Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Abstract This paper rigorously establishes that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available. In this sense, multilayer feedforward networks are a class of universal approximators. <s> BIB001 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> For fast inspection of defects in textile fabric the complexity of calculations has to be reduced, in order to limit the system costs. Additionally algorithms which are suitable for migration into hardware have to be chosen. Therefore in the paper a segmentation algorithm using first order statistics is applied. Preprocessing includes a logarithmic greyscale transformation to obtain insensitivity to illumination changes. Afterwards texture features are extracted by a set of linear filters, which consider local neighbourhood relations. The filtered images are evaluated by histograms being calculated on a window grid. Finally the histograms are classified by a perceptron net trained by backpropagation. An interactive teach-in program is provided to adapt the system to different kinds of textile fabric and appearances of defects. > <s> BIB002 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> In this paper, we evaluate the efficiency and accuracy of a method of detecting fabric defects that have been classified into different categories by a neural network. Four kinds of fabric defects most likely to be found during weaving were learned by the network. Based on the principle of the back-propagation algorithm of learning rule, fabric defects could be detected and classified exactly. The method used for processing image feature extraction is a co-occurrence-based method, by which six feature parameters are obtained. All of them consist of contrast measurements, which involve three spatial displacements (i.e., 1, 12, 16) and four directions (0, 45, 90, 135 degrees) of fabric defects' images used for classification. The results show that fabric defects inspected by means of image recognition in accordance with the artificial neural network agree approximately with initial expectations. <s> BIB003 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Gabor functions, which localize information in both the spatial and the frequency domains, are used as filters for the inspection of common local defects in textile webs. A variety of defects are analyzed in different fabrics and in every case the flaws are finally segmented from the background.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. <s> BIB004 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> A fast system to reveal the presence and type of fabric defects during the weaving process is developed. Since the fabric is similar to a 2-D grid, its defects are clearly observed in the changes in its optical Fourier transform (OFT), which appears stationary while the fabric is moving across the loom. Previous work, based on the statistical parameters of the OFT, showed that the presence of faults can be detected when only global changes in the images are considered. We show that by selecting a small subset of pixels from the image as input to a neural network, fabric defects can not only be detected but also successfully identified. A knowledge-based system could conceivably be constructed to use this information to resolve problems with the loom in real time, without the need for operator intervention. <s> BIB005 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> In this article we introduce our approach to exploit multiscale wavelet methods for texture defect detection. Several wavelet bases and decomposition algorithms are examined in regard of applicability, parameterization and computational costs. The article points out specific problems in localizing texture defects in multiscale wavelet representations. Besides the fast dyadic wavelet transform we demonstrate the application of the translation invariant a trous algorithm on texture samples. Feature extraction methods are proposed and examples of successful defect classification results are shown. <s> BIB006 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> This paper describes a vision-based fabric inspection system that accomplishes on-loom inspection of the fabric under construction with 100% coverage. The inspection system, which offers a scalable, open architecture, can be manufactured at relatively low cost using off-the-shelf components. While synchronized to the motion of the loom, the developed system first acquires very high-quality, vibration-free images of the fabric using either front or backlighting. Then the acquired images are subjected to a novel defect segmentation algorithm, which is based on the concepts of wavelet transform, image fusion and the correlation dimension. The essence of this segmentation algorithm is the localization of those events (i.e., defects) in the input images that disrupt the global homogeneity of the background texture. The efficacy of this algorithm, as well as the overall inspection system, has been tested thoroughly under realistic conditions. The system was used to acquire and to analyze more than 3700 images of fabrics that were constructed with two different types of yarn. In each case, the performance of the system was evaluated as an operator introduced defects from 26 categories into the weaving process. The overall detection rate of the presented approach was found to be 89% with a localization accuracy of 0.2 in. (i.e., the minimum defect size) and a false alarm rate of 2.5%. <s> BIB007 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> In automated visual surface inspection based on statistical pattern recognition, the collection of training material for setting up the classifier may appear to be difficult. Getting a representative set of labeled training samples requires scanning through large amounts of image material by the training personnel, which is an error prone and laborious task. Problems are further caused by the variations of the inspected materials and imaging conditions, especially with color imaging. Approaches based on adaptive defect detection and robust features may appear inapplicable because of losing some faint or large area defects. Adjusting the classifier to adapt to the changed situation may appear difficult because of the inflexibility of the classifiers' implementations. This may lead to impractical often repeated training material collection and classifier retraining cycles. In this paper we propose a non-segmenting defect detection technique combined with a self-organizing map (SOM) based classifier and user interface. The purpose is to avoid the problems with adaptive detection techniques, and to provide an intuitive user interface for classification, helping in training material collection and labelling, and with a possibility of easily adjusting the class boundaries. The approach is illustrated with examples from wood surface inspection.