Morphable Surface Models |
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Authors: | Shelton Christian R |
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Affiliation: | (1) Center for Biological and Computational Learning, Artificial Intelligence Laboratory, M.I.T., Cambridge, MA, USA |
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Abstract: | We describe a novel automatic technique for finding a dense correspondence between a pair of n-dimensional surfaces with arbitrary topologies. This method employs a different formulation than previous correspondence algorithms (such as optical flow) and includes images as a special case. We use this correspondence algorithm to build Morphable Surface Models (an extension of Morphable Models) from examples. We present a method for matching the model to new surfaces and demonstrate their use for analysis, synthesis, and clustering. |
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Keywords: | computer vision learning correspondence morphable models surface matching |
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