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A Novel Wold Decomposition Algorithm for Extracting Deterministic Features from Texture Images: With Comparison
Taoi HSU Wen-Liang HWANG Jiann-Ling KUO Der-Kuo TUNG
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2004/04/01
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Wold decomposition, evanescent, singularity detection, texture analysis, multiresolution Lebesgue decomposition,
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In this paper, a novel Wold decomposition algorithm is proposed to address the issue of deterministic component extraction for texture images. This algorithm exploits the wavelet-based singularity detection theory to process both harmonic a nd evanescent features from frequency domain. This exploitation is based on the 2D Lebesgue decomposition theory. When applying multiresolution analysis techniq ue to the power spectrum density (PSD) of a regular homogeneous random field, its indeterministic component will be effectively smoothed, and its deterministic component will remain dominant at coarse scale. By means of propagating these positions to the finest scale, the deterministic component can be properly extracted. From experiment, the proposed algorithm can obtain results that satisfactorily ensure its robustness and efficiency.