques

paper_content:

⎯ Quality control in ceramic tile manufacturing is hard, labor intensive and it is performed in a harsh industrial environment with noise, extreme temperature and humidity. It can be divided into color analysis, dimension verification, and surface defect detection, which is the main purpose of our work. Defects detection is still based on the judgment of human operators while most of the other manufacturing activities are automated so, our work is a quality control enhancement by integrating a visual control stage using image processing and morphological operation techniques before the packing operation to improve the homogeneity of batches received by final users. Keywords⎯ Quality control, Defects detection, Visual control, Image processing, Morphological operation.
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paper_title: Local Contrast Enhancement using Local Standard Deviation

paper_content:

For a detailed visibility of an image, since only the global enhancement is not sufficient, local contrast enhancement plays a great role. One of the successful locally adaptive image contrast enhancement methods is by using Local Standard Deviation (LSD). The contrast enhancement using LSD is successfully used in many applications like in medical images, real time images, surveillance applications and many others. These types of applications employ a contrast factor which is divided by LSD. For such type of methods, limitation occurs when the LSD becomes zero. This limitation due to divide by zero can be overcome by adjusting the LSD to a modified value.
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paper_title: A Global-Local Contrast based Image Enhancement Technique based on Local Standard Deviation

paper_content:

Contrast Enhancement plays an important role in digital image processing. It enables the human and automated perceiver to find out the less obvious information in the original image. Global Contrast Enhancement techniques are used in a wide range of applications to enhance the quality of an image. Local contrast enhancement also plays an important role for a detailed visibility of an image. In this paper we tried to enhance the contrast of an image using Global enhancement technique complemented with local image contrast enhancement methods. This paper proposes a method to obtain good contrast image based on its local and global information and by changing the gray values of pixels according to the objective gray scaled image. General Terms Image Processing.
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paper_title: Research and implementation of image enhancement algorithm based on local mean and standard deviation

paper_content:

Aiming at a algorithm it uses Local Mean (LM) and Local Standard Deviation (LSD), this paper designs a new Intelligence algorithm it also uses Local Mean (LM) and Local Standard Deviation (LSD). In a limited local means and standard method, the original local enhancement algorithm was improved, it enhances the area where has low LM and LSD and preserves the other area. Experimental results show that the improved algorithm can achieve the effect of the original algorithm, and for image to further process provide the expected results.
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paper_title: A COMPREHENSIVE METHOD FOR IMAGE CONTRAST ENHANCEMENT BASED ON GLOBAL -LOCAL CONTRAST AND LOCAL STANDARD DEVIATION

paper_content:

Image contrast enhancement is used to increase the detailed visibility of an image by performing some transformation on input image. Global contrast enhancement and local contrast enhancement both are the very popular method for contrast enhancement. But these two methods are also not sufficient in order to enhance an image when image has some constant area and it is not possible to perform any kind of transformation to neighbor hooding pixels of the central pixel. Due to that some information may be lost. This paper proposed a method to remove the divided by zero condition, arises due to the local standard deviation of that constant area in order to enhance an image. This method enhances an image without any loss in detailed visibility of an image.
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paper_title: A color image segmentation algorithm based on region growing

paper_content:

Image segmentation is a classic subject in the field of image processing and also is a hotspot and focus of image processing techniques. With the improvement of computer processing capabilities and the increased application of color image, the color image segmentation are more and more concerned by the researchers. Color image segmentation methods can be seen as an extension of the gray image segmentation method in the color images, but many of the original gray image segmentation methods can not be directly applied to color images. This requires to improve the method of original gray image segmentation method according to the color image which have the feature of rich information or research a new image segmentation method it specially used in color image segmentation. This article proposes a color image segmentation method of automatic seed region growing on basis of the region with the combination of the watershed algorithm with seed region growing algorithm which based on the traditional seed region growing algorithm.
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paper_title: A review on automatic image annotation techniques

paper_content:

Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and image semantics understandable by humans. As a result, research in this area has shifted to bridge the semantic gap between low level image features and high level semantics. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques. In this paper, we focus on this latest development in image retrieval and provide a comprehensive survey on automatic image annotation. We analyse key aspects of the various AIA methods, including both feature extraction and semantic learning methods. Major methods are discussed and illustrated in details. We report our findings and provide future research directions in the AIA area in the conclusions
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paper_title: A Hierarchical Approach to Color Image Segmentation Using Homogeneity

paper_content:

