A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Local binary patterns <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> BIB001 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Local binary patterns <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> BIB002 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Local binary patterns <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> BIB003 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Local binary patterns <s> The LBP operator is a theoretically simple yet very powerful method of analyzing textures. Through its recent extensions, it has been made into a really powerful measure of image texture, showing excellent results in terms of accuracy and computational complexity in many empirical studies. The LBP operator can be seen as a unifying approach to the traditionally divergent statistical and structural models of texture analysis. Texture is described in terms of micro-primitives (textons) and their statistical placement rules. Optionally, the primitives may be coupled with a complementary measure of local image contrast, which measures the strength of the primitives. In this chapter the relation of the LBP operator to other texture analysis methods is explained. The chapter shows how the LBP combines aspects of statistical and structural texture analysis, and why it is called a “unifying approach”. The theoretical foundation of the operator is explained starting from a definition of texture in a local neighborhood. A number of extensions to the basic operator are also introduced. The extensions include three different multi-scale models, an opponent color operator and a rotation invariant version. Finally, a number of successful applications of the operator are highlighted. <s> BIB004
The LBP operator was first introduced by Ojala et al. BIB001 as a shift invariant complementary measure for local image contrast. It uses the graylevel of the centre pixel of a sliding window as a threshold for surrounding neighbourhood pixels. Its value is given as a weighted sum of thresholded neighbouring pixels. Usually, a simple local contrast measurement is calculated as a complement to the LBP value in order to characterise local spatial relationships, together called LBP/C BIB001 . Two-dimensional distributions of the LBP and local contrast measures are used as texture features. The LBP operator is relatively invariant with respect to changes in illumination and image rotation (for example, compared to co-occurrence matrices), and computationally simple BIB004 . It has been applied to defect detection on ceramic tile surfaces  , wood BIB002 BIB003 , and real-time inspection  . Although good performance in texture classification has been achieved  , LBP has been reported as considerably lower performance than co-occurrence matrix and filtering based approaches in detecting textural defects on ceramic tile surfaces, on which textures are usually randomly applied  .