Iterated Graph Cuts with Confident Measure

摘要：

In this paper, an iterated graph cuts based image segmentation approach is proposed. Graph cuts method 1 obtains segmentation in an iterative version of optimization framework. However, the graph cuts algorithm may not segment object well because of much interference from inaccurate updated models. The proposed method works with the new updated models of object to reduce the interference significantly. A novel strategy is proposed to update object models, thereby high confident components can be selected using a new confident measure (CM). The experimental performance demonstrates the validity and effectiveness of the proposed method.

关键词： graph cuts image segmentation confident measure

作者: Dongliang Yang Tingquan Deng

作者单位: College of Computer Science and Technology Harbin Engineering University Harbin, China College of Science Harbin Engineering University Harbin, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 1013-1016

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）