Multiple piecewise constant with geodesic active contours (MPC-GAC) framework for interactive image segmentation using graph cut optimization |
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Authors: | Wenbing Tao Xue-Cheng Tai |
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Affiliation: | a School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Chinab Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637616, Singaporec Department of Mathematics, University of Bergen, 5007 Bergen, Norwayd State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China |
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Abstract: | This paper proposes an improved variational model, multiple piecewise constant with geodesic active contour (MPC-GAC) model, which generalizes the region-based active contour model by Chan and Vese, 2001 11] and merges the edge-based active contour by Caselles et al., 1997 7] to inherit the advantages of region-based and edge-based image segmentation models. We show that the new MPC-GAC energy functional can be iteratively minimized by graph cut algorithms with high computational efficiency compared with the level set framework. This iterative algorithm alternates between the piecewise constant functional learning and the foreground and background updating so that the energy value gradually decreases to the minimum of the energy functional. The k-means method is used to compute the piecewise constant values of the foreground and background of image. We use a graph cut method to detect and update the foreground and background. Numerical experiments show that the proposed interactive segmentation method based on the MPC-GAC model by graph cut optimization can effectively segment images with inhomogeneous objects and background. |
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Keywords: | Multiple piecewise constant Active contour without edges Geodesic active contour Graph cuts Image segmentation Level set |
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