On Integrated Model for Image Filtering and Segmenting Based on Structure Statistic of Decomposable Markov Network
The removing of image noise, which is abnormity of pixels, is image filtering, and the key of problem is ascertaining the location of pixels with abnormity gray-level. The segmenting pixels with no-similar gray-level are image segmentation. Obviously, the abnormity gray-level is equal to no-similar gray-level in measurement of pixels. So a model integrated (namely Decomposable Markov Networks, for short, DMN), which not only can segment but also filter image, is put forward. The microcosmic configurations of DMN are obtained by computing pixels attribute (namely gray-level, texture and so on), and can firstly identify normal (namely including nosimilar or similar gray-level) or abnormity gray-level (namely possible noise). The abilities of DMN identifying are realized by linking intension of networks, which derive a new uncertain complication (namely uncertain relations of microcosmic link) that is leaded by natural random factors of image data spatial distributing. So the macroscopical Structure Statistic of Decomposable Markov Network (SSDMN) can identify statistical abnormity gray-level (namely including no-similar possible noise and similar gray-level), and then filtering and segmenting image is implemented by a model integrated. Obviously, the DMN is facility of integration, and settles a difficult problem, which is uniting description of pixels numerical value and its spatial locations.
Decomposable Markov Network Image Filtering Image Segmentation Structure Statistic
CAO Jian-nong FANG Yong
College of Earth Science and Resources, Changan University 1 CESRCU 126 South Sect Yanta Road, Xia Xian Research Institute of Surveying and Mapping XRISM 1 Middle Yanta Road, Xian, China, 710054
国际会议
第四届国际计算机新科技与教育学术会议(2009 4th International Conference on Computer Science & Education)
南京
英文
107-112
2009-07-25（万方平台首次上网日期，不代表论文的发表时间）