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:  Spatial domain and frequency domain filtering 
1. Abstract of Cited Paper (BIB001):  A theory regarding the information processing strategies in human vision motivates the development of a texture feature space. The feature space is used in texture classification and segmentation problems. A statistic for determining the number of different textures in the image is developed and demonstrated. 
2. Abstract of Cited Paper (BIB002):  For fast inspection of defects in textile fabric the complexity of calculations has to be reduced, in order to limit the system costs. Additionally algorithms which are suitable for migration into hardware have to be chosen. Therefore in the paper a segmentation algorithm using first order statistics is applied. Preprocessing includes a logarithmic greyscale transformation to obtain insensitivity to illumination changes. Afterwards texture features are extracted by a set of linear filters, which consider local neighbourhood relations. The filtered images are evaluated by histograms being calculated on a window grid. Finally the histograms are classified by a perceptron net trained by backpropagation. An interactive teach-in program is provided to adapt the system to different kinds of textile fabric and appearances of defects. > 
3. Abstract of Cited Paper (BIB003):  We present a model of human preattentive texture perception. This model consists of three stages: (1) convolution of the image with a bank of even-symmetric linear filters followed by half-wave rectification to give a set of responses modeling outputs of V1 simple cells, (2) inhibition, localized in space, within and among the neural-response profiles that results in the suppression of weak responses when there are strong responses at the same or nearby locations, and (3) texture-boundary detection by using wide odd-symmetric mechanisms. Our model can predict the salience of texture boundaries in any arbitrary gray-scale image. A computer implementation of this model has been tested on many of the classic stimuli from psychophysical literature. Quantitative predictions of the degree of discriminability of different texture pairs match well with experimental measurements of discriminability in human observers. 
4. Abstract of Cited Paper (BIB004):  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. 
5. Abstract of Cited Paper (BIB005):  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. 
6. Abstract of Cited Paper (BIB006):  When large multivariate datasets are analyzed, it is often desirable to reduce their dimensionality. Principal component analysis is one technique for doing this. It replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables. Often, it is possible to retain most of the variability in the original variables with q very much smaller than p. Despite its apparent simplicity, principal component analysis has a number of subtleties, and it has many uses and extensions. A number of choices associated with the technique are briefly discussed, namely, covariance or correlation, how many components, and different normalization constraints, as well as confusion with factor analysis. Various uses and extensions are outlined. ::: ::: ::: Keywords: ::: ::: dimension reduction; ::: factor analysis; ::: multivariate analysis; ::: variance maximization

Literature Review:
Measuring edge strength and edge frequency is one of the earliest attempts to discriminate different textures. In the spatial domain, the images are usually filtered by gradient filters to extract edges, lines, isolated dots, etc. Sobel, Robert, Canny, Laplacian, Deriche, Laws filters have been routinely used as a precursor to measuring edge density. In BIB004 , Malik and Perona designed a bank of differences of offset Gaussian function filters to model pre-attentive texture perception in human vision. Ade  proposed eigenfilters, a set of masks obtained from the Karhunen-Lóeve (KL) transform BIB007 of local image patches, for texture representation. In BIB005 , Kumar and Pang used linear finite impulse response (FIR) filters to detect defects in textiles. Filter responses from both defect free and defective regions were collected. Then, optimal filters were selected based on discriminant analysis of the filters using objective functions, such as Mahalanobis-Singh and Fisher criterion. Neubauer BIB003 exploited three 5 × 5 FIR filters and performed classification using histograms of features calculated from 10 × 10 pixel regions. Zhou et al. used simple linear filters to capture line-like defects in IC packages. Unser and Ade BIB001 and recently Monadjemi et al. BIB006 employed eigenfilters in defect detection. The authors argued that unlike other spatial operators, eigenfilters are image dependent and the detailed images are orthogonal to each other. Many other methods apply filtering in the frequency domain, particularly when no straightforward kernel can be found in the spatial domain. The image is transformed into the Fourier domain, multiplied with the filter function and then re-transformed into the spatial domain saving on the spatial convolution operation. Ring and wedge filters are some of the most commonly used frequency domain filters. In BIB002 , Coggins and Jain used seven dyadically spaced ring filters and four wedge-shaped orientation filters, which have Gaussian cross sections, for feature extraction. D'Astous and Jernigan  used peak features, such as strength and area, and power distribution features, such as power spectrum eigenvalues and circularity, to discriminate textures. In  , the authors used the Fourier transform to reconstruct textile images for defect detection. The line patterns in a textile image, supposed to be defects, were taken out by removing high energy frequency components in the Fourier domain using a one-dimensional Hough transform. The differences between the restored image and the original image were considered as potential defects. A similar idea was explored in  , but low pass filtering was used to remove the periodic information. Chan and Pang  extracted harmonic peaks from horizontal and vertical power spectrum slices, based on the observation that defects usually occur in horizontal and vertical directions. However, these methods all rely on the assumption that faultless fabric is a repetitive and regular texture. These methods will not be suitable for defect detection in random textures.