Image segmentation using fuzzy energy-based active contour with shape prior |
| |
Affiliation: | 1. School of Automation, Northwestern Polytechnical University, Youyi West Road No. 127, Shanxi, Xi’an, 710072 China;2. Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8 Canada;3. The Institute of Artificial Intelligence and Robotic, Xi’an Jiaotong University, Xianning West Road No. 28, Shanxi, Xi’an, 710049 China;4. National Key Laboratory of UVA Technology, Northwestern Polytechnical University, Youyi West Road No. 127, Shanxi, Xi’an, 710072 China |
| |
Abstract: | This paper presents a fuzzy energy-based active contour model with shape prior for image segmentation. The paper proposes a fuzzy energy functional including a data term and a shape prior term. The data term, inspired from the region-based active contour approach proposed by Chan and Vese, evolves the contour relied on image information. The shape term inspired from Chan and Zhu’s work, defined as the distance between the evolving shape and a reference one, constrains the evolving contour with respect to the reference shape. To align the shapes, we exploit the shape normalization procedure which takes into account the affine transformation. In addition, to minimize the energy functional, we utilize a direct method to calculate the energy alterations. The proposed model therefore can deal with images with background clutter and object occlusion, improves the computational speed, and avoids difficulties associated with time step selection issue in gradient descent-based approaches. |
| |
Keywords: | Image segmentation Shape prior Shape normalization Level set method Fuzzy energy Moment-based alignment Active contour models PCA |
本文献已被 ScienceDirect 等数据库收录! |
|