Approximately Global Optimization for Robust Alignment of Generalized Shapes

—In this paper, we introduce a novel method to solve shape alignment problems. We use gray-scale “images” to represent source shapes, and propose a novel two-component Gaussian Mixture (GM) distance map representation for target shapes. This asymmetric representation is a flexible image-based representation which is able to represent different kinds of shape data, including continuous contours, unstructured sparse point sets, edge maps, and even gray-scale gradient maps. Using this representation, a new energy function based on a novel two-component Gaussian Mixture distance model is proposed. The new energy function was empirically evaluated to be a more robust shape dissimilarity metric that can be computed efficiently. Such high efficiency is essential for global optimization methods. We adopt and modify one of them, the Particle Swarm Optimization (PSO), to effectively estimate the global optimum of the new energy function. Differently from the original PSO, several new strat...

• Type: Journal
• Year: 2011
• Where: PAMI
• Authors: Hongsheng Li, Tian Shen, Xiaolei Huang
• Keywords: Operations Research, PAMI 2011, Shape Alignment Problems, Shape Data, Two-component Gaussian Mixture