A Metric Approach to Vector-Valued Image Segmentation

To Appear in International Journal of Computer Vision. Special Issue on Geometrical, Variational and Level Sets Methods in Computer Vision. A Metric Approach to Vector-Valued Image Segmentation

We address the issue of low-level segmentation of vector-valued images, focusing on the case of color natural images. The proposed approach relies on the formulation of the problem in the metric framework, as a Voronoi tessellation of the image domain. In this context, a segmentation is determined by a distance transform and a set of sites. Our method consists in dividing the segmentation task ...

Generalized Voronoi Tessellations for Vector-Valued Image Segmentation

We address the issue of low-level segmentation for vectorvalued 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; t...

Segmentation d’Images Vectorielles par Partitions de Voronoï Généralisées Vector-Valued Image Segmentation by Generalized Voronoi Tessellations

We address the issue of low-level segmentation for vectorvalued 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 ; ...

Vector-valued nonlinear diffusion and its application to image segmentation Ir

Image Segmentation: Type–2 Fuzzy Possibilistic C-Mean Clustering Approach

Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-...