Simulating the Grassfire Transform Using an Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
FORMS: a flexible object recognition and modeling system
International Journal of Computer Vision
Representation and Self-Similarity of Shapes
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Local Symmetries of Shapes in Arbitrary Dimension
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Hyperbolic "Smoothing" of Shapes
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction
Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction
Stochastic Jump-Diffusion Process for Computing Medial Axes in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
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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 [7] and region competition [18]. Thus it provides a new direction for computing medial axis from real textured images. Experiments are demonstrated on both synthetic and real shapes.