Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications. <s> BIB001 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> Continued increases in the cost of materials and labor make it imperative for furniture manufacturers to control costs by improved yield and increased productivity. This paper describes an Automated Lumber Processing System (ALPS) that employs computer tomography, optical scanning technology, the calculation of an optimum cutting strategy, and a computer-driven laser cutting device. While certain major hardware components of ALPS are already commercially available, a major missing element is the automatic inspection system needed to locate and identify surface defects on boards. This paper reports research aimed at developing such an inspection system. The basic strategy is to divide the digital image of a board into a number of disjoint rectangular regions and classify each independently. This simple procedure has the advantage of allowing an obvious parallel processing implementation. The study shows that measures of tonal and pattern related qualities are needed. The tonal measures are the mean, variance, skewness, and kurtosis of the gray levels. The pattern related measures are those based on cooccurrence matrices. In this initial feasibility study, these combined measures yielded an overall 88.3 percent correct classification on the eight defects most commonly found in lumber. To minimize the number of calculations needed to make the required classifications a sequential classifier is proposed. <s> BIB002 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> Two standard approaches for texture analysis make use of numerical features of the second-order gray-level statistics, and on first-order statistics of gray-level differences, respectively. Feature sets of these types, all designed analogously, were used to analyze four sets of carpet samples exposed to different degrees of wear. It was found that some of the features extracted from the spatial gray-level-dependence matrix, neighboring gray-level-dependence matrix, gray-level difference method, and the gray-level run-length method allowed discrimination of degrees of wear in wool carpet. The methods developed could be of use in quality control. > <s> BIB003 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <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> BIB004 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> A segmentation scheme to detect surface defects is proposed. An unsupervised neural network, the self-organizing map, is used to estimate the distribution of fault-free samples. An unknown sample is classified as a defect if it differs enough from this estimated distribution. A new scheme for determining this difference is suggested. The scheme makes use of the Voronoi set of each map unit and defines a new rule for finding the best-matching map unit. The proposed scheme is general in the sense that it can be applied to fault detection of different types of surfaces. <s> BIB005 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently. For classification a method based on Kullback discrimination of sample and prototype distributions is used. The classification results for single features with one-dimensional feature value distributions and for pairs of complementary features with two-dimensional distributions are presented <s> BIB006 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> This paper examines the problem of quality control and defect identification in woven textile fabrics by introducing an improved method for texture description. The approach is based on spatial gray level dependence methodology and addresses the issue of optimal parameter selection for deriving the maximum textural information. We introduce the use of the /spl chi//sup 2/ significance test on elemental feature matrices in order to obtain higher per feature texture description and hence improve the capture of defects in the underlying textile pattern. <s> BIB007 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> A new class of multiscale symmetry features provides a useful high-level representation for color texture. These symmetry features are defined within and between the bands of a color image using complex moments computed from the output of a bank of orientation and scale selective filters. We show that these features not only represent symmetry information but are also invariant to rotation, scale, and illumination conditions. The features computed between color bands are motivated by opponent process mechanisms in human vision. Experimental results are provided to show the performance of this set of features for texture classification and retrieval. <s> BIB008 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <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> BIB009 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> We have developed a self-organizing map (SOM) -based approach for training and classification in visual surface inspection applications. The approach combines the advantages of non-supervised and supervised training and offers an intuitive visual user interface. The training is less sensitive to human errors, since labeling of large amounts of individual training samples is not necessary. In the classification, the user interface allows on-line control of class boundaries. Earlier experiments show that our approach gives good results in wood inspection. In this paper, we evaluate its real time capability. When quite simple features are used, the bottleneck in real time inspection is the nearest SOM code vector search during the classification phase. In experiments, we compare acceleration techniques that are suitable for high dimensional nearest neighbor search typical for the method. We show that even simple acceleration techniques can improve the speed considerably, and the SOM approach can be used in real time with a standard PC. <s> BIB010 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> Automatic thresholding has been