In this paper, a novel hierarchical approach to color image segmentation is studied. We extend the general idea of a histogram to the homogeneity domain. In the first phase of the segmentation, uniform regions are identified via multilevel thresholding on a homogeneity histogram. While we process the homogeneity histogram, both local and global information is taken into consideration. This is particularly helpful in taking care of small objects and local variation of color images. An efficient peak-finding algorithm is employed to identify the most significant peaks of the histogram. In the second phase, we perform histogram analysis on the color feature hue for each uniform region obtained in the first phase. We successfully remove about 99.7% singularity off the original images by redefining the hue values for the unstable points according to the local information. After the hierarchical segmentation is performed, a region merging process is employed to avoid over-segmentation. CIE(L*a*b*) color space is used to measure the color difference. Experimental results have demonstrated the effectiveness and superiority of the proposed method after an extensive set of color images was tested.
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paper_title: Color image segmentation based on homogram thresholding and region merging

paper_content:

In this paper, a color image segmentation approach based on homogram thresholding and region merging is presented. The homogram considers both the occurrence of the gray levels and the neighboring homogeneity value among pixels. Therefore, it employs both the local and global information. Fuzzy entropy is utilized as a tool to perform homogram analysis for finding all major homogeneous regions at the first stage. Then region merging process is carried out based on color similarity among these regions to avoid oversegmentation. The proposed homogram-based approach (HOB) is compared with the histogram-based approach (HIB). The experimental results demonstrate that the HOB can find homogeneous regions more effectively than HIB does, and can solve the problem of discriminating shading in color images to some extent.
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paper_title: A review on automatic image annotation techniques

paper_content:

Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and image semantics understandable by humans. As a result, research in this area has shifted to bridge the semantic gap between low level image features and high level semantics. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques. In this paper, we focus on this latest development in image retrieval and provide a comprehensive survey on automatic image annotation. We analyse key aspects of the various AIA methods, including both feature extraction and semantic learning methods. Major methods are discussed and illustrated in details. We report our findings and provide future research directions in the AIA area in the conclusions
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paper_title: Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm

paper_content:

We propose a methodology that incorporates k-means and improved watershed segmentation algorithm for medical image segmentation. The use of the conventional watershed algorithm for medical image analysis is widespread because of its advantages, such as always being able to produce a complete division of the image. However, its drawbacks include over-segmentation and sensitivity to false edges. We address the drawbacks of the conventional watershed algorithm when it is applied to medical images by using k-means clustering to produce a primary segmentation of the image before we apply our improved watershed segmentation algorithm to it. The k-means clustering is an unsupervised learning algorithm, while the improved watershed segmentation algorithm makes use of automated thresholding on the gradient magnitude map and post-segmentation merging on the initial partitions to reduce the number of false edges and over-segmentation. By comparing the number of partitions in the segmentation maps of 50 images, we showed that our proposed methodology produced segmentation maps which have 92% fewer partitions than the segmentation maps produced by the conventional watershed algorithm
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paper_title: Color image segmentation based on homogram thresholding and region merging

paper_content:

In this paper, a color image segmentation approach based on homogram thresholding and region merging is presented. The homogram considers both the occurrence of the gray levels and the neighboring homogeneity value among pixels. Therefore, it employs both the local and global information. Fuzzy entropy is utilized as a tool to perform homogram analysis for finding all major homogeneous regions at the first stage. Then region merging process is carried out based on color similarity among these regions to avoid oversegmentation. The proposed homogram-based approach (HOB) is compared with the histogram-based approach (HIB). The experimental results demonstrate that the HOB can find homogeneous regions more effectively than HIB does, and can solve the problem of discriminating shading in color images to some extent.
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paper_title: Improved K-Means Clustering Algorithm

paper_content:

K-means algorithm is widely used in spatial clustering. It takes the mean value of each cluster centroid as the Heuristic information, so it has some disadvantages: sensitive to the initial centroid and instability. The improved clustering algorithm referred to the best clustering centriod which is searched during the optimization of clustering centroid. That increased the searching probability around the best centroid and improved the stability of the algorithm. The experiment on two groups of representative dataset proved that the improved K-means algorithm performs better in global searching and is less sensitive to the initial centroid.
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paper_title: Two-dimensional Otsu's thresholding segmentation method based on grid box filter

paper_content:

