Abstract
Image segmentation plays a very important pole in image processing and computer vision field. Most of the energy minimization of level set methods are based on the steepest descent method and finite difference scheme. In this paper, we propose a sweeping algorithm to minimize Local Chan Vese (LCV) model. We calculate the energy change when a pixel is moved from the outside region to the inside region of evolving curves and vice versa, instead of directly solving the Euler-Lagrange equation. The algorithm is fast and robust to initial level set contour and can avoid solving partial differential equation. There is no need for the re-initialization step, any stability conditions and the distance regularization term. The experiments have shown the effectiveness of the proposed algorithm.
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Acknowledgements
The authors would like to express their thanks to Dr. Y. Boutiche, the author of reference [8], for discussing the algorithm of sweeping principle of CV model. This work was supported by the grant of the National Natural Science Foundation of China, No. 61672204, the Project of National Science and Technology Support Plan of China, No. 2015BAD18B05, the grant of Major Science and Technology Project of Anhui Province, No. 17030901026, the grant of the key Scientific Research Foundation of Education Department of Anhui Province, Nos. KJ2018A0555, KJ2016A603, KJ2017A152, KJ2017A542, the grant of Key Constructive Discipline Project of Hefei University, No. 2016xk05, Excellent Talents Training Funded Project of Universities of Anhui Province, No. gxfx2017099.
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Zou, L. et al. (2018). A Fast Algorithm for Image Segmentation Based on Local Chan Vese Model. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_7
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DOI: https://doi.org/10.1007/978-3-319-95933-7_7
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