Fast and robust image segmentation with active contours and Student's-t mixture model
作者：
Highlights：
• 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.
摘要
Highlights•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.
论文关键词：Segmentation,Active contours,Level set,Student's-t mixture model,EM algorithm
论文评审过程：Received 21 October 2015, Revised 8 July 2016, Accepted 19 September 2016, Available online 21 September 2016, Version of Record 30 September 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.09.014