Abstract Otsu algorithm, an automatic thresholding method, is widely used in classic image segmentation applications. In this paper, a novel two-dimensional (2D) Otsu thresholding algorithm based on local grid box filter is proposed. In our method, firstly by utilizing the coarse-to-fine idea, the 2D histogram is divided into regions by grid technique, and each region is used as a point to form a new 2D histogram, to which 2D Otsu thresholding algorithm and an improved particle swarm optimization (PSO) algorithm are applied to get the region number of the new 2D histogram threshold. Then on the result region, the mean of the 2D histogram is computed base on box filter, and the two algorithms are applied again to obtain the final threshold for the original image. Experimental results on real data show that the proposed algorithm gets better segmentation results than the traditional recursion Otsu algorithm. It significantly reduces the time of segmentation process and simultaneously has the higher segmentation accuracy.
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paper_title: Applications of Image Recognition for Real-Time Water Level and Surface Velocity

paper_content:

In this paper, we present two types of the real-time water monitoring system using the image processing technology, the water level recognition and the surface velocity recognition. According to the bridge failure investigation, floods in the river often pose potential risk to bridges, and scouring could undermine the pier foundation and cause the structures to collapse. It is very important to develop monitoring techniques for bridge safety in the field. In this study, we installed two high-resolution cameras on the in-situ bridge site to get the real-time water level and surface velocity image. For the water level recognition, we use the image processing techniques of the image binarization, character recognition, and water line detection. For the surface velocity recognition, the proposed system apply the PIV(Particle Image Velocimetry, PIV) method to obtain the recognition of the water surface velocity by the cross correlation analysis. Finally, the proposed systems are used to record and measure the variations of the water level and surface velocity for a period of three days. The good results show that the proposed systems have potential to provide real-time information of water level and surface velocity during flood periods.
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paper_title: Otsu's method for clip limiting histograms for contrast enhancement of digital mammograms

paper_content:

This paper presents a novel Otsu based clipping technique for Contrast — Limited Adaptive Histogram Equalization (OC-CLAHE) algorithm for enhancing the contrast of digital mammograms. The algorithm is proposed because the existing CLAHE technique limits the amplification of contrast by clipping the histogram at a predefined value. This clip-limit is crisp and invariant to image data. This causes all the pixels inside the window region of the image to be equally affected. The proposed Otsu based clipping algorithm automates the selection of clip-limit which makes it flexible and variant to image data. Measures of Contrast Improvement Index (CII) and Discrete Entropy (DE) are used to evaluate contrast enhancement. Experimental results show that the proposed OC-CLAHE algorithm produces better contrast enhancement than several state-of-art algorithms.
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paper_title: A new image segmentation method based on Otsu method and ant colony algorithm

paper_content:

Otsu method and ant colony algorithm are all traditional algorithms. The two algorithms can complement each other. The combination of two algorithms will improve image segmentation and speed up segmentation's speed. Tests proved new hybrid algorithm is more effective in image segmentation.
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paper_title: Fast HEVC intra coding algorithm based on Otsu's method and gradient

paper_content:

High Efficiency Video Coding (HEVC), the newest video coding standard established, aims to significantly improve the compression performance compared with H.264/AVC and other existing video coding standards. In the intra coding of HEVC, this improvement is achieved by adopting the coding tree unit (CTU) structure and providing up to 35 prediction modes for each prediction unit (PU). However, the computational complexity is greatly increased due to these new techniques. In this paper, we propose a fast coding unit (CU) size decision and PU mode decision algorithm for HEVC intra coding to reduce the time consumption. Since CU sizes are highly correlated to the texture detail and variance information of the largest coding unit (LCU), we introduce the Otsu's method to measure the texture complexity of each LCU and skip some CU depth levels. Meanwhile, as the prediction modes always correspond to gradient directions, we apply improved Sobel masks to measure gradient directions of PUs and reduce some candidate intra modes. Experimental results show that our proposed algorithm can save 33% time consumption on average with negligible loss of coding efficiency.
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paper_title: Image Segmentation Based on 2D Otsu Method with Histogram Analysis

paper_content:

