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:  Autocorrelation 
1. Abstract of Cited Paper (BIB001):  This study presents a new method for textures classification based on higher order statistics (HOS). We propose the use of third order correlation tools for texture analysis. We compare the performance of three different tools: the bicorrelation in the spatial domain, the bispectrum in the frequency domain and the bicorspectrum which is a spatial/frequency representation in that case. We test classification on representative textures of Brodatz album. 
2. Abstract of Cited Paper (BIB002):  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.

Literature Review:
The autocorrelation feature is derived based on the observation that some textures are repetitive in nature, such as textiles. It measures the correlation between the image itself and the image translated with a displacement vector. Textures with strong regularity will exhibit peaks and valleys in the autocorrelation measure. It is closely related to the power spectrum of the Fourier transform. This second order statistic can be sensitive to noise interference. Higher order statistics, e.g. BIB002 BIB003 , have been investigated, for example, Huang and Chan BIB003 used fourth-order cumulants to extract harmonic peaks and demonstrated its ability to localise defects in textile images and Wood BIB001 used autocorrelation of subimages to detect textile defects. Nevertheless, the autocorrelation function is generally considered as unsuitable for random textures with irregularly arranged textural elements.