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. <s> BIB008 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> In this paper a pilot system for defect detection and classification of web textile fabric in real-time is presented. The general hardware and software platform, developed for solving this problem, is presented while a powerful novel method for defect detection after multiresolution decomposition of the fabric images is proposed. This method gives good results in the detection of low contrast defects under real industrial conditions, where many types of noise are present. An artificial neural network, trained by a back-propagation algorithm, performs the defect classification in categories. <s> BIB009 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field. <s> BIB010 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> This paper presents an automatic system for steel quality assessment, by measuring textural properties of carbide distributions. In current steel inspection, specially etched and polished steel specimen surfaces are classified manually under a light microscope, by comparisons with a standard chart. This procedure is basically two-dimensional, reflecting the size of the carbide agglomerations and their directional distribution. To capture these textural properties in terms of image fea tures, we first apply a rich set of image-processing operations, including mathematical morphology, multi-channel Gabor filtering, and the computation of texture measures with automatic scale selection in linear scale-space. Then, a feature selector is applied to a 40-dimensional feature space, and a classification scheme is defined, which on a sample set of more than 400 images has classification performance values comparable to those of human metallographers. Finally, a fully automatic inspection system is designed, which actively selects the most salient carbide structure on the specimen surface for subsequent classification. The feasibility of the overall approach for future use in the production process is demonstrated by a prototype system. It is also shown how the presented classification scheme allows for the definition of a new reference chart in terms of quantitative measures. <s> BIB011 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> The problem of automated defect detection in textured mate- rials is investigated. A new algorithm based on multichannel filtering is presented. The texture features are extracted by filtering the acquired image using a filter bank consisting of a number of real Gabor functions, with multiple narrow spatial frequency and orientation channels. For each image, we propose the use of image fusion to multiplex the infor- mation from sixteen different channels obtained in four orientations. Adaptive degrees of thresholding and the associated effect on sensitivity to material impurities are discussed. This algorithm realizes large com- putational savings over the previous approaches and enables high- quality real-time defect detection. The performance of this algorithm has been tested thoroughly on real fabric defects, and experimental results have confirmed the usefulness of the approach. © 2000 Society of Photo- Optical Instrumentation Engineers. (S0091-3286(00)01912-7) <s> BIB012 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> In this paper we present a machine vision system for the automatic inspection of defects in textured surfaces found in industry. The defects to be inspected are those that appear as local anomalies embedded in a homogeneous texture. The proposed method is based on a Gabor filtering scheme that computes the output response of energy from the convolution of a textured image with a specific Gabor filter. The best parameters of a Gabor filter are selected so that the energy of the homogeneous texture is zero, and any unpredictable defeats will generate significantly large energy values. A simple thresholding scheme then follows to discriminate between homogeneous regions and defective regions in the filtered image. This transforms texture differences into detectable filter output. The experiments on structural textures such as leather and sandpaper have shown the effectiveness of the proposed method. <s> BIB013 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Abstract In this paper, an efficient algorithm, which combines concepts from wavelet theory and co-occurrence matrices, is presented for detection of defects encountered in textile images. Detection of defects within the inspected texture is performed first by decomposing the gray level images into sub-bands, then by partitioning the textured image into non-overlapping sub-windows and extracting the co-occurrence features and finally by classifying each sub-window as defective or non-defective with a Mahalanobis distance classifier being trained on defect free samples a priori. The experimental results demonstrating the use of this algorithm for the visual inspection of textile products obtained from the real factory environment are also presented. Experiments show that focusing on a particular band with high discriminatory power improves the detection performance as well as increases the computational efficiency. <s> BIB014 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> The appearance of sawn timber has huge natural variations that a human inspector easily compensates for in his brain when determining the types of defects and the grade of each board. However, for automatic wood inspection systems these variations are a major source of complication. For instance, normal wood grain and knots should be reliably discriminated in all circumstances, but simple thresholding based detection methods used in state-of-the-art inspection systems frequently fail even in this apparently straightforward task. In this paper we compare color and texture features in defect detection from lumber. A non-supervised clustering based approach is used for discriminating defects and sound wood. The solution is simple to train, and supports detecting knots and other defects by using multidimensional feature vectors containing texture and color cues for small non-overlapping regions in the image. A key idea is to employ a Self-Organizing Map (SOM) for discriminating between sound wood and defects. The experiments show that color histogram features together with local binary pattern based texture measures are a very promising method. The non-supervised training approach contributes to low error escape and false alarm rates. <s> BIB015 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> In this document, a new method to detect and to characterize surface defects in mechanical parts is reported. Cellular neural networks are used as tools for the implementation of the stereoscopic vision analysis technique. Suitable applications in microscopic defect analysis (real-time processing), in various fields (e.g. aeronautics applications) are introduced, by means of the reported examples in order to validate the cellular neural networks (CNN) approach. <s> BIB016 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Image classification by a neural-fuzzy system is presented for normal fabrics and eight kinds of fabric defects. This system combines the fuzzification technique with fuzzy logic and a back-propagation learning algorithm with neural networks. Four input features—the ratio of projection lengths in the horizontal and vertical directions, the gray-level mean and standard deviation of the image, and the large number emphasis (LNE) based on the neighboring gray level dependence matrix for the defect area—are selected and their usefulness is justified. The neural network is also implemented and compared with the neural-fuzzy system. The results demonstrate that the neural-fuzzy system is superior to the neural network in classification ability. <s> BIB017 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Investigates two methods for