所属单位：信息与控制工程学院

发表刊物：International Journal for Light and Electron Optics

关键字：中文关键字：自适应循环置信度传播；马尔可夫随机场；局部交互区域MRF；消息自收敛；标号修剪，英文关键字：Adaptive loopy belief propagation；Markov random fi

摘要：Loopy belief propagation (LBP) algorithm over pairwise-connected Markov random fields (MRFs) has become widely used for low-level vision problems. However, Pairwise MRF is often insufficient to capture the statistics of natural images well, and LBP is still extremely slow for application on an MRF with large discrete label space. To solve these problems, the present study proposes a new segmentation algorithm based on adaptive LBP. The proposed algorithm utilizes local region information to construct a local region model, as well as a local interaction region MRF model for image segmentation. The adaptive LBP algorithm maximizes the global probability of the proposed MRF model, which employs two very important strategies, namely, “message self-convergence” and “adaptive label pruning”. Message selfconvergence can improve the reliability of a pixel in choosing a label in local region, and label pruning can dismiss impossible labels for every pixel. Thus, the most reliable information messages transfer through the LBP algorithm. The experimental results show that the proposed algorithm not only obtains more accurate segmentation results but also greater speed.

备注：徐胜军

合写作者：韩九强,于军棋

第一作者：赵亮,徐胜军

论文类型：期刊论文

卷号：卷：124

期号：期：22

页面范围：页：5732– 5738

是否译文：否

发表时间：2013-11-01