Nonparametric statistical snake based on the minimum stochastic complexity.

Pascal Martin; Philippe Réfrégier; Frédéric Galland; Frédéric Guérault

doi:10.1109/tip.2006.877317

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Nonparametric statistical snake based on the minimum stochastic complexity.

Pascal Martin·Philippe Réfrégier·Frédéric Galland·Frédéric Guérault

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Abstract

We propose a nonparametric statistical snake technique that is based on the minimization of the stochastic complexity (minimum description length principle). The probability distributions of the gray levels in the different regions of the image are described with step functions with parameters that are estimated. The segmentation is thus obtained by minimizing a criterion that does not include any parameter to be tuned by the user. We illustrate the robustness of this technique on various types of images with level set and polygonal contour models. The efficiency of this approach is also analyzed in comparison with parametric statistical techniques.

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Algorithms Artificial Intelligence Computer Simulation Image Enhancement Image Interpretation, Computer-Assisted Information Storage and Retrieval Models, Statistical Pattern Recognition, Automated Stochastic Processes Journal Article Research Support, Non-U.S. Gov't

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Pascal Martin, Philippe Réfrégier, Frédéric Galland, Frédéric Guérault. 2006. Nonparametric statistical snake based on the minimum stochastic complexity.. https://doi.org/10.1109/tip.2006.877317

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