Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation
Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation
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DOI：
10.1109/34.537343
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发表时间：
1996-09-01
影响因子：
23.6
通讯作者：
Yuille, A
中科院分区：
文献类型：
--
作者：
Zhu, SC;Yuille, A
提出了一种基于区域竞争的统计变分图像分割方法。该算法是通过最小化广义贝叶斯/MDL准则使用变分原理。该算法保证收敛到局部最小值，并结合了蛇/气球和区域生长的方面。事实上，经典的蛇/气球和区域生长算法可以直接从我们的方法。我们提供了理论分析的区域竞争，包括边界位置的准确性，初始条件的标准，以及使用过滤器的边缘检测的关系。它是简单的推广算法的多波段分割，我们证明了它的灰度图像，彩色图像和纹理图像。新的颜色模型使我们能够消除强度梯度和阴影，从而获得分割的基础上的对象的阴影。它还有助于检测高光区域。
We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum and combines aspects of snakes/balloons and region growing. Indeed the classic snakes/balloons and region growing algorithms can be directly derived from our approach. We provide theoretical analysis of region competition including accuracy of boundary location, criteria for initial conditions, and the relationship to edge detection using filters. It is straightforward to generalize the algorithm to multiband segmentation and we demonstrate it on gray level images, color images and texture images. The novel color model allows us to eliminate intensity gradients and shadows, thereby obtaining segmentation based on the albedos of objects. It also helps detect highlight regions.