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Shape Regularized Active Contour Using Iterative Global Search and Local Optimization

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Shape Regularized Active Contour Using Iterative Global Search and Local Optimization
Recently, nonlinear shape models have been shown to improve the robustness and flexibility of segmentation. In this paper, we propose Shape Regularized Active Contour (ShRAC) that incorporates existing nonlinear shape models into the classical active contour approach. ShRAC uses a discrete representation of the contour to allow efficient combinatorial search. The search for optimal contour is performed by a coarse-to-fine algorithm that iterates between combinatorial search and gradient-based local optimization. First, Multi-Solution Dynamic Programming (MSDP) is used to generate initial candidates by minimizing only the image energy. In the second step, a combination of image energy and shape energy determined by a given prior shape model is minimized for the initial candidates using a local optimization method and the best one is selected. To have diverse initial candidates, we employ a Clustered Solution Pruning procedure in the MSDP search space. Finally, Local Shape Regularizatio...
Tianli Yu, Jiebo Luo, Narendra Ahuja
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2005
Where CVPR
Authors Tianli Yu, Jiebo Luo, Narendra Ahuja
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