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

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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.

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Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200030, China

    Han Yan-fang & Shi Peng-fei

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  1. Han Yan-fang
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  2. Shi Peng-fei
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Project (No. 035115039) supported by the Scientific Committee of Shanghai, China

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Han, Yf., Shi, Pf. Mean shift texture surface detection based on WT and COM feature image selection. J. Zhejiang Univ. - Sci. A 7, 969–975 (2006). https://doi.org/10.1631/jzus.2006.A0969

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  • DOI: https://doi.org/10.1631/jzus.2006.A0969

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