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CVPR
2009
IEEE

Global Optimization for Alignment of Generalized Shapes

15 years 5 months ago
Global Optimization for Alignment of Generalized Shapes
In this paper, we introduce a novel algorithm to solve global shape registration problems. We use gray-scale “images” to represent source shapes, and propose a novel twocomponent Gaussian Mixtures (GM) distance map representation for target shapes. Based on this flexible asymmetric image-based representation, a new energy function is defined. It proves to be a more robust shape dissimilarity metric that can be computed efficiently. Such high efficiency is essential for global optimization methods. We adopt one of them, the Particle Swarm Optimization (PSO), to effectively estimate the global optimum of the new energy function. Experiments and comparison performed on generalized shape data including continuous shapes, unstructured sparse point sets, and gradient maps, demonstrate the robustness and effectiveness of the algorithm.
Hongsheng Li (Lehigh University), Tian Shen (Lehig
Added 09 May 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Hongsheng Li (Lehigh University), Tian Shen (Lehigh University), Xiaolei Huang (Lehigh University)
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