Texture image segmentation using statistical active contours

Guowei Gao; Huibin Wang; Chenglin Wen; Lizhong Xu

doi:10.1117/1.JEI.27.5.051211

11 April 2018 Texture image segmentation using statistical active contours

Guowei Gao, Huibin Wang, Chenglin Wen, Lizhong Xu

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 5, 051211 (April 2018). https://doi.org/10.1117/1.JEI.27.5.051211

Abstract

We present texture-based active contours method for two-phase image segmentation in a statistical framework. The proposed method first combines color, texture, and saliency weight to form an augmented image and introduces the joint distribution of these features into the image likelihood term in the energy function. Second, we use the local probability distribution to obtain a smooth label that can reduce the fragmentation in the initialization and evolution of segmentation contours. Finally, we propose a simple and efficient geometric prior based directly on the level sets and introduce the related spatial constraints into the Bayes inference to estimate the smooth probabilistic label. Therefore, the image is represented by high-dimensional features but segmented in low-dimensional space. Furthermore, evolving of the level-set function and updating of the smooth probabilistic label are run alternately in a fast manner. We experimentally compare our texture-based method with others on complicated natural images and demonstrate its good performance in practice.

Citation Download Citation

Guowei Gao, Huibin Wang, Chenglin Wen, and Lizhong Xu "Texture image segmentation using statistical active contours," Journal of Electronic Imaging 27(5), 051211 (11 April 2018). https://doi.org/10.1117/1.JEI.27.5.051211

Received: 21 December 2017; Accepted: 15 March 2018; Published: 11 April 2018

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