A fast segmentation method based on constraint optimization and its applications: Intensity inhomogeneity and texture segmentation

Authors:
Jun Liu;Xue-cheng Tai;Haiyang Huang;Zhongdan Huan
Affiliations:
School of Mathematical Sciences, Laboratory of Mathematics and Complex Systems, Beijing Normal University, Beijing 100875, PR China;Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, 637616, Singapore and Department of Mathematics, University of Bergen, Johannes B ...;School of Mathematical Sciences, Laboratory of Mathematics and Complex Systems, Beijing Normal University, Beijing 100875, PR China;School of Mathematical Sciences, Laboratory of Mathematics and Complex Systems, Beijing Normal University, Beijing 100875, PR China
Venue:
Pattern Recognition
Year:
2011

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Abstract

We propose a new constraint optimization energy and an iteration scheme for image segmentation which is connected to edge-weighted centroidal Voronoi tessellation (EWCVT). We show that the characteristic functions of the edge-weighted Voronoi regions are the minimizers (may not unique) of the proposed energy at each iteration. We propose a narrow banding algorithm to accelerate the implementation, which makes the proposed method very fast. We generalize the CVT segmentation to hand intensity inhomogeneous and texture segmentation by incorporating the global and local image information into the energy functional. Compared with other approaches such as level set method, the experimental results in this paper have shown that our approach greatly improves the calculation efficiency without losing segmentation accuracy.