Preserving objects in Markov Random Fields region growing image segmentation

被引：6
|
作者：
Dawoud, Amer [1 ]
Netchaev, Anton [1 ]
机构：
[1] Univ So Mississippi, Sch Comp, Hattiesburg, MS 39406 USA
来源：
PATTERN ANALYSIS AND APPLICATIONS | 2012年 / 15卷 / 02期
关键词：
Segmentation; Markov Random Fields; Energy function minimization; Fusion; UNSUPERVISED SEGMENTATION; WATERSHEDS; NOISY;
D O I：
10.1007/s10044-011-0198-x
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes an algorithm that preserves objects in Markov Random Fields (MRF) region growing based image segmentation. This is achieved by modifying the MRF energy minimization process so that it would penalize merging regions that have real edges in the boundary between them. Experimental results show that the integration of edge information increases the precision of the segmentation by ensuring the conservation of the objects contours during the region-growing process.
引用
收藏
页码：155 / 161
页数：7
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