Title of article :
A Metric Approach to Vector-Valued Image Segmentation
Author/Authors :
PABLO A. ARBELA´ EZ AND LAURENT D. COHEN، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2006
Pages :
8
From page :
119
To page :
126
Abstract :
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 in two successive sub-tasks: pre-segmentation and hierarchical representation.We design specific distances for both sub-problems by considering low-level image attributes and, particularly, color and lightness information. Then, the interpretation of the metric formalism in terms of boundaries allows the definition of a soft contour map that has the property of producing a set of closed curves for any threshold. Finally, we evaluate the quality of our results with respect to ground-truth segmentation data
Keywords :
Boundary detection , vector-valued image , path variation , Distance transforms , Color , image segmentation , ultrametrics
Journal title :
INTERNATIONAL JOURNAL OF COMPUTER VISION
Serial Year :
2006
Journal title :
INTERNATIONAL JOURNAL OF COMPUTER VISION
Record number :
828207
Link To Document :
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