This paper addresses the problems of automatically constructing algebraic surface models from sets of 2-d and3-d images and using these models in pose computation, motion anddeformation estimation, andobject recogn...
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This paper addresses the problems of automatically constructing algebraic surface models from sets of 2-d and3-d images and using these models in pose computation, motion anddeformation estimation, andobjectrecognition. We propose using a combination of constrained optimization and nonlinear least-squares estimation techniques to minimize the mean-squared geometric distance between a set of points or rays and a parameterized surface. In modeling tasks, the unknown parameters are the surface coefficients, while in pose anddeformation estimation tasks they represent the transformation mapping the observer's coordinate system onto the modeled surface's own coordinate system. We have applied this approach to a variety of real range, CT, and video images.
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