Image segmentation plays an important role in image analysis and computer vision system. Among all segmentation techniques, the automatic thresholding methods are widely used because of their advantages of simple implement and time saving. Otsu method is one of thresholding methods and frequently used in various fields. Two-dimensional (2D) Otsu method behaves well in segmenting images of low signal-to-noise ratio than one-dimensional (1D). But it gives satisfactory results only when the numbers of pixels in each class are close to each other. Otherwise, it gives the improper results. In this paper, 2D histogram projection is used to correct the Otsu threshold. The 1D histograms are acquired by 2D histogram projection in x and y axes and a fast algorithm for searching the extrema of the projected histogram is proposed based on the wavelet transform in this paper. Experimental results show that the proposed method performs better than the traditional Otsu method for our renal biopsy samples.
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paper_title: Enhancement of iris boundary detection based on Otsu method

paper_content:

The aim of this paper is to propose a new method for pupil boundary detection. Iris recognition system is considered as one of the most dependable identification system. Anyhow, these systems are still not widespread due to many weaknesses, for example, the processing time is still not instant that lead to ineffectual identification method. One of the major tasks in iris recognition systems is accurate detection of the iris. However, pupil boundary detection method depended on the circular or elliptic shape of the iris which could lead to inaccurate results of boundary detection. Thus, this can affect on the next step of iris recognition systems. Hence this research proposed thresholding the eye image using Otsu's methods to remove the pupil. Our method was tested on all CASIA V3.0 eye images and 100% accuracy of iris boundary detection is attained with acceptable computational time that suitable to be implemented for real time processing.
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paper_title: A Review on Otsu Image Segmentation Algorithm

paper_content:

Image segmentation is the fundamental approach of digital image processing. Among all the segmentation methods, Otsu method is one of the most successful methods for image thresholding because of its simple calculation. Otsu is an automatic threshold selection region based segmentation method. This paper studies various Otsu algorithms.
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paper_title: Imaging of a Moving Target With Rotating Parts Based on the Hough Transform

paper_content:

The rotation of structures in a target introduces additional frequency modulations on the returned signals and also generates sidebands about the center Doppler frequency of the target. In other words, the body image will be contaminated due to the interference from the rotating parts. In this paper, an imaging method for moving targets with rotating parts is presented. The method is simple to implement and is based on the Hough transform (HT), which is widely used in image processing. Using the standard HT and an extended HT, we put forward a separation method by detecting the straight lines and the sinusoids on the spectrogram, respectively. A computer simulation is given to illustrate the effectiveness of the proposed method.
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paper_title: Applications of Image Recognition for Real-Time Water Level and Surface Velocity

paper_content:

In this paper, we present two types of the real-time water monitoring system using the image processing technology, the water level recognition and the surface velocity recognition. According to the bridge failure investigation, floods in the river often pose potential risk to bridges, and scouring could undermine the pier foundation and cause the structures to collapse. It is very important to develop monitoring techniques for bridge safety in the field. In this study, we installed two high-resolution cameras on the in-situ bridge site to get the real-time water level and surface velocity image. For the water level recognition, we use the image processing techniques of the image binarization, character recognition, and water line detection. For the surface velocity recognition, the proposed system apply the PIV(Particle Image Velocimetry, PIV) method to obtain the recognition of the water surface velocity by the cross correlation analysis. Finally, the proposed systems are used to record and measure the variations of the water level and surface velocity for a period of three days. The good results show that the proposed systems have potential to provide real-time information of water level and surface velocity during flood periods.
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paper_title: Feature extraction algorithm for fill level and cap inspection in bottling machine

