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:  Introduction 
1. Abstract of Cited Paper (BIB001):  Machine vision and automatic surface inspection has been an active field of research during the last few years. However, very little research has been contributed to the area of defect detection in textured images, especially for the case of random textures. In this paper, we propose a novel algorithm that uses colour and texture information to solve the problem. A new colour clustering scheme based on human colour perception is developed. The algorithm is training based and produces very promising results on defect detection in random textured images and in particular, granite images. 
2. Abstract of Cited Paper (BIB002):  Abstract Precision inspection has been widely used in manufacturing to measure the dimensional accuracy of parts and products to meet the quality requirements. For regular geometric features, coordinate-measuring machines (CMM) can be used effectively to assess the accuracy and tolerances. For parts with free-form surfaces, the inspection becomes complex. Therefore, numerous researches have been carried out to tackle both fundamental and application issues concerning free-form surface inspection. In addition to academic research, some commercial packages have also been developed. This paper provides a comprehensive literature review of methodologies, techniques and various processes of inspections of parts with free-form surfaces. The specific topics cover: measurement data acquiring methods including contact and non-contact measurement approaches; inspection planning; geometric description methods of design models or measurement data; and, the free-form surface localization and comparison techniques, which are emphasized in this paper and mainly include the establishment of corresponding relationship, 3D transformation solving, measurement data to design model comparison or surface to surface distance calculations. Other issues, such as the influence factors to the localization/registration process, definition and inspection of free-form surface tolerance, and discussions on the functions of some commercial inspection packages available on market, are also discussed. 
3. Abstract of Cited Paper (BIB003):  This paper is concerned with the problem of automatic inspection of ceramic tiles using computer vision. The Detection of defects is carried out in three stages: Feature Extraction, Feature Description and Classification. Suitable feature extraction and description improves the performance of inspection system. A number of feature extractors and descriptors are proposed in literature. We selected Laws Texture Energy Measures as feature extractor and descriptor for texture upon ceramic tiles. Then we investigated the suitability of each energy measure of Laws Texture Energy Measures. After experimenting with feature extracted from number of tiles, we define a relationship b/w defect and corresponding suitable energy measures. 
4. Abstract of Cited Paper (BIB004):  AbstractThe present paper presents a Gabor filtering approach for the automatic inspection of defects in coloured texture surfaces. It can simultaneously measure both chromatic and textural anomalies in an image. Two chromatic features derived from the CIE–L*a*b* colour space are used to form a complex number for colour pixel representation. The proposed method is based on the energy response from the convolution of a Gabor filter with the colour image characterized by two chromatic features in the form of a complex number. The Gabor filtering process converts the difficult defect detection in a coloured texture image into simple threshold segmentation in the filtered image. Experimental results from a number of coloured texture surfaces such as textile fabric, wood and tile have shown the effectiveness of the proposed method. 
5. Abstract of Cited Paper (BIB005):  We present an approach to detecting and localizing defects in random color textures which requires only a few defect free samples for unsupervised training. It is assumed that each image is generated by a superposition of various-size image patches with added variations at each pixel position. These image patches and their corresponding variances are referred to here as textural exemplars or texems. Mixture models are applied to obtain the texems using multiscale analysis to reduce the computational costs. Novelty detection on color texture surfaces is performed by examining the same-source similarity based on the data likelihood in multiscale, followed by logical processes to combine the defect candidates to localize defects. The proposed method is compared against a Gabor filter bank-based novelty detection method. Also, we compare different texem generalization schemes for defect detection in terms of accuracy and efficiency.

Literature Review:
Non-destructive visual inspection for texture and/or colour abnormalities has application on a variety of surfaces e.g. wood, steel, wafer, ceramics, and even non-flat objects such as fruits and aircraft surfaces, and is highly demanded by industry in order to replace the subjective and repetitive process of manual inspection. For example, in ceramic tile production, chromato-textural properties of the final product can be affected by a variety of external factors that are difficult to control, such as colour pigments, humidity, and temperature. Thus, online monitoring and feedback control of the whole production line becomes desirable. There are numerous reported works in the past two decades during which computer vision based inspection has become one of the most important application areas. Chin  and Newman and Jain  provided a comprehensive overview of surface inspection in late eighties and mid nineties, respectively. Recently, Li and Gu BIB003 surveyed recent advances in free form surface inspection. However, there have been significant advances in all aspects of surface inspection using computer vision in the recent years. There are also newly emerging topics such as tonality inspection and increasing use of colour imaging devices which requires algorithms efficiently deal with vector-valued data. This paper focuses on the recent developments in vision based surface inspection using image processing techniques, particularly those that are based on texture analysis methods. The visual inspection process often involves texture and/or colour analysis and pattern classification (decision making). The former is mainly concerned with feature representation and extraction, as well as data perception and modelling. The latter consists of pattern representation, cluster analysis, and discriminant analysis. We discuss the texture feature extraction and analysis in four categories, namely statistical approaches, structural approaches, filter based methods, and model based approaches. A categorised list of representative works are given as pointers to these methods discussed in this paper. Some recent applied comparative studies are also reviewed. However, it is worth noting that surface inspection using texture analysis should not be considered the same as general texture segmentation and classification. Both defect-free and defective areas of inspected surfaces can be texturally unstationary, i.e. they will be often further segmented into smaller regions while in defect detection the defective region should be treated as a whole no matter how unstationary it is. Classifying surfaces into defect-free and defective is also different from texture classification, as defective samples are not necessary form a single class and the defect types may only be partially predicated beforehand. Additionally, in some applications false positives (rejecting good samples) is more forgivable than false negative (missing defective regions or samples). A significant differentiating factor in visual inspection approaches is that of supervised classification versus novelty detection. For applications where both normal and defective samples can be easily obtained and pre-defined, supervised classification based approaches are usually favoured. However, when defects are unpredictable and defective samples are unavailable, novelty detection is more desirable. Both of these approaches will be reviewed and compared in the context of defect detection. Aside from inspecting textural faults, inspecting tonality defects in terms of overall visual impression is also a significant production quality factor. The variation of overall colour or textural characteristics from surface to surface is known as the tonality problem. Any changes in the tonality, however subtle, will still become BIB001 BIB002    BIB004 BIB005 BIB006 significant once the surfaces are placed together. The problem is compounded when the surface of the object is not just plain-coloured, but highly textured. In applications like tile production it is important to maintain the tonality consistency. The rest of the paper is organised as follows. As the techniques reviewed in textural defect detection generally include those that have been used in tonality inspection, textural defect detection and related texture analysis techniques are first discussed in Section 2. Tonality inspection is then reviewed later in Section 3. The ability for a texture analysis technique to be extendible to deal with colour images is particularly important for applications using colour imaging. Thus, colour texture analysis is separately discussed in Section 4. Section 5 compares classification oriented approaches with novelty detection based approaches. Finally, Section 6 summaries this paper.