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Nonlocal active contour model for texture segmentation

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Abstract

Texture segmentation is a very important subject in the fields of computer vision. In order to segment the textures, active contour model based on nonlocal means method and tikhonov regularization is proposed. In detail, a new nonlocal tikhonov regularization smoothness term is added. The nonlocal operator is based on the image slice similarity. So better segmentation accuracy can be achieved for the images which contain special texture features. The good matter of our method is not only nonlocal operator added but also the original tikhonov regularization smoothness item based on the pixel values retained. The nonlocal operator is time consuming, while the reserved smoothness term can save time to some extent. The traditional active contour model can only be used to segment the conventional images. The nonlocal active contour model can dispose the textures well. What’s more, in order to improve the computation efficiency, this paper designs the Split-Bregman algorithm. At last, our performance is demonstrated by segmenting many real texture images.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (No.61305045 and No.61170106), National “Twelfth Five-Year” development plan of science and technology (No.2013BAI01B03), Qingdao science and technology development project (No. 13-1-4-190-jch).

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Authors and Affiliations

  1. College of Computer Science and Technology, Qingdao University, Qingdao, China

    Jingge Lu, Guodong Wang & Zhenkuan Pan

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  1. Jingge Lu
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  2. Guodong Wang
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  3. Zhenkuan Pan
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Correspondence to Guodong Wang.

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Lu, J., Wang, G. & Pan, Z. Nonlocal active contour model for texture segmentation. Multimed Tools Appl 76, 10991–11001 (2017). https://doi.org/10.1007/s11042-016-3462-7

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  • DOI: https://doi.org/10.1007/s11042-016-3462-7

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