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Automated Visual Inspection Systems (AVIS) have a strong ability to improve bottle manufacturing quality control by means of inspecting products automatically instead of through manual inspections. AVIS automatically tends to make a suitable decision in process and results classification according to the images of the products via image processing and Artificial Intelligence techniques. Since bottling is one of the most common packaging styles in the food and medical industries, in this paper we will concentrate on the visual inspection of bottles. Checking the quality of the cap closure and over-filling/under-filling checks for the level of the liquid in the bottle have been investigated to reach an optimized bottle product. Therefore, in this research general hardware and modules for these systems are investigated. Besides, new techniques of bottle inspection are reviewed along with presenting previous work of other researchers. Subsequently we will propose a feature extraction algorithm to inspect cap closure and level of the liquid in the bottle together, in the same system. According to the new proposed method, our system classifies three situations for cap condition and three situations for the condition of the level of the liquid. As a result the system has investigated 9 situations. The algorithm of the system will accept its system when the liquid level is in the correct position and the cap is in the normal condition. Other situations will be rejected. The proper algorithm which is proposed here using bottle visual inspection techniques leads our system to reach an optimized liquid level with a high quality of the cap closure.
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paper_title: A classified and comparative study of edge detection algorithms

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Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. This paper introduces a new classification of most important and commonly used edge detection algorithms, namely ISEF, Canny, Marr-Hildreth, Sobel, Kirsch, Lapla1 and Lapla2. Five categories are included in our classification, and then advantages and disadvantages of some available algorithms within this category are discussed. A representative group containing the above seven algorithms are the implemented in C++ and compared subjectively, using 30 images out of 100 images. Two sets of images resulting from the application of those algorithms are then presented. It is shown that under noisy conditions, ISEF, Canny, Marr-Hildreth, Kirsch, Sobel, Lapla2, Lapla1 exhibit better performance, respectively.
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paper_title: Sobel Edge Detection Algorithm

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Edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. Sobel which is a popular edge detection algorithm is considered in this work. There exists a function, edge.m which is in the image toolbox. In the edge function, the Sobel method uses the derivative approximation to find edges. Therefore, it returns edges at those points where the gradient of the considered image is maximum. The Sobel operator performs a 2-D spatial gradient measurement on images. It uses a pair of horizontal and vertical gradient matrices whose dimensions are 3×3 for edge detection operations. It will also demonstrate how to build a Sobel detector function of 5 ×5 dimension in matlab to find edges.
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paper_title: An improved Sobel edge detection

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This paper proposes a method which combines Sobel edge detection operator and soft-threshold wavelet de-noising to do edge detection on images which include White Gaussian noises. In recent years, a lot of edge detection methods are proposed. The commonly used methods which combine mean de-noising and Sobel operator or median filtering and Sobel operator can not remove salt and pepper noise very well. In this paper, we firstly use soft-threshold wavelet to remove noise, then use Sobel edge detection operator to do edge detection on the image. This method is mainly used on the images which includes White Gaussian noises. Through the pictures obtained by the experiment, we can see very clearly that, compared to the traditional edge detection methods, the method proposed in this paper has a more obvious effect on edge detection.
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paper_title: Edge detection of images based on improved Sobel operator and genetic algorithms

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Edge detection of images is a classical problem in computer vision and image processing. The key of edge detection is the choice of threshold; the choice of threshold directly determines the results of edge detection. How to automatically determine an optimal threshold is one of difficult points of edge detection. In this paper, Sobel edge detection operator and its improved algorithm are first discussed in term of optimal thresholding. Then based on Genetic Algorithms and improved Sobel operator, a new automatic threshold algorithm for images processing is proposed. Finally, the edge detection experiments of two real images are conducted by means of two algorithms. The comparative experiment results show that the new algorithm of automatic threshold is very effective. The results are also better than the classical Otsu methods.
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paper_title: Analysis of Edge-Detection Techniques for Crack Identification in Bridges

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Bridge monitoring and maintenance is an expensive yet essential task in maintaining a safe national transportation infrastructure. Traditional monitoring methods use visual inspection of bridges on a regular basis and often require inspectors to travel to the bridge of concern and determine the deterioration level of the bridge. Automation of this process may result in great monetary savings and can lead to more frequent inspection cycles. One aspect of this automation is the detection of cracks and deterioration of a bridge. This paper provides a comparison of the effectiveness of four crack-detection techniques: fast Haar transform (FHT), fast Fourier transform, Sobel, and Canny. These imaging edge-detection algorithms were implemented in MatLab and simulated using a sample of 50 concrete bridge images (25 with cracks and 25 without). The results show that the FHT was significantly more reliable than the other three edge-detection techniques in identifying cracks.
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paper_title: An Improved CANNY Edge Detection Algorithm

