Image Segmentation Using Local Variation and Edge-Weighted Centroidal Voronoi Tessellations

Authors:
Jie Wang;Lili Ju;Xiaoqiang Wang
Affiliations:
Department of Scientific Computing, Florida State University, Tallahassee, FL, USA;Department of Mathematics, University of South Carolina, Columbia, SC, USA;Department of Scientific Computing, Florida State University, Tallahassee, FL, USA
Venue:
IEEE Transactions on Image Processing
Year:
2011

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Abstract

The classic centroidal Voronoi tessellation (CVT) model and its generalizations work quite well at extracting uniformly colored objects, but often fail to handle images with distinct color distribution or strong inhomogeneous intensity. To resolve this problem within the CVT methodology, in this paper we incorporate the information of local variation of colors/intensities and the length of boundaries into the energy functional and develop a new model called the Local Variation and Edge-Weighted Centroidal Voronoi Tessellation (LVEWCVT) for image segmentation. Its mathematical formulation and practical implementations are also discussed and given. We test the LVEWCVT method on various type of segments and also compare it with several state-of-art algorithms using extensive segmentation examples, the results demonstrate excellent performance and competence of the proposed method.