The focus of this dissertation is on the texture analysis based on wavelet.After reviewing wavelet theories and classical methods for texture analysis briefly, several new approaches based on wavelet are proposed. First,wavelet pyramid transform is introduced to texture representation.Oneordcr statistics is used to extract texture feature in each detail image for each scale.All features for each scale are collected to represent the texture in original scale.Two principles for wavelet basis selection are also presented by results from experiment. According to the property of this method,a rapid hierarchical technique for texture classification is introduced. Bivariate non-separable compactly supported orthonormal continuous wavelets are first introduced for texture analysis.A simple and fast one-dimension feature filtering segmentation algorithm is proposed to work with non-separable wavelet for multi-scale image segmentation.The method is faster compare to others. After fuzzy C-Means clustering algorithm is well combined with wavelet packet, a new multi-scale image segmentation approach is proposed.The method put focus on coarse scale and small size images first,and then go up to fine scale step by step to get more accurate result until scale zero.In additional,the problem of determining the number of clusters is resolve by a new evaluation algorithm. The performances of the proposed methods are shown in extensive experiments, These experiments also incorporate methods broadly covering most approaches to texture analysis based on wavelet. Finally,a new method for automated integrates circuits chip image segmentation is proposed.In this technique,the large image data are managed quickly.
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