Graph Cut Segmentation Method Based on Multiple Priori Shape
XIN Yuelan1,2 , WANG Xili1, ZHANG Xiaohua3, HUANG Heming2
1.School of Computer Science, Shaanxi Nomal University, Xi′an 710119.2.Department of Physics, Qinghai Normal University, Xining 810008.3.Department of Intelligent Information System, Hiroshima Institute of Technology, Japan
Abstract To segment multiple objects in the graph, a graph cut segmentation method for multiple priori shape constraints is proposed. The shape distance in a discrete level set framework is used to define the priori shape model ,and then this model is merged into the regional item of the graph cut framework. The priori energy function is expanded by adding multiple shape priors. The weight coefficient of shape prior item is adaptively adjusted to realize the adaptive control of shape items accounted for the proportion of the energy function. And thus, the problem of artificial selection of parameters is solved and the efficiency of segmentation is enhanced. To obtain the invariance of the method proposed in this research for shape affine transformation, the techniques combining the scale invariant feature transform and the random sample consensus are employed to align. The experimental results indicate that multiple targets in the image can be segmented by the proposed method. Moreover, the image noise pollution as well as occlusion is inhibited.
About author:: XIN Yuelan, born in 1972, Ph.D. candidate, associate professor. Her research interests include image processing and pattern recognition.WANG Xili(Corresponding author),born in 1969, Ph.D., professor. Her research interests include artificial intelligence, pattern recognition and image processing.ZHANG Xiaohua, born in 1963, Ph.D., associate profe-ssor. His research interests include computer graphics, image processing and machine learning.HUANG Heming, born in 1969, Ph.D., professor. His research interests include pattern recognition.)
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2016, Vol. 29 