widely used in the machine vision industry for automated visual inspection of defects. A commonly used thresholding technique, the Otsu method, provides satisfactory results for thresholding an image with histogram of bimodal distribution. This method, however, fails if the histogram is unimodal or close to unimodal. For defect detection applications, defects range from no defect, small defect, to large defect, which means the gray-level distributions range from unimodal to bimodal. In this paper, we revised and improved the Otsu method for selecting optimal threshold values for both unimodal and bimodal distributions. We also tested the performance of the revised method on common defect detection applications. <s> BIB011 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> Computer vision is moving into a new era in which the aim is to develop visual skills for robots that allow them to interact with a dynamic, unconstrained environment. To achieve this aim, new kinds of vision algorithms need to be developed which run in real time and subserve the robot's goals. Two fundamental goals are determining the identity of an object with a known location, and determining the location of a known object. Color can be successfully used for both tasks. ::: This dissertation demonstrates that color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models. It shows that color histograms are stable object representations in the presence of occlusion and over change in view, and that they can differentiate among a large number of objects. For solving the identification problem, it introduces a technique called Histogram Intersection, which matches model and image histograms and a fast incremental version of Histogram Intersection which allows real-time indexing into a large database of stored models. It demonstrates techniques for dealing with crowded scenes and with models with similar color signatures. For solving the location problem it introduces an algorithm called Histogram Backprojection which performs this task efficiently in crowded scenes. <s> BIB012 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> In industrial quality inspection of colour texture surfaces, such as ceramic tiles or fabrics, it is important to maintain a consistent colour shade or tonality during production. We present a multidimensional histogram method using a novelty detection scheme to inspect the surfaces. The image noise, introduced by the imaging system, is found mainly to affect the chromatic channels. For colour tonality inspection, the difference between images is very subtle and comparison in the noise dominated chromatic channels is error prone. We perform vector-ordered colour smoothing and extract a localised feature vector at each pixel. The resulting histogram represents an encapsulation of local and global information. Principal component analysis (PCA) is performed on this multidimensional feature space of an automatically selected reference image to obtain reliable colour shade features, which results in a reference eigenspace. Then unseen product images are projected onto this eigenspace and compared for tonality defect detection using histogram comparison. The proposed method is compared and evaluated on a data set with groundtruth. <s> BIB013 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> Chi-square histogram distance is one of the distance measures that can be used to find dissimilarity between two histograms. Motivated by the fact that texture discrimination by human vision system is based on second-order statistics, we make use of histogram of grey level co-occurrence matrix (GLCM) that is based on second-order statistics and propose a new machine vision algorithm for automatic defect detection on patterned textures. Input defective images are split into several periodic blocks and GLCMs are computed after quantising the grey levels from 0-255 to 0-63 to keep the size of GLCM compact and to reduce computation time. Dissimilarity matrix derived from chi-square distances of the GLCMs is subjected to hierarchical clustering to automatically identify defective and defect-free blocks. Effectiveness of the proposed method is demonstrated through experiments on defective real-fabric images of two major wallpaper groups (pmm and p4m groups). <s> BIB014 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> Abstract Fabric defect detection has been an active area of research since a long time and still a robust system is needed which can fulfill industrial requirements. A robust automatic fabric defect detection system (FDDS) would results in quality products and more revenues. Many different approaches and method have been tried to implement FDDS. Most of them are based on two approaches, one is statistical like gray level co-occurrence (GLCM) and other is transform based like Gabor filter. This paper presents a new scheme for automated FDDS implementation using GLCM and also compare it with Gabor filter approach. GLCM texture statistics are extracted and plotted against the inter-pixel distance of GLCM as signal graph. The non-defective fabric image information is compared with the test fabric image. In Gabor filter based approach, a bank of Gabor filter with different scales and orientations is generated and fabric images are filtered with convolution mask. The generated magnitude responses are compared for defect decision. In our implementation of both approaches in same environment, the GLCM approach produces higher defect detection accuracies than Gabor filter approach and more computationally efficient. <s> BIB015 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> Abstract In industrial fabric productions, automated real time systems are needed to find out the minor defects. It will save the cost by not transporting defected products and also would help in making company image of quality fabrics by sending out only undefected products. A real time fabric defect detection system (FDDS), implemented on an embedded DSP platform is presented here. Textural