the detection of defects on textured surfaces using neural networks and support vector machines. Every pixel from the inspection image is characterized by a feature vector, which serves as a local measure of homogeneity of texture. The feature vectors from the gray-level arrangement of neighboring pixels are transformed to eigenspace using Principal Component Analysis (PCA). The transformed features from a predetermined set of training images are used to train the classifier. The trained classifier is used to classes every pixel from inspection image into two-class, i.e. with- or without-defect. The experimental results on real fabric defects show that the proposed scheme can successfully segment the defects from the inspection images. <s> BIB018 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Abstract The task of detecting flaws in woven textiles can be formulated as the problem of segmenting a “known” non-defective texture from an “unknown” defective texture. In order to discriminate defective texture pixels from non-defective texture pixels, optimal 2-D Gabor filters are designed such that, when applied to non-defective texture, the filter response maximises a Fisher cost function. A pixel of potentially flawed texture is classified as defective or non-defective based on the Gabor filter response at that pixel. The results of this optimised Gabor filter classification scheme are presented for 35 different flawed homogeneous textures. These results exhibit accurate flaw detection with low false alarm rate. Potentially, our novel optimised Gabor filter method could be applied to the more complicated problem of detecting flaws in jacquard textiles. This second and more difficult problem is also discussed, along with some preliminary results. <s> BIB019 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> The appearance of sawn timber has huge natural variations that the human inspector easily compensates for mentally when determining the types of defects and the grade of each board. However, for automatic wood inspection systems these variations are a major source for complication. This makes it difficult to use textbook methodologies for visual inspection. These methodologies generally aim at systems that are trained in a supervised manner with samples of defects and good material, but selecting and labeling the samples is an error-prone process that limits the accuracy that can be achieved. We present a non-supervised clustering-based approach for detecting and recognizing defects in lumber boards. A key idea is to employ a self-organizing map (SOM) for discriminating between sound wood and defects. Human involvement needed for training is minimal. The approach has been tested with color images of lumber boards, and the achieved false detection and error escape rates are low. The approach also provides a self-intuitive visual user interface. <s> BIB020 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> In this research, an image system is used as a tool for dynamic inspection of fabrics, and the inspection sample is a piece of plain white fabric. The four defects are holes, oil stains, warp-lacking, and weft-lacking. The image treatment employs a high-resolution linear scan digital camera. Fabric images are acquired first, then the images are transferred to a computer for analysis. Finally, the data are adopted as input data for a neural network, which is obtained from readings after treating the images. In this system, there are three feedforward networks, an input layer, one hidden layer, and an output layer. Because it has the ability to cope with the nonlinear regression property, this method can reinforce the effects of image identification. <s> BIB021 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Appearance is an important property of fabrics. Traditionally, fabric ins on is done by workers, but it is so subjective that accuracy is a problem because inspectors tire easily and suffer eyestrain. To overcome these disadvantages, an image system is used as the detecting tool in this paper. A plain white fabric is adopted as the sample, and the distinguishing defects are holes, oil stains, warp-lacking, and weft-lacking. An area scan camera with 512 × 512 resolution is used in the scheme, and a grabbed image is transmitted to a computer for filtering and thresholding. The corresponding image data are then used in the back-propagation neural network as input. There are three input units, maximum length, maximum width, and gray level of fabric defects, in the input layer of the neural network. This system is successfully employed to determine nonlinear properties and enhance recognition. <s> BIB022 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> A new eigenfilter-based novelty detection approach to find abnormalities in random textures is presented. The proposed algorithm reconstructs a given texture twice using a subset of its own eigenfilter bank and a subset of a reference (template) eigenfilter bank, and measures the reconstruction error as the level of novelty. We then present an improved reconstruction generated by structurally matched eigenfilters through rotation, negation, and mirroring. We apply the method to the detection of defects in textured ceramic tiles. The method is over 90% accurate, and is fast and amenable to implementation on a production line. <s> BIB023 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Over the last few years research has been oriented toward developing a machine vision system for locating and identifying, automatically, defects on rails. Rail defects exhibit different properties and are divided into various categories related to the type and position of flaws on the rail. Several kinds of interrelated factors cause rail defects such as type of rail, construction conditions, and speed and/or frequency of trains using the rail. The aim of this paper is to present an experimental comparison among three filtering approaches, based on texture analysis of rail surfaces, to detect the presence/absence of a particular class of surface detects: corrugation. <s> BIB024 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> This paper proposes an approach that detects surface defects with three-dimensional characteristics on scale-covered steel blocks. The surface reflection properties of the flawless surface changes strongly. Light sectioning is used to acquire the surface range data of the steel block. These sections are arbitrarily located within a range of a few millimeters due to vibrations of the steel block on the conveyor. After the recovery of the depth map, segments of the surface are classified according to a set of extracted features by means of Bayesian network classifiers. For establishing the structure of the Bayesian network, a floating search algorithm is applied, which achieves a good tradeoff between classification performance and computational efficiency for structure learning. This search algorithm enables conditional exclusions of previously added attributes and/or arcs from the network. The experiments show that the selective unrestricted Bayesian network classifier outperforms the naïve Bayes and the tree-augmented naïve Bayes decision rules concerning the classification rate. More than 98% of the surface segments have been classified correctly. <s> BIB025 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> In this paper we approach the problem of fast surface grading of flat