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:  Comparative studies 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  We question the role that large scale filter banks have traditionally played in texture classification. It is demonstrated that textures can be classified using the joint distribution of intensity values over extremely compact neighborhoods (starting from as small as 3 /spl times/ 3 pixels square), and that this outperforms classification using filter banks with large support. We develop a novel texton based representation, which is suited to modeling this joint neighborhood distribution for MRFs. The representation is learnt from training images, and then used to classify novel images (with unknown viewpoint and lighting) into texture classes. The power of the method is demonstrated by classifying over 2800 images of all 61 textures present in the Columbia-Utrecht database. The classification performance surpasses that of recent state-of-the-art filter bank based classifiers such as Leung & Malik, Cula & Dana, and Varma & Zisserman. 
3. Abstract of Cited Paper (BIB003):  We present novel simple appearance and shape models that we call epitomes. The epitome of an image is its miniature, condensed version containing the essence of the textural and shape properties of the image. As opposed to previously used simple image models, such as templates or basis functions, the size of the epitome is considerably smaller than the size of the image or object it represents, but the epitome still contains most constitutive elements needed to reconstruct the image. A collection of images often shares an epitome, e.g., when images are a few consecutive frames from a video sequence, or when they are photographs of similar objects. A particular image in a collection is defined by its epitome and a smooth mapping from the epitome to the image pixels. When the epitomic representation is used within a hierarchical generative model, appropriate inference algorithms can be derived to extract the epitome from a single image or a collection of images and at the same time perform various inference tasks, such as image segmentation, motion estimation, object removal and super-resolution. 
4. Abstract of Cited Paper (BIB004):  In this paper, a method of harmonics extraction from Higher Order Statistics (HOS) is developed for texture decomposition. We show that the diagonal slice of the fourth-order cumulants is proportional to the autocorrelation of a related noiseless sinusoidal signal with identical frequencies. We propose to use this fourth-order cumulants slice to estimate a power spectrum from which the harmonic frequencies can be easily extracted. Hence, a texture can be decomposed into deterministic components and indeterministic components as in a unified texture model through a Wold-like decomposition procedure. The simulation and experimental results demonstrated that this method is effective for texture decomposition and it performs better than traditional lower order statistics based decomposition methods. 
5. Abstract of Cited Paper (BIB005):  This article presents a statistical theory for texture modeling. This theory combines filtering theory and Markov random field modeling through the maximum entropy principle, and interprets and clarifies many previous concepts and methods for texture analysis and synthesis from a unified point of view. Our theory characterizes the ensemble of images I with the same texture appearance by a probability distribution f(I) on a random field, and the objective of texture modeling is to make inference about f(I), given a set of observed texture examples.In our theory, texture modeling consists of two steps. (1) A set of filters is selected from a general filter bank to capture features of the texture, these filters are applied to observed texture images, and the histograms of the filtered images are extracted. These histograms are estimates of the marginal distributions of f( I). This step is called feature extraction. (2) The maximum entropy principle is employed to derive a distribution p(I), which is restricted to have the same marginal distributions as those in (1). This p(I) is considered as an estimate of f( I). This step is called feature fusion. A stepwise algorithm is proposed to choose filters from a general filter bank. The resulting model, called FRAME (Filters, Random fields And Maximum Entropy), is a Markov random field (MRF) model, but with a much enriched vocabulary and hence much stronger descriptive ability than the previous MRF models used for texture modeling. Gibbs sampler is adopted to synthesize texture images by drawing typical samples from p(I), thus the model is verified by seeing whether the synthesized texture images have similar visual appearances to the texture images being modeled. Experiments on a variety of 1D and 2D textures are described to illustrate our theory and to show the performance of our algorithms. These experiments demonstrate that many textures which are previously considered as from different categories can be modeled and synthesized in a common framework.

Literature Review:
A classification of the texture analysis techniques used for defect detection is shown in Table 1 . As mentioned earlier, the statistical and filter based methods have been in favour in terms of the amount of research reported. It is also worth noting that the categorisation of the texture analysis techniques used for defect detection as described above and listed in Table 1 is not a crisp classification. There are techniques that combine methods from different categories for texture analysis, e.g. BIB001 applies co-occurrence measurement on wavelet transformed detail images. There are several comparative studies in the literature that evaluate texture analysis methods in application to defect detection. It must be noted that different studies use different datasets and possibly different parameter settings.Özdemir et al.  compared six texture features, consisting of MRF, KL transform, 2D Lattice filters, Laws filters, co-occurrence matrices, and a FFT-based method, for detecting textile defects. Texture modelling using a high (9th) order MRF model gave the best detection result. Iivarinen BIB002 demonstrated LBP and co-occurrence matrices features had similar performance in inspecting textured surfaces. Recently in  , Monadjemi implemented three statistical (histogram-based, LBP, and co-occurrence matrices) and five signal processing schemes (Gabor filters, directional Walsh-Hadamard transform, discrete cosine transform, eigenfilters, and composition of Gabor filters) for randomly textured ceramic tile abnormality detection. The Gabor filter based composition scheme was found to be the most accurate method with good consistent performance across various tile types. Although a solid conclusion can not be drawn to determine the best method for defect detection, it is clearly evident that filtering approaches, in particular Gabor filtering, have been more popularly applied in these areas (cf. Table 1 ). However, an attractive idea is to use local neighbourhood pixel relationships to model the texture, e.g. using methods based on the LBP, MRF, or the epitome and texem models. In fact, multi-channel filtering supports the claim that the joint distribution of neighbouring pixels determines texture appearance, as the joint distribution of pixel values in the filter support window determines the distribution of the filter response  . Notably, Varma and Zisserman BIB003 demonstrated better performance in texture classification using small neighbourhoods than using filter bank-based approaches. Representing texture using primitives is also effective, for example the texton representation. However, due to the difficulties in explicitly deriving primitive representation and associated displacement rules, there are relatively limited works using structural approaches (cf. Table 1 ). As image textures may often contain both statistical and structural properties, a texture analysis method should be able to represent both types of properties in order to completely describe the texture BIB005 . Modelbased texture analysis methods can generally represent both properties, e.g. BIB006 . Statistical models and their estimation have recently been an attractive topic, for example BIB004 .