features of fabric image are extracted based on gray level co-occurrence matrix (GLCM). A sliding window technique is used for defect detection where window moves over the whole image computing a textural energy from the GLCM of the fabric image. The energy values are compared to a reference and the deviations beyond a threshold are reported as defects and also visually represented by a window. The implementation is carried out on a TI TMS320DM642 platform and programmed using code composer studio software. The real time output of this implementation was shown on a monitor. <s> BIB016 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <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> BIB017 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <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> BIB018 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> Abstract A new approach is proposed for automatically detecting crack defects with dark colors and low contrasts in magnetic tile images using the fast discrete curvelet transform (FDCT) and texture analysis. In this methodology the original images were first decomposed and reconstructed based on the FDCT. Then the thresholds of decomposition coefficients were calculated by texture feature measurements. With these thresholds the surface textures in the images can be eliminated. Finally by extracting contours from the reconstructed images, the expected images without textures but with crack defects contours were obtained. Experimental results show that the proposed method could eliminate the contours of the textures, and extract from the image cracks longer than 0.8 mm. <s> BIB019 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> Abstract Defects on product surfaces affect quality of the product. Machine vision provides an efficient tool for the surface defect detection. Threshold is commonly used to separate objects from the image background in the vision-based inspection method. The Otsu method is one of the most used approaches to decide the threshold for a satisfied result when the image histogram is bimodal, but it fails when the histogram of an image is unimodal or close to unimodal. Defects in product surfaces can range from small to large sizes, which results in distributions of the image histogram change from unimodal to bimodal. An improved Otsu method, named the weighted object variance (WOV), is proposed in this research to detect defects on product surfaces. A parameter that equals the cumulative probability of defects occurrence is weighted on the object variance of between-class variance. The weight ensures that the threshold always be a value that locates at the valley of two peaks or at the left bottom rim of a single peak histogram. It is essential to have a high detection rate and low false alarm rate for the defect detection. Experimental results demonstrate the effectiveness of the improved Otsu method in the defect detection of various surfaces. Compared to other thresholding methods such as maximum entropy, Otsu, valley-emphasis, and modified valley-emphasis methods, the WOV method provides better segmentation results. <s> BIB020 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> Abstract Automatic thresholding is a popular segmentation technique that is widely used for automated visual inspection of defects. Many methods have been proposed for appropriate selection of the threshold value. However, most of these methods perform well for images where defects and background have distinguishable histogram modes and select a threshold close to a valley between the two modes which is usually very hard to locate except for the clearly bi-modal histograms. Additionally, where defect detection requires bi-level segmentation, these methods require a prior knowledge of the number of thresholding levels. In this paper, a new approach for threshold selection is taken that aims to find the threshold value at the boundary of the intensity ranges of defects (object) and background by comparing the histogram modes of the background and defective regions. The proposed method automatically detects defective regions as well as defect-free regions or background. By study of the histogram of the background region, appropriate threshold values are automatically selected at the extents of the background histogram mode. The proposed method proved very effective on several standard images of surface defects. The significance of the method's efficiency is well seen in successfully segmenting defects in images with non-uniform background and with no visible bi- or multi-modal behavior. Another significance of the technique is segmentation of defects comprising of two intensity regions (such as bumps and pits) without specifying the number of threshold levels. <s> BIB021 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <s> Abstract In this study, a new detection algorithm for yarn-dyed fabric defect based on autocorrelation function and grey level co-occurrence matrix (GLCM) is put forward. First, autocorrelation function is used to determine the pattern period of yarn-dyed fabric and according to this, the size of detection window can be obtained. Second, GLCMs are calculated with the specified parameters to characterise the original image. Third, Euclidean distances of GLCMs between being detected images and template image, which is selected from the defect-free fabric, are computed and then the threshold value is given to realise the defect detection. Experimental results show that the algorithm proposed in this study can achieve accurate detection of common defects of yarn-dyed fabric, such as the wrong weft, weft crackiness, stretched warp, oil stain and holes. <s> BIB022 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <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> BIB023 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <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> BIB024 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Statistical Approaches <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> BIB025