pieces decorated with random patterns. The proposed method is based on the use of global statistics of color computed in the CIE Lab space. Two other fast methods based on color histograms [1] and Centile-LBP features [8] are introduced for comparison purposes. We used CIE Lab in order to provide accuracy and perceptual approach in color difference computation. Experiments with RGB were also carried out to study CIE Lab reliability. The ground truth was provided through an image database of ceramic tiles. Nevertheless, the approach is suitable to be extended to other random decorated surfaces like marble, granite, wood or textile stuff. The experiments make us to conclude that a simple collection of global statistics of color in the CIE Lab space is powerful enough to well discriminate surface grades. The average success surpasses 95% in most of the tests, improving literature methods and achieving factory compliance. <s> BIB026 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> This paper presents a new approach to the question of surface grading based on soft colour-texture descriptors and well known classifiers. These descriptors come from global image statistics computed in perceptually uniform colour spaces (CIE Lab or CIE Luv). The method has been extracted and validated using a statistical procedure based on experimental design and logistic regression. The method is not a new theoretical contribution, but we have found and demonstrate that a simple set of global statistics softly describing colour and texture properties, together with well-known classifiers, are powerful enough to meet stringent factory requirements for real-time and performance. These requirements are on-line inspection capability and 95% surface grading accuracy. The approach is also compared with two other methods in the surface grading literature; colour histograms [1] and centile-LBP [8]. This paper is an extension and in-depth development of ideas reported in a previous work [11]. <s> BIB027 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> We present an approach to detecting and localizing defects in random color textures which requires only a few defect free samples for unsupervised training. It is assumed that each image is generated by a superposition of various-size image patches with added variations at each pixel position. These image patches and their corresponding variances are referred to here as textural exemplars or texems. Mixture models are applied to obtain the texems using multiscale analysis to reduce the computational costs. Novelty detection on color texture surfaces is performed by examining the same-source similarity based on the data likelihood in multiscale, followed by logical processes to combine the defect candidates to localize defects. The proposed method is compared against a Gabor filter bank-based novelty detection method. Also, we compare different texem generalization schemes for defect detection in terms of accuracy and efficiency. <s> BIB028 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Soil release is an important attribute of fabrics that impacts their pricing on the marketplace. Currently, stain release is evaluated manually and hence its assessment tends to be subjective. This paper introduces a novel approach based on image analysis for objective evaluation of stain release in fabrics. This system has the capability to accurately detect, localize and grade stains with minimal human intervention. The grades assigned by the system are based on a digitized version of the AATCC soil release replica. The stain detection performance of the system was validated on a large dataset of 360 stain images. The system produced excellent results with a sensitivity (true positive rate) of around 93 percent and specificity (1 — false positive rate) of around 95 percent. Further, a correlation study and analysis of variance were performed using the system assigned grades and the technician assigned grades, and these initial results are very promising. <s> BIB029 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> 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. <s> BIB030 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> This work investigates the problem of automatic and robust fabric defect detection and classification which are more essential and important in assuring the fabric quality. Two characteristics of this work are: first, a new scheme combining Gabor filters and Gaussian mixture model (GMM) is proposed for fabric defect detection and classification. In detection, the foreground mask and texture features are extracted using Gabor filters. In classification, a GMM based classifier is trained and assigns each foreground pixel to known classes. The second characteristic of this work is the test data is actually collected from Qinfeng textile factory, China, including nine different fabric defects with more than 1000 samples. All the evaluation of our method is based on these actual fabric images and the experimental results show the proposed algorithm achieved satisfied performance. <s> BIB031 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> This paper proposes an approach of textile flaw classification based on histogram and BP neural network. The common two types of textile flaws, namely oil stain and hole, can be extracted and classified. The method can detect flaws for two types of texture fabrics: statistical textures with isotropic patterns and structural textures with oriented patterns. For the extraction of flaw features, histograms of "hole" and "oil stain" are computed as the input of BP neural network. Some samples are selected for testing, the results show that the method can effectively detect defects and classify the types of defects with high recognition correct rate. <s> BIB032 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> In this paper, a new approach for fabric defect classification using radial basis function (RBF) network improved by Gaussian mixture model (GMM) is investigated. First, the gray level arrangement in the neighborhood of each pixel is extracted as the feature. This raw feature is subject to principal component analysis (PCA) which adopts the between class scatter matrix as the generation matrix to eliminate the variance within the same class. Second, the RBF network with Gaussian kernel is used as the classifier because of the nonlinear discrimination ability and support for multi-output. To train the classifier, GMM is introduced to cluster the feature set and precisely estimate the parameter in Gaussian RBF, in which each cluster strictly conforms to a multi-variance Gaussian distribution. Thus the parameter of each kernel function in RBF network can be acquired from a corresponding cluster. The proposed algorithm is experimented on fabric defect images with nine classes and achieves superior performance, which proves its utility in practice. <s> BIB033 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Road surface conditions are continuously degrading due to meteorological conditions, ground movements and traffic that leads to the formation of defects, such as grabbing, holes, and cracks. This article presents a method to automatically distinguish images of road surfaces with defects from road surfaces without defects. This method, based on supervised learning, is generic and may be applied to all type of defects present in the images. They typically present strong