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:  Co-occurrence matrices 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  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. > 
3. Abstract of Cited Paper (BIB003):  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. 
4. Abstract of Cited Paper (BIB004):  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. 
5. Abstract of Cited Paper (BIB005):  In this paper the performance of two histogram-based texture analysis techniques for surface defect detection is evaluated. These techniques are the co-occurrence matrix method and the local binary pattern method. Both methods yield a set of texture features that are computed form a small image window. The unsupervised segmentation procedure is used in the experiments. It is based on the statistical self-organizing map algorithm that is trained only with fault-free surface samples. Results of experiments with both feature sets are good and there is no clear difference in their performances. The differences are found in their computational requirements where the features of the local binary pattern method are better in several aspects.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Literature Review:
Spatial graylevel co-occurrence matrices (GLCM) BIB001 are one of the most well-known and widely used texture features. These second order statistics are accumulated into a set of 2D matrices, each of which measures the spatial dependency of two graylevels, given a displacement vector. Texture features, such as energy, entropy, contrast, homogeneity, and correlation, are then derived from the co-occurrence matrix. Several works have reported using co-occurrence matrices to detect defects, such as BIB002 BIB003  BIB004 BIB005 . For example in BIB004 , Iivarinen et al. applied co-occurrence texture features to detecting defects in paper web where the normal textures have characteristic frequency. Co-occurrence matrix features can suffer from a number of shortcomings. It appears there is no generally accepted solution for optimising the displacement vector   . The number of graylevels is usually reduced in order to keep the size of the co-occurrence matrix manageable. It is also important to ensure the number of entries of each matrix is adequate to be statistically reliable. For a given displacement vector, a large number of features can be computed, which implies dedicated feature selection procedures. In a comparative study byÖzdemir et al. in  , the co-occurrence matrix method showed poor performance in detecting textural defects in textile products compared to other techniques such as MRF and filtering-based methods. Iivarinen BIB006 found co-occurrence matrices and the local binary pattern (LBP) operator had similar performance in detecting defects, while LBP was more efficient.