Matching Structures by Computing Minimal Paths on a Manifold - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Journal of Visual Communication and Image Representation Year : 2002

Matching Structures by Computing Minimal Paths on a Manifold

Abstract

The general problem of matching structures is very pervasive in computer vision and image processing. The research presented here tackles the problem of object matching in a very general perspective. It is formulated for the matching of surfaces. It applies to objects having small or large deformation and arbitrary topological changes. The process described hinges on a geodesic distance equation for a family of curves or surfaces embedded in the graph of a cost function. This geometrical approach to object matching has the advantage that the similarity criterion can be used to define the shape of the cost function. Matching paths are computed on the cost manifolds using distance maps. These distance maps are generated by solving a general partial differential equation which is a generalization of the geodesic dis- tance evolution scheme introduced by R. Kimmel, A. Amir, and A. F. Bruckstein (1995, IEEE Trans. Pattern Anal. Mach. Intell. 17, 635-640). An Eulerian level-set formulation is also introduced, leading to a numerical scheme used for solving par- tial differential equations originating from hyperbolic conservation laws, which has proven to be very robust and stable.
Fichier principal
Vignette du fichier
jvcomirep.pdf (173.98 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00948350 , version 1 (18-02-2014)

Identifiers

Cite

Etienne Huot, Hussein Yahia, Isaac Cohen, Isabelle Herlin. Matching Structures by Computing Minimal Paths on a Manifold. Journal of Visual Communication and Image Representation, 2002, 13 (1), pp.302-312. ⟨10.1006/jvci.2001.0485⟩. ⟨hal-00948350⟩
151 View
153 Download

Altmetric

Share

Gmail Facebook X LinkedIn More