textural information with patterns that show fluctuations at small scales and some uniformity at larger scales. The textural information is described by applying a large set of linear and nonlinear filters. To select the most pertinent ones for the current application, a supervised learning based on AdaBoost is performed. The whole process is tested both on a textural recognition task based on the VisTex image database and on road images collected by a dedicated road imaging system. A comparison with a recent crack detection algorithm from Oliveira and Correia demonstrates the proposed method's efficiency. <s> BIB034 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> In this study, a machine vision system is developed to achieve fabric inspection and defect classification processes automatically. The system consists of an image acquisition hardware and an image processing software. A simple and portable system was designed so that it can be adapted easily to all types of the fabric inspection machines. The software of the system consists of defect detection and classification algorithms. The defect detection algorithm is based on wavelet transform, double thresholding binarization, and morphological operations. It was applied real time via a user interface prepared by using MATLAB® program. The defect classification approach is based on gray level co-occurrence matrix and feed forward neural network. Five commonly occurring defect types, warp lacking, weft lacking, soiled yarn hole, and yarn flow, were detected and classified. The defective and defect-free regions of the fabric were detected with an accuracy of 93.4% and the defects are classified with 96.3% accuracy ... <s> BIB035 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Abstract This study proposed an automatic optical inspection (AOI) system for detection of thin-film transistor (TFT) array defects. gray level co-occurrence matrix (GLCM) and MATLAB regionprops function were used to calculate 53 TFT array defect features, which were inputted into the neural network to train the defect classifier. The images to be inspected were compared with a standard image first, in order to judge whether the TFT array samples have defects. For defective images of a TFT array, the proposed defect classifier can successfully recognize five kinds of defects in the process. According to the experimental results, the defect recognition rate of proposed system is verified to be 83.3%, which can replace manual inspection and reduce the risks of false inspections due to long duration manual work. Moreover, the proposed AOI system can improve testing efficiency and reduce manufacturing costs. <s> BIB036 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Defect detection has been a focal point in fabric inspection research and remains challenging. In this paper, a novel method for fabric defect detection is presented. In the proposed algorithm, only defect-free fabric images are used to build the over-complete basis set via sparse coding. Compared to traditional defect detection methods via sparse coding, our method uses a Gabor filter to reduce the complexity of the fabric signal, and takes the fabric patch’s projections in the small scale over-complete basis set as the original features, not the sparse representation. We compare the averages of the patch and its neighborhoods’ features with the standard features, which are the averages of all defect-free fabric images’ features. At last, according to this compared distance, the patch is classified as defective or non-defective. The experimental results on our own database and the TILDA database reveal that our features are more robust and the proposed algorithm can detect defects on twill, plain, gingha... <s> BIB037 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Inevitably there will be various types of fabric defect exists in textile production line. In order to distinguish and classify the types of defects more efficiently and accurately, an algorithm which combines Local Binary Patterns (LBP) and Gray-level Co-occurrence Matrix (GLCM) is proposed in this paper for fabric defect classification. The most pivotal step of the algorithm is to extract the local and global feature values of defect images. Firstly the local feature information of the image is extracted by adopting LBP algorithm. And then the overall texture information of the image is described via GLCM algorithm. In this way, the fabric image can be fully described from global and local. Finally, the two-part feature information are structured as a whole as the input of BP Neural Network. Thus the trained BP Neural Network can be used to classify the different types of defects. Experimental results show that the algorithm has higher classification accuracy. <s> BIB038 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> We present an automated system for detecting surface defects on OLED panels. These panels exhibit varying textures and patterns which complicates the defect detection process. These detection systems have to be highly accurate and reliable as even a small error in detection can cause huge losses. In this paper, we present a method for detection of OLED panel surface defects using a novel and simple set of features based on local inlier-outlier ratios and modified LBP. The proposed inlier-outlier vector is easy to compute and provides robust discrimination between defect and non-defect samples of micro defects such as scratches and spots which are missed by modified LBP, thus proving to be a good complement to the modified LBP vector. Next, we train a SVM classifier using the concatenation of inlier-outlier ratios and modified LBP features. In the experiments, we have evaluated our method on several defects like scratch, spot, stain and pit, and the results show that our method significantly outperforms methods which use only modified LBP approach with minimal increase in computational complexity. <s> BIB039 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> This paper proposes an unsupervised model to inspect various detects in fabric images with diverse textures. A fabric image with defects is usually composed of a relatively consistent background texture and some sparse defects, which can be represented as a low-rank matrix plus a sparse matrix in a certain feature space. The process is formulated as a least squares regression based subspace segmentation model, which is convex, smooth and can be solved efficiently. A simple and effective prior is also learnt from local texture features of the image itself. Instead of considering only the feature space' s global structure, the local prior is incorporated with it seamlessly by the proposed subspace segmentation model to guide and improve the segmentation. Experiments on a variety of fabric images demonstrate the effectiveness and robustness of the proposed method. Compared with existing methods, our method is more robust and locates various defects more precisely. <s> BIB040 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> A real time system fusing data from vision and laser sensors to detect and classify types of defects in brick masonry is presented. A Support Vector Machine (SVM) algorithm is conceived and used to develop a Defect Finding Classification Model (DFCM) to automatically classify the types of defects found in masonry walls using the image data obtained from both vision and 2D laser sensors mounted on an