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CANNY arithmetic operator has been proved to have good detective effect in the common usage of edge detection. However, CANNY operator also has certain deficiencies. Based on the analysis of the traditional CANNY algorithm, an improved canny algorithm is proposed in this paper. In the algorithm, self-adaptive filter is used to replace the Gaussian filter, morphological thinning is adopted to thin the edge and morphological operator is used to achieved the refining treatment of edge points detection and the single pixel level edge. The results of experiment show the improved CANNY algorithm is reasonable.
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paper_title: Selecting the Most Favourable Edge Detection Technique for Liquid Level Inspection in Bottles

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In this paper few edge detection techniques viz. Optimal & Template Based are compared to inspect the over and under fill liquid level of bottle in machine vision system. The text represents the steps and approaches for the inspection of over and under filled level in the bottle which would not only be helpful for quality inspection but in precised time too using different edge detection techniques. The results of Shen-Castan’s ISEF optimal edge detection algorithm were found to be much better than the traditional edge detection methods like Roberts, Prewitt, Sobel, Marr- Hilderth LoG algorithm and Canny algorithm.
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paper_title: Real-time canny edge detection parallel implementation for FPGAs

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Edge detection is one of the most fundamental algorithms in digital image processing. The Canny edge detector is the most implemented edge detection algorithm because of its ability to detect edges even in images that are intensely contaminated by noise. However, this is a time consuming algorithm and therefore its implementations are difficult to reach real time response speeds. Especially nowadays where the demand for high resolution image processing is constantly increasing, the need for fast and efficient edge detector implementations is ever so present. A new parallel Canny edge detector FPGA implementation is proposed in this paper to answer this demand. This design takes advantage of 4-pixel parallel computations to achieve high throughput without increasing the on-chip memory demands. Synthesis and simulation results are presented to prove the design's efficiency and high frames per second rate.
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paper_title: Fault diagnosis of photovoltaic modules through image processing and Canny edge detection on field thermographic measurements

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Today, conventional condition monitoring of installed, operating photovoltaic (PV) modules is mainly based on electrical measurements and performance evaluation. However, such practices exhibit restricted fault-detection ability. This study proposes the use of standard thermal image processing and the Canny edge detection operator as diagnostic tools for module-related faults that lead to hot-spot heating effects. The intended techniques were applied on thermal images of defective PV modules, from several field infrared thermographic measurements conducted during this study. The whole approach provided promising results with the detection of hot-spot formations that were diagnosed to specific defective cells in each inspected module. These evolving hot spots lead to abnormally low performance of the PV modules, a fact that is also validated by the manufacturer's standard electrical tests.
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paper_title: Imaging of a Moving Target With Rotating Parts Based on the Hough Transform

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The rotation of structures in a target introduces additional frequency modulations on the returned signals and also generates sidebands about the center Doppler frequency of the target. In other words, the body image will be contaminated due to the interference from the rotating parts. In this paper, an imaging method for moving targets with rotating parts is presented. The method is simple to implement and is based on the Hough transform (HT), which is widely used in image processing. Using the standard HT and an extended HT, we put forward a separation method by detecting the straight lines and the sinusoids on the spectrogram, respectively. A computer simulation is given to illustrate the effectiveness of the proposed method.
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paper_title: A HOUGH TRANSFORM GLOBAL PROBABILISTIC APPROACH TO MULTIPLE SUBJECT DIFFUSION MRI TRACTOGRAPHY By

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A global probabilistic fiber tracking approach based on the voting process provided by the Hough transform is introduced in this work. The proposed framework tests candidate 3D curves in the volume, assigning to each one a score computed from the diffusion images, and then selects the curves with the highest scores as the potential anatomical connections. The algorithm avoids local minima by performing an exhaustive search at the desired resolution. The technique is easily extended to multiple subjects, considering a single representative volume where the registered high-angular resolution diffusion images (HARDI) from all the subjects are non-linearly combined, thereby obtaining population-representative tracts. The tractography algorithm is run only once for the multiple subjects, and no tract alignment is necessary. We present experimental results on HARDI volumes, ranging from simulated and 1.5T physical phantoms to 7T and 4T human brain and 7T monkey brain datasets.
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paper_title: On detection of multiple object instances using hough transforms

