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:  Visual inspection via novelty detection 
1. Abstract of Cited Paper (BIB001):  In this paper we present a machine vision system for the automatic inspection of defects in textured surfaces found in industry. The defects to be inspected are those that appear as local anomalies embedded in a homogeneous texture. The proposed method is based on a Gabor filtering scheme that computes the output response of energy from the convolution of a textured image with a specific Gabor filter. The best parameters of a Gabor filter are selected so that the energy of the homogeneous texture is zero, and any unpredictable defeats will generate significantly large energy values. A simple thresholding scheme then follows to discriminate between homogeneous regions and defective regions in the filtered image. This transforms texture differences into detectable filter output. The experiments on structural textures such as leather and sandpaper have shown the effectiveness of the proposed method. 
2. Abstract of Cited Paper (BIB002):  The problem of automated defect detection in textured mate- rials is investigated. A new algorithm based on multichannel filtering is presented. The texture features are extracted by filtering the acquired image using a filter bank consisting of a number of real Gabor functions, with multiple narrow spatial frequency and orientation channels. For each image, we propose the use of image fusion to multiplex the infor- mation from sixteen different channels obtained in four orientations. Adaptive degrees of thresholding and the associated effect on sensitivity to material impurities are discussed. This algorithm realizes large com- putational savings over the previous approaches and enables high- quality real-time defect detection. The performance of this algorithm has been tested thoroughly on real fabric defects, and experimental results have confirmed the usefulness of the approach. © 2000 Society of Photo- Optical Instrumentation Engineers. (S0091-3286(00)01912-7) 
3. Abstract of Cited Paper (BIB003):  This paper investigates various approaches for automated inspection of textured materials using Gabor wavelet features. A new supervised defect detection approach to detect a class of defects in textile webs is proposed. Unsupervised web inspection using a multichannel filtering scheme is investigated. A new data fusion scheme to multiplex the information from the different channels is proposed. Various factors interacting the tradeoff for performance and computational load are discussed. This scheme establishes high computational savings over the previously proposed approaches and results in high quality of defect detection. Final acceptance of visual inspection systems depends on economical aspects as well. Therefore, a new low-cost solution for fast web inspection is also included in this paper. The experimental results conducted on real fabric defects for various approaches proposed in this paper confirm their usefulness. 
4. Abstract of Cited Paper (BIB004):  Abstract The task of detecting flaws in woven textiles can be formulated as the problem of segmenting a “known” non-defective texture from an “unknown” defective texture. In order to discriminate defective texture pixels from non-defective texture pixels, optimal 2-D Gabor filters are designed such that, when applied to non-defective texture, the filter response maximises a Fisher cost function. A pixel of potentially flawed texture is classified as defective or non-defective based on the Gabor filter response at that pixel. The results of this optimised Gabor filter classification scheme are presented for 35 different flawed homogeneous textures. These results exhibit accurate flaw detection with low false alarm rate. Potentially, our novel optimised Gabor filter method could be applied to the more complicated problem of detecting flaws in jacquard textiles. This second and more difficult problem is also discussed, along with some preliminary results. 
5. Abstract of Cited Paper (BIB005):  The problem of automated defect detection in textured materials is investigated. A new approach for defect detection using linear FIR filters with optimized energy separation is proposed. The performance of different feature separation criteria with reference to fabric defects has been evaluated. The issues relating to the design of optimal filters for supervised and unsupervised web inspection are addressed. A general web inspection system based on the optimal filters is proposed. The experiments on this new approach have yielded excellent results. The low computational requirement confirms the usefulness of the approach for industrial inspection. 
6. Abstract of Cited Paper (BIB006):  Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information about objects that were not known at the time of training the model. In this paper we provide state-of-the-art review in the area of novelty detection based on statistical approaches. The second part paper details novelty detection using neural networks. As discussed, there are a multitude of applications where novelty detection is extremely important including signal processing, computer vision, pattern recognition, data mining, and robotics. 
7. Abstract of Cited Paper (BIB007):  Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. In this paper we focus on neural network-based approaches for novelty detection. Statistical approaches are covered in Part 1 paper. 
8. Abstract of Cited Paper (BIB008):  A new eigenfilter-based novelty detection approach to find abnormalities in random textures is presented. The proposed algorithm reconstructs a given texture twice using a subset of its own eigenfilter bank and a subset of a reference (template) eigenfilter bank, and measures the reconstruction error as the level of novelty. We then present an improved reconstruction generated by structurally matched eigenfilters through rotation, negation, and mirroring. We apply the method to the detection of defects in textured ceramic tiles. The method is over 90% accurate, and is fast and amenable to implementation on a production line. 
9. Abstract of Cited Paper (BIB009):  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:
In a novelty detection task, the classifier's task is to identify whether an input pattern is part of the data or it is in fact unknown. As for defect detection, it involves assigning a "normal" or "abnormal" label to a pattern (e.g. a surface or a pixel). Contrary to supervised classification, novelty detection only needs the normal samples for training purposes and usually uses a distance measure and a threshold for decision making. Recently, Markou and Singh BIB007 BIB008 gave a detailed review of novelty detection approaches, using statistical and neural network-based approaches. Statistical parametric approaches are commonly used in visual inspection, for example BIB001 BIB002 BIB003 BIB005 BIB009 . The fundamental assumption is that the data distribution is Gaussian in nature. Thus, it can be easily statistically modelled based on means and covariances. As misclassifications can not be used as a criterion for the performance of a classifier as in a supervised manner, the available performance measure for novelty detection methods is the probability of false positives, that is rejection of good samples. Increasing the acceptance decision boundary will then obviously decrease the risk. However, it is also clear that the probability of false negatives depends on the acceptance region. Thus, it is usual to set the minimum acceptance region according to a fixed false positive probability. For example, in a parametric classifier, the decision boundary can be set as µ ± kσ with k = 2 or k = 3, which corresponds to 5.0% and 0.3% expected false positive rate. In some applications, the decision boundary is simply set as the maximum range of normal samples in the training stage, e.g. BIB004 BIB006 . Probabilistic approaches, e.g. Gaussian mixture models, use kernel functions to estimate general distribution of training patterns. Each pattern is usually represented as a point in a d-dimensional feature space, where d is the length of the feature vector. The parameters of the model are determined by maximising the likelihood of the training data, usually through Expectation Maximisation (EM) algorithms. The objective is then to establish decision boundaries in the feature space and reject patterns that fall in regions of low density. The decision boundaries are determined by the probability distribution of the patterns at training stage. Thus, they can be conveniently computed by examining data likelihoods. In BIB010 , two different mixture models are used to measure the pattern likelihoods. Novelty detection is then accomplished by using simple parametric thresholding, determined automatically from training data.