Shape matching and registration by data-driven EM |
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Authors: | Zhuowen Tu Songfeng Zheng Alan Yuille |
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Affiliation: | aLaboratory of Neuro Imaging (LONI), Department of Neurology, UCLA 635 Charles E. Young Drive South, Los Angeles, CA 90095, USA;bDepartment of Statistics, UCLA 8125 Math Sciences Bldg, Los Angeles, CA 90095, USA;cDepartment of Statistics, UCLA 8967 Math Sciences Bldg, Los Angeles, CA 90095, USA |
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Abstract: | In this paper, we present an efficient and robust algorithm for shape matching, registration, and detection. The task is to geometrically transform a source shape to fit a target shape. The measure of similarity is defined in terms of the amount of transformation required. The shapes are represented by sparse-point or continuous-contour representations depending on the form of the data. We formulate the problem as probabilistic inference using a generative model and the EM algorithm. But this algorithm has problems with initialization and computing the E-step. To address these problems, we define a data-driven technique (discriminative model) which makes use of shape features. This gives a hybrid algorithm which combines the generative and discriminative models. The resulting algorithm is very fast, due to the effectiveness of shape-features for solving correspondence requiring only a few iterations. We demonstrate the effectiveness of the algorithm by testing it on standard datasets, such as MPEG7, for shape matching and by applying it to a range of matching, registration, and foreground/background segmentation problems. |
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Keywords: | Shape matching Registration Soft assign EM Shape context |
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