图割与非线性统计形状先验的图像分割  

Image segmentation based on graph cuts and nonlinear statistical shape prior

作　　者：辛月兰[1,2] 张晓华 汪西莉[1] 

机构地区：[1]陕西师范大学计算机科学学院,陕西西安710062 [2]青海师范大学物理系 [3]广岛工业大学情报学部智能情报系统系

出　　处：《计算机工程与科学》2015年第3期566-575,共10页Computer Engineering & Science

基　　金：国家自然科学基金资助项目(41171338;61462072);教育部"春晖计划"资助项目(Z2012100)

摘　　要：提出一种图割与非线性统计形状先验的图像分割方法。首先,在输入空间对输入的形状模板进行配准,得到训练集;其次,采用非线性核函数将目标形状先验映射到特征空间进行主成分分析,获取其投影形状,将此投影形状映射回原输入空间得到目标的平均形状,构成新的能量函数;第三,通过自适应调整形状先验项的权值系数,使能量函数的形状先验项自适应于被分割的图像;最后,用Graph Cuts方法最小化能量函数完成图像分割。实验结果表明,该方法不仅能准确分割与形状先验模板有差别的图像,而且对目标有遮挡或污染的图像也有较好的分割效果,提高了分割效率。An image segmentation method based on graph cuts and nonlinear statistical shape prior is proposed.Firstly,the input shape templates are registered in the input space,and the training sets are obtained.Secondly,the target shape prior is mapped to a feature space with principal component analysis by using a nonlinear kernel function,and the projected shape is obtained,which is mapped back to the original input space to obtain the average shape of the target,and thus forms a new energy function.Thirdly,through the weight coefficient self-adaptive adjustment of the shape prior term,the shape prior term of the energy function becomes adaptive to the image to be segmented.Finally,the image segmentation is accomplished by graph cuts technology so as to minimize the energy function.Experimental results show that the proposed method can not only correctly segment the images which are different than the shape prior templates,but also has better segmentation effect for the object images with occlusion and pollution.Moreover,the proposed method can improve the quality of image segmentation.

关 键 词：核主成分分析 平均形状 概率图 图像分割 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]