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Ninth IEEE International Conference on Computer Vision (ICCV'03) - Volume 2   p. 923
Landmark-based Shape Deformation with Topology-Preserving Constraints
Song Wang, University of South Carolina
Jim Xiuquan Ji, University of Illinois at Urbana-Champaign
Zhi-Pei Liang, University of Illinois at Urbana-Champaign

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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICCV.2003.1238447
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Abstract
This paper presents a novel approach for landmark-based shape deformation, in which fitting error and shape difference are formulated into a support vector machine (SVM) regression problem. To well describe nonrigid shape deformation, this paper measures the shape difference using a thin-plate spline model. The proposed approach is capable of preserving the topology of the template shape in the deformation. This property is achieved by inserting a set of additional points and imposing a set of linear equality and/or inequality constraints. The underlying optimization problem is solved using a quadratic programming algorithm. The proposed method has been tested using practical data in the context of shape-based image segmentation. Some relevant practical issues, such as missing detected landmarks and selection of the regularization parameter are also briefly discussed.
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Citation:  Song Wang, Jim Xiuquan Ji, Zhi-Pei Liang, "Landmark-based Shape Deformation with Topology-Preserving Constraints," iccv, p. 923,  Ninth IEEE International Conference on Computer Vision (ICCV'03) - Volume 2,  2003