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:  Tonality Inspection 
1. Abstract of Cited Paper (BIB001):  This paper is based on resolving a problem found in the ceramics industry, namely Porcelanatto. Porcelanatto is a relatively new product, it is a new type of glazed tile and is used as a floor covering. Its manufacturing process consists basically of mixing two or three types of grains and after that the mixture is heated in an oven. Finally the mixture is polished and a perfectly flat surface is produced. Ideally, a piece of Porcelanatto should be a random mix of two or three types of grain where each grain has a fixed color and average size. Nevertheless, a selection of real tiles from the production line show slight differences in average grain size. These are responsible for the distinct appearance between tiles visible to the human eye once they have been placed on the floor. As it is impossible to obtain ideal tiles they must be classified into groups according to the criteria `appearance to the human eye' which is related to the average grain size. The description of an automatic system based on computer vision to classify them is the aim of this paper. The proposed system consists of: PC, image processing card with its library, black and white video camera CCD, illumination, data acquisition card and production line. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  In this paper we present the results of a preliminary computer vision system to classify the production of a ceramic tile industry. We focus on the classification of a specific type of tiles whose production can be affected by external factors, such as humidity, temperature, origin of clays and pigments. Variations on these uncontrolled factors provoke small differences in the color and the texture of the tiles that force to classify all the production. A constant and non- subjective classification would allow avoiding devolution from customers and unnecessary stock fragmentation. The aim of this work is to simulate the human behavior on this classification task by extracting a set of features from tile images. These features are induced by definitions from experts. To compute them we need to mix color and texture information and to define global and local measures. In this work, we do not seek a general texture-color representation, we only deal with textures formed by non-oriented colored-blobs randomly distributed. New samples are classified using Discriminant Analysis functions derived from known class tile samples. The last part of the paper is devoted to explain the correction of acquired images in order to avoid time and geometry illumination changes.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. 
4. Abstract of Cited Paper (BIB004):  We present a method of colour shade grading for industrial inspection of random textures, the differences of which are at the threshold of human perception. This method uses image restoration techniques to recover an unblurred version of the image, and then blurs it the same way as the human visual system does, to emulate the process of the image being captured by the human sensor. Subsequently, the colour image is transformed into a perceptually uniform colour space, where colour gra ding takes place. 
5. Abstract of Cited Paper (BIB005):  This paper demonstrates the use of a simple but effective color-based inspection method in parquet slab grading. The approach is to divide the image to small rectangular regions and calculate color percentile features from these areas. Classification is performed in two stages: defect detection and recognition. The recognition results are further used in determining the final grade for the parquet slabs. Comparative results are also presented. 
6. Abstract of Cited Paper (BIB006):  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. 
7. Abstract of Cited Paper (BIB007):  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 industrial quality inspection of colour textured surfaces, such as ceramic tiles or fabrics, it is also important to maintain consistent tonality during production. It is concerned with inspecting consistency among products regarding visual perception. Here, visual perception usually refers to chromatic, textural, or both appearance. Tonality variations, although subtle, can still be discernible once the surfaces are put together. This is therefore another important quality factor. Tonality inspection can be carried out on both uniform pattern surfaces and randomly textured surfaces, but manual detection is not only tiresome but rather difficult. Problems such as spatial and temporal variation of the illumination may introduce effects which make tonality grading even more difficult. There are clearly increasing research on this issue, e.g.   BIB001  BIB003 BIB005  BIB004 BIB006 BIB007 BIB008  BIB002 . In BIB004 , Baldrich et al. segmented the tile image into several stacks using a K-means approach. Then statistical measures were used to represent local and global colour information and segment chromatic and shape characteristics of blobs within each stack. However, this was designed for a specific family of grainy tiles and may not be applicable to other types of randomly textured tiles. In  , Lumbreras et al. used wavelet transforms to assess different colour channels and various decomposition schemes to find appropriate features in order to sort tiles into perceptually homogeneous classes. The feature vectors were classified to the nearest class by using Fisher's linear discriminant function. Similar work has been reported in  , using wavelet analysis in RGB channels. The visual perception concerned with in these works, such as BIB004   , include both textural and chromatic properties. There are also scenarios in which consistency of chromatic characteristics are as predominantly important as for visual perception, for example BIB003 BIB007 . Fig. 2 gives such an example where three surfaces have three different chromatic tonalities with very subtle differences. Kauppinen BIB006 used RGB colour percentile features which were calculated from cumulative histograms to classify wood surfaces. Penaranda et al.  computed the first and second histogram moments of each channel of the RGB colour space as chromatic descriptors to classify tiles according to visual perception. Very recently, Lopez et al. BIB007 BIB008 used higher order histogram moments from each channel in L * a * b * colour space to characterise the colour tonality of ceramic tiles. In BIB001  , Boukouvalas et al. presented spatial and temporal constancy correction of the image illumination on the surfaces of uniform colour and two-colour (fix) patterned tiles. The luminance and the average colours in image channels, such as red, green, and blue, were used to perform tonality grading. Later in BIB003 , the same authors proposed a colour histogram based method to automatically grade colour shade for randomly textured tiles by measuring the difference between the RGB histograms of a reference tile and each newly produced tile. By quantising the pixel values to a small number of bins for each band and employing an ordered binary tree, the 3D histograms were efficiently stored and compared. Several measures were investigated to perform the histogram comparison. Normalised cross correlation was found to be the most appropriate one as it gave the most consistent performance and also had a bounded range. This allowed the a priori definition of thresholds for colour tonality. In BIB005 , the authors applied perceptual smoothing before colour tonality inspection. In  , Xie and Mirmehdi further explored by incorporating local chromatic features to discriminate subtle colour tonality difference. These studies suggested that global measurements, particularly colour histograms and their related statistics are useful in colour tonality defect detection. Smoothing to reduce noise interference (prehistogram computation) has also been found beneficial in colour tonality discrimination BIB005  .