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:  Histogram properties 
1. Abstract of Cited Paper (BIB001):  The authors present a method designed to solve the problem of automatic color shade grading for industrial inspection of randomly textured multicolored ceramic tiles. Their grading method is based on the comparison of color histograms and they present results of correctly grading several series of ceramic tiles, the differences of which were at the threshold of human perception. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  We present a novel approach, based on local image histograms, for statistically characterizing the appearance of deformable models. In deformable model segmentation, appearance models measure the likeli- hood of an object given a target image. To determine this likelihood we compute pixel intensity histograms of local object-relative image regions from a 3D image volume near the object boundary. We use a Gaussian model to statistically characterize the variation of non-parametric his- tograms mapped to Euclidean space using the Earth Mover's Distance. The new method is illustrated and evaluated in a deformable model segmentation study on CT images of the human bladder, prostate, and rectum. Results show improvement over a previous profile based appear- ance model, out-performance of statistically modeled histograms over simple histogram measurements, and advantages of local image regions over global regions.

Literature Review:
Commonly used histogram statistics include range, mean, geometric mean, harmonic mean, standard deviation, variance, and median. Histogram comparison statistics, such as L 1 norm, L 2 norm, Mallows or EMD distance, Bhattacharyya distance, Matusita distance, Divergence, Histogram intersection, Chi-square, and Normalised correlation coefficient, can also be used as texture features. Despite their simplicity, histogram techniques have proved their worth as a low cost, low level approach in various applications, such as BIB001 BIB002  . They are invariant to translation and rotation, and insensitive to the exact spatial distribution of the colour pixels. These characteristics make them ideal for use in application to tonality discrimination, e.g. BIB002  . The accuracy of histogram based methods can be enhanced by using statistics from local image regions BIB004  . Simple histogram moments, such as mean and standard deviation, from subblocks were used for defect classification  . Recently, Ng BIB003 proposed a histogram separation technique based on the Otsu global thresholding method  to segment surface defects. However, it requires the assumption that intensity of defective regions are separatable from those of normal regions which is not always true for textured surface.