摘要
基于树结构的马尔可夫随机场(TS-MRF),提出模糊多级逻辑模型(fuzzy MLL),并提出了一种新的图像分割算法——模糊TS-MRF算法。与传统的MRF分割算法和TS-MRF算法比较,该方法在计算耗时增加很少的情况下,对分割精度提高较大。更为重要的是,该方法提供了一个新思路,使得基于MRF的先验信息的描述更为精细。
Proposes a new image segmentation method based on Tree-Structured Markov Random Field （TS-MRF） and fuzzy Muhi-Level Logistic （MLL） model,where fuzziness has been introduced into TS-MRF and the calculation of potential function becomes more meticulous and more precise.Compares with the classical TS-MRF method,the quantitative errors are smaller under the same conditions when the new algorithm is used,while the computational time increases little.More interesting,this method may hint one simple and efficient way to make MRF-based prior（not limited in MLL model） more precise,i.e.making the MRF- based clique changed to be fuzzy clique by using posterior probability.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第30期37-39,82,共4页
Computer Engineering and Applications
基金
国家重点基础研究发展规划(973)(the National Grand Fundamental Research 973 Program of China under Grant No.2003CB716101)
广东省自然科学基金(the Natural Science Foundation of Guangdong Province of China under Grant No.06024419)。
关键词
图像分割
马尔可夫随机场
模糊
算法
image segmentation
Markov random field
fuzzy set
algorithm
