文献类型：期刊文献

英文题名：An image segmentation method for chinese paintings by combining deformable models with graph cuts

作者：He, Ning[1]; Lu, Ke[2]

第一作者：何宁

机构：[1] School of Information, Beijing Union University, Beijing, 100101, China; [2] College of Computing & Communication Engineering, Graduate University of Chinese Academy of Sciences, Beijing, 100049, China

第一机构：北京联合大学智慧城市学院

年份：2011

卷号：6761 LNCS

起止页码：571-579

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20112914152139)

语种：英文

外文关键词：Computer vision - Deformation - Graphic methods - Graph theory - Image segmentation - Transmission control protocol - Computation theory - Backpropagation

摘要：In recent years researchers have developed many graph theory based algorithms for image setmentation. However, previous approaches usually require trimaps as input, or consume intolerably long time to get the final results, and most of them just consider the color information. In this paper we proposed a fast object extraction method. First it combines deformable models information with explicit edge information in a graph cuts optimization framework. we segment the input image roughly into two regions: foreground and background. After that, we estimate the opacity values for the pixels nearby the foreground/background border using belief propagation (BP). Third, we introduce the texture information by building TCP images’ co-occurrence matrices. Experiments show that our method is efficient especially for TCP images. ? 2011, Springer-Verlag Berlin Heidelberg.