You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques 
Chapter Title:  Visual inspection via supervised classification 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  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. 
4. Abstract of Cited Paper (BIB004):  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. 
5. Abstract of Cited Paper (BIB005):  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. 
6. Abstract of Cited Paper (BIB006):  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. 
7. Abstract of Cited Paper (BIB007):  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. 
8. Abstract of Cited Paper (BIB008):  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. 
9. Abstract of Cited Paper (BIB009):  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].

Literature Review:
In supervised classification, the input pattern, based on features derived from earlier stages, is identified as a member of a pre-defined known class. This approach has been widely used in visual inspection, e.g. BIB002   BIB006 BIB007  BIB009 BIB010 . The K-Nearest Neighbour (KNN) classifier is a simple nonparametric supervised distance-based learning algorithm where the pattern is assigned to the class shared by the majority of the K nearest neighbours. In BIB009 BIB010 , Lopez et al. used KNN to classify ceramic tile surfaces based on chromatic features extracted from individual channels. The authors also investigated various values of K in terms of classification accuracy. Mandriota et al.  also applied KNN to classify filter responses and wavelet coefficients to inspect rail surfaces. Contrary to BIB009 BIB010 , the authors did not find any performance improvement on their dataset by increasing the value K. Wiltsh et al. BIB002 used a parametric minimum distance based classifier to inspect steel images. Latif-Amet et al. BIB003 also used a Mahalanobis distance based parametric classifier. Recently, Pernkopf BIB006 classified steel surfaces based on data likelihood computed from coupled hidden Markov random fields. In  , Chan and Pang classified four types of fabric defects by fitting into the expected feature model. Artificial neural networks have been extensively used in decision making procedures due to their ability to learn complex non-linear input-output relationships. In  , raw pixel values in textile images were extracted from local neighbourhood as the textural feature for each individual pixel. PCA was then applied to the feature vectors to reduce the feature space dimension. Finally, a feed-forward neural network was used to classify each pixel. Recently, Monadjemi et al. BIB007 applied a back propagation neural network and KNN to classify ceramic tile surfaces using various texture features, such as co-occurrence matrices, LBP, Gabor filtering, eigenfiltering, and discrete cosine transform. They proposed a neural network that generally outperformed the KNN classifier. Another popular network is the Self-Organising Map (SOM), which is mainly used for clustering and feature mapping BIB004 . Nisknen et al. BIB001 BIB005  performed SOM based clustering of wood surfaces. However, although the clustering is unsupervised, the labelling of defect-free and defective samples in the SOM map was manually performed. Support vector machines (SVM) is also used to classify surfaces, such as  and BIB008 . For example, in  , SVM was used to classify defects based features extracted from histograms, co-occurrence matrices, and shape information of defective regions. Supervised classification have been demonstrated as a powerful approach when both training data and testing data are well-conditioned. For example, in  , as high as 97.02% accuracy was achieved while using cooccurrence features and back propagation neural network, compared to 91.46% accuracy while using 7 × 7 eigen-filters and only trained on defect-free samples. However, supervised approach often involves a lengthy training stage and, more importantly, it requires a substential number of defective samples, which for some applications can be difficult to obtain.