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Journal of Applied Mathematics
Volume 2014, Article ID 614613, 11 pages
Research Article

Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information

1College of Information Engineering, Qingdao University, Qingdao 266071, China
2The Affiliated Hospital of Medical College, Qingdao University, Qingdao 266003, China

Received 20 March 2014; Revised 28 May 2014; Accepted 6 June 2014; Published 26 June 2014

Academic Editor: Peter G. L. Leach

Copyright © 2014 Guodong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.