A Novel Wold Decomposition Algorithm for Extracting Deterministic Features from Texture Images : With Comparison(Image)

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概要

• 論文の詳細を見る

• 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.

• 社団法人電子情報通信学会の論文
• 2004-04-01

著者

• Hwang Wen-liang

  Institute Of Information Science Academia Sinica

• HSU Taoi

  Department of Graphic Communication Technology, Shih Hsin University

• KUO Jiann-Ling

  School of Defense Science, Chung-Cheng Institute of Technology, National Defense University

• TUNG Der-Kuo

  School of Defense Science, Chung-Cheng Institute of Technology, National Defense University

• Hsu Taoi

  Department Of Graphic Communication Technology Shih Hsin University

• Tung Der-kuo

  School Of Defense Science Chung-cheng Institute Of Technology National Defense University

• Kuo Jiann-ling

  School Of Defense Science Chung-cheng Institute Of Technology National Defense University

関連論文

• A Novel Wold Decomposition Algorithm for Extracting Deterministic Features from Texture Images : With Comparison(Image)

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