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...

• Type: Conference
• Year: 1996
• Where: ICPR
• Authors: Jean-Philippe Tarel
• Keywords: Computer Vision, Geometric Modeling, Geometric Registration, ICPR 1996, Model Based Alignment, Shape Alignment