基于高斯分布估计的快速水平集分割方法研究

Research on Fast Level Set Segmentation Method based on Gauss Distribution Estimation

传统的CV模型只能用于分割灰度分布比较均匀、目标与背景灰度均值差异较大的图像,且因需要求解偏微分方程导致分割速度很慢.文章在传统快速水平集分割模型的基础上,将高斯分布估计引入速度项,使得快速水平集可以分割复杂的目标,并将HIS空间中的色调分量与强度分量进行融合,得到了一种彩色高斯快速模型.新算法具有分割速度快、可分割复杂目标的优点.

The traditional CV model can only be used for the segmentation of the image whose gray distribution is uniform and whose targets and background have a big difference in gray level.The segmentation speed is very slow due to the need of solving partial differential equations during the segmentation process.On the base of the traditional fast level-set segmentation model,Gauss distribution estimation is introduced into a fast level-set method and a new fast level-set method segmentation algorithm is developed.The new algorithm has the advantages of fast segmentation speed and the segmentation of complex targets.