Delyon Guillaume, Galland Frédéric, Réfrégier Philippe
IEEE Trans Image Process. 2006 Oct;15(10):3207-12. doi: 10.1109/tip.2006.877484.
We present a generalization of a new statistical technique of image partitioning into homogeneous regions to cases where the family of the probability laws of the gray-level fluctuations is a priori unknown. For that purpose, the probability laws are described with step functions whose parameters are estimated. This approach is based on a polygonal grid which can have an arbitrary topology and whose number of regions and regularity of its boundaries are obtained by minimizing the stochastic complexity of the image. We demonstrate that efficient homogeneous image partitioning can be obtained when no parametric model of the probability laws of the gray levels is used and that this approach leads to a criterion without parameter to be tuned by the user. The efficiency of this technique is compared to a statistical parametric technique on a synthetic image and is compared to a standard unsupervised segmentation method on real optical images.
我们将一种新的图像分割统计技术推广到灰度级波动概率律族先验未知的情况。为此，用参数可估计的阶梯函数来描述概率律。该方法基于一个多边形网格，其拓扑结构可以是任意的，通过最小化图像的随机复杂度来获得区域数量及其边界的规则性。我们证明，在不使用灰度级概率律的参数模型时，可以实现高效的均匀图像分割，并且该方法产生的准则无需用户调整参数。将该技术的效率与合成图像上的统计参数技术进行比较，并与真实光学图像上的标准无监督分割方法进行比较。