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To detect multiple objects of interest, the methods based on Hough transform use non-maxima supression or mode seeking in order to locate and to distinguish peaks in Hough images. Such postprocessing requires tuning of extra parameters and is often fragile, especially when objects of interest tend to be closely located. In the paper, we develop a new probabilistic framework that is in many ways related to Hough transform, sharing its simplicity and wide applicability. At the same time, the framework bypasses the problem of multiple peaks identification in Hough images, and permits detection of multiple objects without invoking nonmaximum suppression heuristics. As a result, the experiments demonstrate 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: Real-time line detection through an improved Hough transform voting scheme

paper_content:

The Hough transform (HT) is a popular tool for line detection due to its robustness to noise and missing data. However, the computational cost associated to its voting scheme has prevented software implementations to achieve real-time performance, except for very small images. Many dedicated hardware designs have been proposed, but such architectures restrict the image sizes they can handle. We present an improved voting scheme for the HT that allows a software implementation to achieve real-time performance even on relatively large images. Our approach operates on clusters of approximately collinear pixels. For each cluster, votes are cast using an oriented elliptical-Gaussian kernel that models the uncertainty associated with the best-fitting line with respect to the corresponding cluster. The proposed approach not only significantly improves the performance of the voting scheme, but also produces a much cleaner voting map and makes the transform more robust to the detection of spurious lines.
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paper_title: A Hough transform-based voting framework for action recognition

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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: A survey of Hough Transform

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In 1962 Hough earned the patent for a method 1], popularly called Hough Transform (HT) that efficiently identifies lines in images. It is an important tool even after the golden jubilee year of existence, as evidenced by more than 2500 research papers dealing with its variants, generalizations, properties and applications in diverse fields. The current paper is a survey of HT and its variants, their limitations and the modifications made to overcome them, the implementation issues in software and hardware, and applications in various fields. Our survey, along with more than 200 references, will help the researchers and students to get a comprehensive view on HT and guide them in applying it properly to their problems of interest. HighlightsA survey of Hough Transform (HT) has been done.The main variants of HT and its formulations have been described.The hardware and software implementations of HT have been described.HT has many applications in diverse fields. Out of them some applications in some selected fields have been mentioned.
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Title: A REVIEW OF VISION BASED DEFECT DETECTION USING IMAGE PROCESSING TECHNIQUES FOR BEVERAGE MANUFACTURING INDUSTRY

Section 1: INTRODUCTION
Description 1: Introduce the topic of vision-based defect detection in the beverage manufacturing industry, discussing the significance, manual vs. automated inspection, and benefits of automated systems.

Section 2: AUTOMATED QUALITY INSPECTION TECHNIQUES
Description 2: Discuss various automated quality inspection techniques including adaptive thresholding, gray level co-occurrence matrix, morphological operation, and Otsu’s thresholding.

Section 3: Shape Inspection
Description 3: Explain the techniques and studies related to shape inspection in the context of beverage manufacturing, including methodologies like partial erosion-based technique, SHFCM, and SVM.

Section 4: Colour Inspection
Description 4: Describe the various techniques used for color inspection of products, such as region growing technique, FCM with SVM, and K-means clustering.

Section 5: Level Inspection
Description 5: Outline the methods used for level inspection focusing on techniques like ISEF, edge detection algorithms, and Hough transform.

Section 6: DEFECT DETECTION TECHNIQUES
Description 6: Detail the different defect detection techniques used in image processing, including thresholding-based techniques and clustering-based techniques.

Section 7: Shape Defect Detection Techniques
Description 7: Present specific techniques for shape defect detection such as adaptive thresholding, morphological operation, and local standard deviation.

Section 8: Colour Concentration Defect Detection Techniques
Description 8: Discuss color concentration defect detection techniques like K-means clustering and Otsu’s thresholding.

Section 9: Level Defect Detection Technique
Description 9: Elaborate on level defect detection techniques focusing on methods including Sobel edge detection, canny edge detection, and Hough transform.

Section 10: CONCLUSION
Description 10: Summarize the findings and discussions from the paper, emphasizing the effectiveness of various techniques for vision-based defect detection in the beverage manufacturing industry.