计算机应用研究2017，Vol.34Issue(5)：1572-1576,1584,6.DOI:10.3969/j.issn.1001-3695.2017.05.066
基于核函数与局部信息的凸优化分割模型
Novel convex optimization segmentation model based on kernel function and local intensity information
摘要
Abstract
This paper was intended to put forward some improvements against the defects of the C-V model that couldn't accurately segment the images with intensity inhomogeneity and the images with high noise and its computational efficiency was not very high.So this paper made improvements against these defects.First,for each point in a region,it defined a local energy according to the kernel function metric between the intensities of all points within its neighborhood and the intensity average of the region.Then for the whole image domain,it defined a global energy as a data term to integrate the local energy with respect to the neighborhood center.The kernel metric and the local intensity information that were incorporated into the energy made the updating of region mean values more robust against the noise and improved the robustness of segmentation.The convex optimization was applied to this new model,which used a weighted TV-norm given by the edge indicator function to detect the boundaries more accurately.Finally,it used the split Bregman iterative method for numerical solution.Experimental results show that the proposed model can obtain better results with respect to images with noise and inhomogeneous,compared with C-V model,RSF model and DRLSE model.This method greatly increases the computation efficiency and accuracy.关键词
核函数/图像分割/水平集/凸优化/split Bregman方法Key words
kernel function/image segmentation/level set method/convex optimization/split Bregman method分类
信息技术与安全科学引用本文复制引用
张玲,彭新光,李海芳,李钢..基于核函数与局部信息的凸优化分割模型[J].计算机应用研究,2017,34(5):1572-1576,1584,6.基金项目
国家自然科学基金资助项目(61472270,61402318) （61472270,61402318）
