A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s>  <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> BIB001 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s>  <s> The problem of classification of defects occurring in a textile manufacture is addressed. A new classification scheme is devised in which different features, extracted from the gray level histogram, the shape, and cooccurrence matrices, are employed. These features are classified using a support vector machines (SVM) based framework, and an accurate analysis of different multiclass classification schemes and SVM parameters has been carried out. The system has been tested using two textile databases showing very promising results. <s> BIB002 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s>  <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> BIB003
: Example defects on different types of surfaces -from left: Steel BIB001 , stone  , textile BIB002 , wood  , and ceramic tiles BIB003 .