articulated 6-axis robotic arm. Thirteen image features were extracted to train the SVM. It was found that the proposed approach has a detection accuracy of over 96%. <s> BIB041 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> The objective of this paper is to propose a robust approach to building a computer vision system to detect and classify pavement defects based on features, such as the contour of feature (chain code histogram, Hu-moment), the shape of an object (length, width, area). In this paper, we present a method to build an automated system to detect and classify the different types of defects such as rupture of the road edge, potholes, subsidence depressions based on image processing techniques and machine learning methods. That system includes the following steps. First step is to detect defect position (ROI) then the defect is described by its features. Finally, each defect is classified these different defect features. In our approach the following algorithms have been using: Markov Random Fields and Graph cuts method for image segmentation, Random Forests algorithm for data classification. <s> BIB042 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> In order to improve the accuracy of detection of defects in banknote sorting, a new algorithm is proposed to detect cracks and scratches on banknote images. The quaternion wavelet transform and the least squares method are used for the banknote image registration. The features of the defects that are robust to gray intensity changes are constructed using edge information captured by the Kirsch operator. The banknote image is divided into several subzones of fixed size. The level of defect of the banknote image is estimated based on the defective features of each sub-zone. The experimental results show that the proposed algorithm is robust even with low quality banknote images and can obtain a high recognition rate and high stability. The proposed algorithm has already been used in a practical banknote sorting system. The quaternion wavelet transform is applied to banknote image registration.Defect banknote features are extracted by the proposed edge intensity.New banknote defect detection algorithm based on our QWT is proposed. <s> BIB043 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. High annotation effort and the limitation to a vocabulary of known markers limit the power of such approaches. Here, we perform unsupervised learning to identify anomalies in imaging data as candidates for markers. We propose AnoGAN, a deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, accompanying a novel anomaly scoring scheme based on the mapping from image space to a latent space. Applied to new data, the model labels anomalies, and scores image patches indicating their fit into the learned distribution. Results on optical coherence tomography images of the retina demonstrate that the approach correctly identifies anomalous images, such as images containing retinal fluid or hyperreflective foci. <s> BIB044 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> In this paper, we propose a discriminative representation for patterned fabric defect detection when only limited negative samples are available. Fabric patches are efficiently classified into defectless and defective categories by Fisher criterion-based stacked denoising autoencoders (FCSDA). First, fabric images are divided into patches of the same size, and both defective and defectless samples are utilized to train FCSDA. Second, test patches are classified through FCSDA into defective and defectless categories. Finally, the residual between the reconstructed image and defective patch is calculated, and the defect is located by thresholding. Experimental results demonstrate the effectiveness of the proposed scheme in the defect detection for periodic patterned fabric and more complex jacquard warp-knitted fabric. <s> BIB045 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> In this paper we propose a new unsupervised, automated texture defect detection that does not require any user-inputs and yields high accuracies at the same time. To achieve this end we use the non-extensive entropy with Gaussian gain as the regularity index, computed locally from texture patches through a sliding window approach. The optimum window size is determined by modeling the entropy values by a two-mode Gaussian mixture model and checking for the minimum entropy of the mode-probabilities. The outlier entropy values corresponding to defective areas are defined as those that exceed thrice the standard deviation, as is the norm in statistics. The result is automatic defect detection with no manual intervention. Empirical results on defective texture images from the Brodatz database provide accurate localization of the defect as compared to Chetverikov and Hanbury's maximal regularity method, which requires manual setting of threshold parameters for each type of texture despite of being a benchmark for texture defect detection. <s> BIB046 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Goat leather is responsible for a large part of the income generated by the most diverse clothing products, but the lack of modernization of some stages of leather production is still very evident, and may lead to divergent opinions regarding the quality of leather among tanning industries and finishing industries. In this paper it is proposed a new approach to aid goat leather qualification specialists based on the position of the found failures in the goat leather. There are two steps in the proposed approach. The first one is find the failure regions and the last one is extract some feature from the failure map founded. In this paper the authors use Gray Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP) and Structural Co-occurrence Matrix (SCM) as feature extractors from leather images. The classification task is executed by k-Nearest Neighbors (KNN), Multi Layer Perceptron (MLP) and Support Vector Machine (SVM). The combination of the LBP attribute extractor with the MLP classifier has 90% accuracy rates for the classification of regions with failure, but for quality classification of the leather, the SVM classifier has the best results (86% of accuracy rate), also using LBP. The results show that the proposed approach can be used to aid the specialists to classify the quality of the goat leather. <s> BIB047 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Surface defect detection and classification based on machine vision can greatly improve the efficiency of industrial production. With enough labeled images, defect detection methods based on convolution neural network have achieved the detection effect of state-of-art. However in practical applications, the defect samples or negative samples are usually difficult to be collected beforehand and manual labelling is time-consuming. In this paper, a novel defect detection framework only based on training of positive samples is proposed. The basic detection concept is to establish a reconstruction network which can repair defect areas in the samples if they are existed, and then make a comparison between the input sample and the restored one to indicate the accurate defect areas. We combine GAN and autoencoder for defect image reconstruction and use LBP for image local contrast to detect defects. In the training process of the algorithm, only positive samples is needed, without defect samples and manual