Multiscale segmentation with vector-valued nonlinear diffusions on arbitrary graphs.

Xiaogang Dong; Ilya Pollak

doi:10.1109/tip.2006.873473

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Multiscale segmentation with vector-valued nonlinear diffusions on arbitrary graphs.

Xiaogang Dong·Ilya Pollak

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Abstract

We propose a novel family of nonlinear diffusion equations and apply it to the problem of segmentation of multivalued images. We show that this family can be viewed as an extension of stabilized inverse diffusion equations (SIDEs) which were proposed for restoration, enhancement, and segmentation of scalar-valued signals and images in [39]. Our new diffusion equations can process vector-valued images defined on arbitrary graphs which makes them well suited for segmentation. In addition, we introduce novel ways of utilizing the shape information luring the diffusion process. We demonstrate the effectiveness of our methods on a large number of segmentation tasks.

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Algorithms Artificial Intelligence Computer Simulation Diffusion Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Models, Statistical Nonlinear Dynamics Numerical Analysis, Computer-Assisted Pattern Recognition, Automated Signal Processing, Computer-Assisted Journal Article Research Support, U.S. Gov't, Non-P.H.S.

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Xiaogang Dong, Ilya Pollak. 2006. Multiscale segmentation with vector-valued nonlinear diffusions on arbitrary graphs.. https://doi.org/10.1109/tip.2006.873473

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