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Proceedings Paper

Texture segmentation based on nonlinear compact multi-scale structure tensor and TV-flow
Author(s): Wei Xu; Shou-Dong Han; Yu-Chen Peng
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Paper Abstract

This paper proposes an interactive texture segmentation method based on GrabCut. In order to extract the texture features effectively, a new texture descriptor is designed by integrating the nonlinear compact multi-scale structure tensor (NCMSST) and total variation flow (TV-flow). NCMSST is constructed by means of dimension reduction and nonlinear filtering for the traditional multi-scale structure tensor (MSST), and TV-flow is used to compensate the loss of large-scale texture descriptive ability by extracting local scale information. Then, the GrabCut framework is applied to deal with the texture image segmentation, and the corresponding experiment results demonstrate the superiority of our proposed texture descriptor in terms of high efficiency and accuracy.

Paper Details

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100331A (29 August 2016); doi: 10.1117/12.2243960
Show Author Affiliations
Wei Xu, Huazhong Univ. of Science and Technology (China)
Shou-Dong Han, Huazhong Univ. of Science and Technology (China)
Yu-Chen Peng, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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