A Fast Edge Tracking Algorithm For Image Segmentation Using A Simple Markov Random Field Model

摘要：

This paper presents an fast edge tracking algorithm for reducing the computation time of unsupervised image segmentation using a simple Markov random field model (MRF). The classical two-component MRF (CMRF) based image segmentation algorithm is time-consuming for sweeping image and repeat computing all labels at each iteration process. However, most of labels remain unchanged from an iteration to the next. So most of computations are redundant and contribute nothing to the final segmentation. The proposed algorithm works by tracking edge rather than all pixels and computing their labels at each iteration. The algorithm is simple, easy to implement but fast. Experimental results show that, compare to the image segmentation algorithm based on CMRF method, the proposed algorithms can substantially reduce the computation time but the segmentation results are comparable.

关键词： Image segmentation Markov random field edge tracking

作者: Feiyue He Zheng Tian Xiangzeng Liu Xifa Duan

作者单位: School of Science, Northwestern Polytechnical University School of Science, Xian Polytechnic Univer School of Science, Northwestern Polytechnical University Xian Microelectronics Technology Institute Xian, China

会议类型: 国际会议

会议名称: 2012 International Conference on Computer Science and Electronic Engineering(2012 IEEE计算机科学与电子工程国际会议 ICCSEE 2012)

会议地点: 杭州

会议语种:英文

页码: 633-636

在线出版日期: 2012-03-23（万方平台首次上网日期，不代表论文的发表时间）