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Morphable Surface Models
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DOI：10.1023/A:1008170818506.png)
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Abstract
En 中文
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.
Keywords:
computer vision
learning
correspondence
morphable models
surface matching
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