Multi-Objects Interpretation

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Multi-Objects Interpretation
We describe a general-purpose method for the accurate and robust interpretation of a data set of p-dimensional points by several deformable prototypes. This method is based on the fusion of two algorithms: a Generalization of the Iterative Closest Point (GICP) to different types of deformations for registration purposes, and a fuzzy clustering algorithm (FCM). Our method always converges monotonically to the nearest local minimum of a mean-square distance metric, and experiments show that the convergence is fast during the first few iterations. Therefore, we propose a scheme for choosing the initial solution to converge to an "interesting" local minimum. The method presented is very generic and can be applied: - to shapes or objects in a p-dimensional space, - to many shape patterns such as polyhedra, quadrics, snakes, - to many possible shape deformations such as rigid displacements, similitudes, affine and homographic transforms. Consequently, our method has important a...
Jean-Philippe Tarel
Added 09 Nov 2009
Updated 17 Dec 2010
Type Conference
Year 1996
Where ICPR
Authors Jean-Philippe Tarel
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