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Influence of the Noise Model on Level Set Active Contour Segmentation
June 2004 (vol. 26 no. 6)
pp. 799-803

Abstract—We analyze level set implementation of region snakes based on the maximum likelihood method for different noise models that belong to the exponential family. We show that this approach can improve segmentation results in noisy images and we demonstrate that the regularization term can be efficiently determined using an information theory-based approach, i.e., the minimum description length principle. The criterion to be optimized has no free parameter to be tuned by the user and the obtained segmentation technique is adapted to nonsimply connected objects.

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Index Terms:
Segmentation, level-set methods, active contours, minimum description length.
Citation:
Pascal Martin, Philippe R?fr?gier, Fran?ois Goudail, Fr?d?ric Gu?rault, "Influence of the Noise Model on Level Set Active Contour Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 799-803, June 2004, doi:10.1109/TPAMI.2004.11
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