文献类型：期刊文献

英文题名：SEM microscope image segmentation based on Markov Field Models

作者：Zhu, Yu[1]; Zuo, Tian[1]; Wang, Yuzhou[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, China

年份：2009

起止页码：177

外文期刊名：Proceedings of the 5th International Conference on Image and Graphics, ICIG 2009

收录：EI(收录号：20102012934953)

语种：英文

外文关键词：Activated carbon - Pattern recognition - Carbon fibers - Iterative methods - Image analysis - Nearest neighbor search

摘要：The Scanning Electronic Microscopy (SEM) images are usually used to analyze a certain kind of material properties. To get automatic and accurate quantitative research, image processing methods are utilized to analyze surface morphology from the images obtained from SEM. In this paper, we focus on the Activated Carbon Fiber (ACF) SEM material images. K-nearest neighbor smoothing and Lapalacian sharpenning methods are performed for preprocessing. This paper presents a segmentation method based on Markov Field Models algorithm to separate the pores and background for the material image. Iterated Conditional Mode (ICM) priority iteration algorithm is applied to find exact optimal estimators for labeled field and the model parameters. The experimental results show that the segmentation algorithm based on Markov Field Models is appropriate for microscope images. ? 2009 IEEE.