关键词: 水平集, 重新初始化, CV模型, 活动区域轮廓模型, 图像分割

Abstract: Active region contour model has been successfully applied in image segmentation，object tracking，etc，which has many pros compared with active contour model based on edge.But，during the evolution of level set，it’s numerically necessary to keep the evolving level set function close to a signed distance function.Re-initialization，a technique for periodically re-initializing the level set function to a signed distance function during the evolution，results in slowing speed of evolution，increasing complex of implement.To overcome the re-initialization，the author proposes to add a panelized energy into active region contour model to keep level set close to a signed distance function；it thus speeds up the curve evolution and the segmentation.The auhtor applies the model to segment the texture image，brain MRI and track objects；the experiments prove the model is effective.

Key words: level set, re-initialization, CV model, active region contour model, image segmentation