label. This paper carries out verification experiments for concentrated fabric images and the dataset of DAGM 2007. Experiments show that the proposed GAN+LBP algorithm and supervised training algorithm with sufficient training samples have fairly high detection accuracy. Because of its unsupervised characteristics, it has higher practical application value. <s> BIB048 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Automated defect inspection has long been a challenging task especially in industrial applications, where collecting and labeling large amounts of defective samples are usually harsh and impracticable. In this paper, we propose an approach to detect and localize defects with only defect-free samples for model training. This approach is carried out by reconstructing image patches with convolutional denoising autoencoder networks at different Gaussian pyramid levels, and synthesizing detection results from these different resolution channels. Reconstruction residuals of the training patches are used as the indicator for direct pixelwise defect prediction, and the reconstruction residual map generated in each channel is combined to generate the final inspection result. This novel method has two prominent characteristics, which benefit the implementation of automatic defect inspection in practice. First, it is absolutely unsupervised that no human intervention is needed throughout the inspection process. Second, multimodal strategy is utilized in this method to synthesize results from multiple pyramid levels. This strategy is capable of improving the robustness and accuracy of the method. To evaluate this approach, experiments on convergence, noise immunity, and defect inspection accuracy are conducted. Furthermore, comparative tests with some excellent algorithms on actual and simulated data sets are performed. Experimental results demonstrated the effectiveness and superiority of the proposed method on homogeneous and nonregular textured surfaces. <s> BIB049 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> The established Machine Learning algorithm Random Forest (RF) has previously been shown to be effective at performing automated defect detection for test pieces which have been processed using fluorescent penetrant inspection (FPI). The work presented here investigates three methods (two previously proposed in other fields, one novel method) of modifying the FPI RF based on the individual performance of decision trees within the RF. Evaluating based on the \(F_{2}\) Score, which is the harmonic mean of precision and recall which places a larger weighting on recall, it is possible to reduce the RF in size by up to 50%, improving speed and memory requirements, whilst still gain equivalent results to a full RF. Introducing a performance based weighting or retraining decision trees which fall below a certain performance level however, offers no improvement on results for the increased computation time required to implement. <s> BIB050 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Defects on steel strip surface can long-term cause undesirable effects, since they make physical and/or chemical properties mismatched from steel's specification. Nowadays, automatic visual-based surface inspection is adopted, in order to detect the defects on steel strip surface after being produced. Moreover, since these defects appear in wide variety of forms and various classes, machine learning methods are generally involved to visual surface inspection for coping with these appearances. In this paper, we present a novel defect detection model to perform defect segmentation of hot-rolled steel strip surface, by using Convolutional Auto-Encoder (CAE) and sharpening process to extract the defect features of input image, then applied postprocessing for visualization. In the experiments, the NEU database, which provides six kinds of typical surface defects of hot-rolled steel strip, was applied to evaluate the efficiency of the proposed model. This database also provides difficulty challenges regarding diversity of intra-class and similarity of inter-class. The results show that the proposed model can perform defect segmentation in all kinds of defects in database, however the efficiency was compromised by illumination changes. Notable that, this segmentation is based on unsupervised learning with small training dataset and no labeling procedure, so it can be easily extended to the real world application. Eventually, this defect detection shall improve the productivity and reliability of steel strip's production process. <s> BIB051 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Supervised and Non-Supervised Classifiers <s> Abstract Defect inspection is extremely crucial to ensure the quality of steel surface. It affects not only the subsequent production, but also the quality of the end-products. However, due to the rare occurrence and appearance variations of defects, surface defect identification of steels has always been a challenging task. Recently, deep learning methods have shown outstanding performance in image classification, especially when there are enough training samples. Since most sample images of steel surface are unlabeled, a new semi-supervised learning method is proposed to classify surface defects of steels. The new method is named CAE-SGAN, as it is based on Convolutional Autoencoder (CAE) and semi-supervised Generative Adversarial Networks (SGAN). CAE-SGAN first trains a stacked CAE through massive unlabeled data. Considering the appearance variations of defects, the passthrough layer is used to help CAE extract fine-grained features. After CAE is trained, the encoder network of CAE is reserved as the feature extractor and fed into a softmax layer to form a new classifier. SGAN is introduced for semi-supervised learning to further improve the generalization ability of the new method. The classifier is trained with images collected from real production lines and images randomly generated by SGAN. Extensive experiments are carried out with samples captured from different steel production lines, and the results indicate that CAE-SGAN had yielded best performances compared with traditional methods. Especially for hot rolled plates, the classification rate is improved by around 16%. <s> BIB052
The main goals of the visual processes are the detection and classification of defects that can be solved by establishing classifiers. In the previous section, we discuss the approaches that are more related to the low-level image processing level, based on Figure 3 . This section compares the methods related to the high-level image processing. Their general goal is to discriminate a specific defect, texture feature, or pattern. Based on their processing mechanics, these classifiers can be classified in two groups: (1) supervised; and (2) non-supervised or semi-supervised classifiers (see Table 3 ). BIB015 BIB008 BIB041 BIB038  BIB039 BIB020 BIB035 BIB036 BIB018  BIB016 BIB010 BIB047 Unsupervised/semi-Statistical/Novelty detection BIB040 BIB023 BIB019 BIB004 BIB011 BIB012 BIB013 BIB024 BIB037 BIB042   BIB050 BIB044 BIB051 BIB048 BIB052 supervised classifiers Gaussian mixture model BIB043 BIB029 BIB031 BIB049 Supervised classification methods incorporate the human model-as discussed in Section 3-where the application is searching for features of a predefined class. Detectable features are predefined and the classifier has to be previously trained to