A Robust Improved Chan-Vese model Based on Gaussian Regularizing Level Set

摘要：

In this paper, a new robust improved Chan-Vese (ICV) model is proposed for image segmentation, which is built based on the techniques of curve evolution, signed pressure force (SPF) function and level set method. Compared with the ICV model, the proposed method is more robust to the location of the initial contour. Similar to the ICV model, a Gaussian regularizing level set method (GRLSM) is used to reduce the computational cost. Experimental results on some synthetic and real images show that our model is efficiency. Moreover, comparisons with the ICV model show that our model is more robust to the location of the initial contour.

关键词： Chan-Vese model ICV GRLSM SPF

作者: Nengyuan Pan Zhengshou Feng MeiQing Wang

作者单位: College of Mathematics and Computer Science, Fuzhou University, Fujian 350108, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 1164-1168

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）