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CLC number: TN911.73

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Received: 2005-08-05

Revision Accepted: 2005-12-21

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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.6 P.969~975

http://doi.org/10.1631/jzus.2006.A0969


Mean shift texture surface detection based on WT and COM feature image selection


Author(s):  HAN Yan-fang, SHI Peng-fei

Affiliation(s):  Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200030, China

Corresponding email(s):   hyf@sjtu.edu.cn

Key Words:  Mean shift, Wavelet transform (WT), Co-occurrence matrix (COM), Texture defect detection


HAN Yan-fang, SHI Peng-fei. Mean shift texture surface detection based on WT and COM feature image selection[J]. Journal of Zhejiang University Science A, 2006, 7(6): 969~975.

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journal="Journal of Zhejiang University Science A",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A0969"
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0969

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T1 - Mean shift texture surface detection based on WT and COM feature image selection
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DOI - 10.1631/jzus.2006.A0969


Abstract: 
mean shift is a widely used clustering algorithm in image segmentation. However, the segmenting results are not so good as expected when dealing with the texture surface due to the influence of the textures. Therefore, an approach based on wavelet transform (WT), co-occurrence matrix (COM) and mean shift is proposed in this paper. First, WT and COM are employed to extract the optimal resolution approximation of the original image as feature image. Then, mean shift is successfully used to obtain better detection results. Finally, experiments are done to show this approach is effective.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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