Statistical approaches focus on analyzing the spatial distribution of pixel values in a recorded image. In this category, it is possible to count numerous publications and techniques, ranging from low-level to higher-order statistics, such as histogram statistics, autocorrelation, local binary patterns (LBP) and others (see Table 2 ). Histogram properties and statistics are useful supports for both higher-level and low-level processes with low computational cost. These processes include operations from statistics, such as sensitivity range, mean, geometric mean, harmonic mean, standard deviation, variance and median. It also includes other histogram comparison statistics used for texture features, such as L1 norm, L2 norm, Mallows or EMD distance, Bhattacharyya distance, Matusita distance, divergence, histogram intersection, Chi-square and the normalized correlation coefficient. Implementations can be found, for instance, in BIB004 BIB011 BIB012 BIB008  , which have proven to be worthy as low-level processes in defect detection. They can be also used as tonality disruptors (e.g., BIB008 BIB013 ) due to their properties of being invariant to translation and rotation, and being insensitive to the exact spatial distribution of color pixels. Yuan et al. BIB020 proposed an improved Otsu thresholding method, called Weighted Object Variance (WOV), and achieved 94% of detection rate with 8.4% of false alarm rate. In another recent study, the authors also proposed and improved Otsu thresholding method, but without reporting detailed results on the method's performance BIB021 . Recently, Dastoorian et al.  implemented an Adaptive Generalized Likelihood Ratio (AGLR) approach in a practical case study to inspect 27 samples, each with a unique fault using 3D laser scanning technology. The method detects defects by checking whether the distribution of the observed data is significantly different from a baseline historical distribution in an adaptive manner. where TP, TN, FN and FP stand for true positive, true negative, false negative and false positive, respectively. Spatial grey level co-occurrence matrices (GLCMs) are one of the most well-known and commonly used texture features BIB001 . These are statistical methods that measure the spatial relationship of grey-scale pixels into co-occurrence matrices. The GLCM functions characterize the texture of an image by calculating how often pairs of pixels with specific values and in a specified spatial relationship occur in an image, given a displacement vector, and then extract texture features such as energy, entropy, contrast, homogeneity and correlation from these matrices. There are several publications describing applications using co-occurrence matrices to detect defects BIB004 BIB014 BIB002 BIB003 BIB005 BIB007 BIB016 BIB015 . Despite the high number of applications, the co-occurrence matrix features have several deficiencies. Several reports  have proved that there is no generally accepted optimization for the displacement vector, and GLCMs have other statistical and computational dependencies. In other comparative studies BIB003 BIB005 , the co-occurrence matrices have shown worse performance in defect detection compared to the Markov Random Fields (MRF), filtering-based, LBP methods. In contrast to other papers, such as BIB015 , the authors showed the opposite result compared to Gabor filter based approach, with a slightly better performance. Recently, the GLCM method has become a very popular approach: Capizzi et al.  proposed an GLCM based detector with a radial basis probabilistic neural network to detect defects on fruits, and achieved 97.25% detection rate with 2.75% false alarm. In BIB025 , the authors used the GLCM and other methods to extract features to train a supervised Support Vector Machine (SVM). In BIB017 BIB018 , the authors achieved 93.4% and 83.3% defect detection rate to replace manual defect inspection, respectively. In BIB019 , the authors used the GLCM for textural feature description and combined it with a Fast Discrete Curvelet Transform (FDCT), achieving 93.3% detection rate with 3.6% false alarm on ferrite magnetic tiles. The methods using autocorrelation can be applied on textures that contain repetitive patterns, such as textiles. This mathematical algorithm aims at finding correlation between the texture and its translation with a displacement vector and derives vertexes in case of high regularity. However, this type of method fits for several defect detection problems, due to the high sensitivity to noise interference and the only application capability on patterned textures makes it unsuitable for most of the detection tasks because of their random surface. Zhu et al. BIB022 published a study about a yarn-dyed fabric defect detector, which combines autocorrelation with the GLCM; however, the system was tested only on 16 samples and no detailed results were reported. Local binary pattern (LBP) is a computational low cost and very efficient texture operator. It calculates thresholds of the grey-scale pixels and its neighbors in a sliding window, which uses the center pixel of the window as a threshold. It considers the threshold's result as a binary number. It was first published by Ojala et al. BIB006 as a labeling visual descriptor for textures. The LBP operator is insensitive to changes in illumination and image rotation, and it makes it a robust operator. It has similarities in the logic of computation with co-occurrence matrices, but the LBP seems to achieve lower performance  . It has been used in several defect detector applications on varied materials BIB009  BIB010   such as ceramics or wood. Recently, Zhang et al. BIB023 combined the GLCM and LBP methods to extract image features to train a BP Neural Network: it achieved 97.6% detection rate on 90 samples from TILDA database  . In BIB024 , Sindagi and Srivastava proposed a modified LBP method to train a SVM classifier, detecting defects with 93% accuracy on 148,905 samples.