recognize them under supervision  BIB023 BIB011 BIB024 BIB020 BIB025 BIB026 BIB027 . As part of the supervised classifiers, the K-Nearest Neighbor (KNN) classifier is a non-parametric learning algorithm where the output object, classified into classes, uses its local neighborhood to formulate a prediction. The KNN algorithm is among the simplest machine learning algorithms, where K is a user-defined constant that defines the number of neighbors to employ for classification. A high K value reduces the noise of classification, but makes the boundaries between classes less distinct, thus the best choice must be tuned upon the dataset. In BIB026 BIB027 , Lopez et al. used KNN to classify ceramic tile images based on chromatic features and achieved high performance using high K-value, while Mandriota et al. BIB024 applied KNN to inspect rail surfaces but did not find significant difference in their dataset performance because of the higher K-value. There are also numerous good classifier-based implementations, e.g., Wiltschi et al. BIB011 and Latif-Amet et al. BIB014 classified images based on the parametric distance. Chan and Pang  classified defects by simulating their main features to describe a classifier. Pernkopf BIB025 used KNN to classify steel surfaces based on dispersions extracted from hidden Markov random fields. Artificial neural networks are commonly used classifiers due to them being universal function approximators BIB001 . They are computing systems inspired by biological neural networks that can learn from data and store the knowledge of the classification. The key feature of the neural networks is the iterative learning process in which teaching-samples are presented to the network in batches or minibatches and the weighted connections between neurons are adjusted by the input values associated with the activation function. A Feed-Forward Neural Network (FFNN), described in  by Kumar, was applied to classify extracted texture features of textile images and to solve segmentation problem. Monadjemi et al. BIB023 established a Back Propagation (BP) Neural Network (NN) combined with lower level processes (e.g., co-occurrence matrices, LBP, Gabor filters, etc.) and outperformed a KNN in ceramic texture features classification. Stojanovic et al.  used a three-layer BP NN to detect fabric defects with 86.2% success. Within the last decades, BPNNs have been commonly used and successful methods for defect detection: as reported in BIB021 BIB022 , Kuo et al. achieved 91.88% on 160 defective images and 94.38% success rates on 240 defective images. Hung and Cheng BIB017 used BPNNs with fuzzification technique but with unclear success rate. In BIB032 , the authors achieved a 91-100% detection rate on 16-16 samples with BPNNs, while Zhang et al. BIB033 achieved a 83.4% success rate with a FFNN. Besides the previously mentioned methods, there are numerous studies about applied neural networks, such as studies reported in BIB003 BIB002 BIB005  BIB030 BIB007 BIB006 BIB009 . In 2012, Cord and Chambon BIB034 proposed an automatic defect detection method by textural pattern recognition based on a supervised learning method, called AdaBoost, and achieved a 91% detection rate with 12.5% false alarm on 6875 samples. In BIB050 , Shipway et al. investigated three methods of modifying the fluorescent penetrant inspection Random Forest (RF) method, based on the individual performance of decision trees within the RF. Their main attempt was to improve the effectiveness of RF at performing automated defect detection. Other commonly used supervised classifiers have been developed, such as Self-Organizing Maps (SOM) mainly used for clustering, feature mapping and SVM to classify defects based on features. SVMs can be an appropriate alternative to FFNNs, because they are computationally easier to train and do not have local minimum problems. Therefore, many studies BIB018 BIB016 have been published about the SVM in defect detection and the authors of  BIB010 reported SOM methods. The authors of BIB015 BIB008 BIB020 performed unsupervised clustering SOM with supervised sample mapping. Supervised classification proved its value in the field of visual inspection. However, it strongly depends by the number of samples and training conditions. Accordingly, the training phase of the classifier often takes time and needs a large defect sample database-which is not always available-but it achieves higher success rates with a longer training phase. Recently, in BIB045 , Li et al. proposed a discriminative representation for patterned fabric defect detection and achieved a 95.8% detection rate with 2.5% false alarms on 600 samples. They classified sample images with the Fisher criterion-based stacked denoising autoencoders (FCSDA) and introduced deep learning for the first time in the defect detection field. Tural et al.  recently developed a system using various image processing and filtering method (Bilateral filtering, Sobel filtering, thresholding, and morphological closing) in a combination with SVM to detect and classify defects on bullet shells. They achieved 96% accuracy in a real-time production environment. The main feature for non-supervised classifiers is the capability of detecting every feature that is not part of the texture and pattern. They represent the other approach of the human detection model, in which the detector is trained with normal samples and every deviation is considered as abnormal. This approach is particularly useful when the spatial distribution of the abnormalities is needed. These methods usually exploit distance-based or thresholding rules to discriminate questionable features. Markou and Singh   published studies on the novelty for detection approaches using statistical and neural-network-based techniques. During visual inspections, statistical parametric approaches are often used BIB023 BIB019 BIB004 BIB011 BIB012 BIB013 BIB024 . The essential hypothesis is the Gaussian natural distribution of the data. Gururajan et al. BIB029 proposed a Gaussian mixture model with Expectation-Maximization features to detect one specific kind of defect, and achieved 93% true positive and 95% true negative detection success rates for six types of soils under four categories of laundering treatments. Zhang et al. BIB031 combined Gabor transformation with a Gaussian mixture model for plain fabric defect detection, with 87% classification success rate achieved. In BIB028 , the authors proposed two different mixture models to measure pattern likelihoods by using simple parametric thresholding, automatically determined from training data with a 92.67% overall accuracy. Recently, Zhu et al. BIB037 applied Gabor filter as a pre-process method to reduce the complexity of the fabric signal, and built the over-complete basis set via sparse coding. They achieved a 93.7% defect detection success rate with 9.6% false alarms on 284 samples. Susan and Sharma BIB046 proposed a new unsupervised automated texture defect detection method that uses a Gaussian mixture entropy model to determine the optimal window size for feature extraction. Recently, Mei et al. BIB049 developed an unsupervised learning based method by using only defect free samples for model training. The approach was carried out by reconstructing image patches with convolutional denoising autoencoder networks at different Gaussian pyramid levels, and synthesizing detection results from these different resolution channels.