SEGMENTATION D’IMAGES VECTORIELLES PAR PARTITIONS DE VORONOI GENERALISEES
P. ARBELAEZ AND L. COHEN
ACTES 14EME CONGRES FRANCOPHONE DE RECONNAISSANCE DES FORMES ET INTELLIGENCE ARTIFICIELLE (RFIA’04). JANVIER 2004. TOULOUSE, FRANCE
We address the issue of low-level segmentation for vector- valued images, focusing on color images. The proposed approach relies on the formulation of the problem as a generalized Voronoi tessellation of the image domain. In this context, the issue is transferred to the definition of an appropriated pseudo-metric and the selection of a set of sources. Two types of pseudo-metrics are considered ; the first one is based on energy minimizing paths and the se- cond is associated to the families of nested partitions of the image domain. We discuss specific applications of our approach to pre-segmentation, edge detection and hierar- chical segmentation on color images.