Journal of Applied Mathematics

Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information

Guodong Wang, Zhenkuan Pan, Qian Dong, Ximei Zhao, Zhimei Zhang, and Jinming Duan

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Abstract

Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images.

Article information

Source
J. Appl. Math., Volume 2014 (2014), Article ID 614613, 11 pages.

Dates
First available in Project Euclid: 2 March 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1425305854

Digital Object Identifier
doi:10.1155/2014/614613

Citation

Wang, Guodong; Pan, Zhenkuan; Dong, Qian; Zhao, Ximei; Zhang, Zhimei; Duan, Jinming. Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information. J. Appl. Math. 2014 (2014), Article ID 614613, 11 pages. doi:10.1155/2014/614613. https://projecteuclid.org/euclid.jam/1425305854


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