Stochastic computation of medial axis in Markov random fields

In this paper the computation of medial axis is posed as a statistical inference problem not as a mathematical transform. This method provides answers to two essential problems in computing the medial axis representation. I) Prior knowledge are adopted for axes and junctions so that the axes around junctions become well defined. II) A stochastic jump-diffusion process is proposed for estimating medial axis in a Markov random field. We argue that the stochastic algorithm for computing medial axis is compatible with existing algorithms for image segmentation, such as snake and region competition. Thus it provides a new direction for computing medial axis from real textured images. Experiments ale demonstrated on both synthetic and real shapes.

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