Statistical region merging
Statistical region merging
复制标题
DOI：
10.1109/tpami.2004.110
复制
发表时间：
2004-11-01
影响因子：
23.6
通讯作者：
Nielsen, F
中科院分区：
文献类型：
--
作者：
Nock, R;Nielsen, F
本文探讨了计算机视觉中经常描述的一个过程的统计基础：通过区域合并在区域选择中遵循特定顺序进行图像分割。我们展示了算法和统计的特殊混合，其分割误差是，正如我们所示，从定性和定量的角度来看都是有限的。这种方法可以在线性时间/空间中有效地近似，从而产生一种适合处理使用最常见的数字像素属性空间描述的图像的快速分割算法。该方法概念简单，易于修改和处理硬噪声损坏、处理遮挡、授权分割尺度控制以及处理球形图像等非常规数据。灰度和彩色图像的实验，用一个简短的现成的c代码获得，显示所获得的分割质量。
This paper explores a statistical basis for a process often described in computer vision: image segmentation by region merging following a particular order in the choice of regions. We exhibit a particular blend of algorithmics and statistics whose segmentation error is, as we show, limited from both the qualitative and quantitative standpoints. This approach can be efficiently approximated in linear time/space, leading to a fast segmentation algorithm tailored to processing images described using most common numerical pixel attribute spaces. The conceptual simplicity of the approach makes it simple to modify and cope with hard noise corruption, handle occlusion, authorize the control of the segmentation scale, and process unconventional data such as spherical images. Experiments on gray-level and color images, obtained with a short readily available C-code, display the quality of the segmentations obtained.