Fast and robust image segmentation with active contours and Student's-t mixture model

详细信息    查看全文

• 作者：Guowei Gao^a ; ^bAuthor Vitae ; Chenglin Wen^c ; ^{wencl@hdu.edu.cn}Author Vitae ; Huibin Wang^aAuthor Vitae
• 关键词：68U10 ; 62F15
• 刊名：Pattern Recognition
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：63
• 期：Complete
• 页码：71-86
• 全文大小：14827 K
• 卷排序：63

文摘

We rewrite the cost function and derive a novel updating of level set function based on probabilistic principles. We propose two novel geometric priors from active contours, and both of them have advantages. We choose the suitable prior as needed to obtain level set function in EM algorithm, which reduce the computational cost. Updating of level set functions and estimation of statistical model parameters are run alternately. We use the student's-t mixture model with heavy tail to enhance the robustness.