Martin Pascal, Réfrégier Philippe, Galland Frédéric, Guérault Frédéric
Physics and Image Processing Group, Fresnel Institute UMR CNRS 6133, Ecole Généraliste d'Ingénieurs de Marseille, Domaine Universitaire de St Jérôme, 13397 Marseille 20, France.
IEEE Trans Image Process. 2006 Sep;15(9):2762-70. doi: 10.1109/tip.2006.877317.
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.
我们提出了一种基于随机复杂度最小化（最小描述长度原则）的非参数统计蛇形技术。用具有估计参数的阶梯函数描述图像不同区域灰度级的概率分布。通过最小化一个不包含任何需用户调整参数的准则来获得分割结果。我们用水平集和多边形轮廓模型在各种类型的图像上说明了该技术的鲁棒性。与参数统计技术相比，还分析了这种方法的效率。
