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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


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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author="HAN Yan-fang, SHI Peng-fei",
journal="Journal of Zhejiang University Science A",
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%DOI 10.1631/jzus.2006.A0969

T1 - Mean shift texture surface detection based on WT and COM feature image selection
A1 - HAN Yan-fang
A1 - SHI Peng-fei
J0 - Journal of Zhejiang University Science A
VL - 7
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SP - 969
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%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2006.A0969

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


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