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A unified framework for detecting groups and application to shape recognition

Abstract : A unified a contrario detection method is proposed to solve three classical problems in clustering analysis. The first one is to evaluate the validity of a cluster candidate. The second problem is that meaningful clusters can contain or be contained in other meaningful clusters. A rule is needed to define locally optimal clusters by inclusion. The third problem is the definition of a correct merging rule between meaningful clusters, permitting to decide whether they should stay separate or unit. The motivation of this theory is shape recognition. Matching algorithms usually compute correspondences between more or less local features (called shape elements) between images to be compared. This paper intends to form spatially coherent groups between matching shape elements into a shape. Each pair of matching shape elements indeed leads to a unique transformation (similarity or affine map.) As an application, the present theory on the choice of the right clusters is used to group these shape elements into shapes by detecting clusters in the transformation space.
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Submitted on : Friday, May 19, 2006 - 8:04:04 PM
Last modification on : Wednesday, October 27, 2021 - 3:00:00 PM
Long-term archiving on: : Sunday, April 4, 2010 - 8:56:08 PM


  • HAL Id : inria-00070320, version 1


Frédéric Cao, Julie Delon, Agnès Desolneux, Pablo Musé, Frédéric Sur. A unified framework for detecting groups and application to shape recognition. 2005. ⟨inria-00070320⟩



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