29 August 2016 Texture segmentation based on nonlinear compact multi-scale structure tensor and TV-flow
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Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100331A (2016); doi: 10.1117/12.2243960
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
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.
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Wei Xu, Shou-Dong Han, Yu-Chen Peng, "Texture segmentation based on nonlinear compact multi-scale structure tensor and TV-flow", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100331A (29 August 2016); doi: 10.1117/12.2243960; https://doi.org/10.1117/12.2243960
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KEYWORDS
Image segmentation

Nonlinear filtering

Dimension reduction

Distributed interactive simulations

Principal component analysis

Diffusion

Distance measurement

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