摘要
传统的分水岭分割算法属于无监督的图像分割算法,分割获得的子区域往往不具备现实的语义信息。在分水岭分割的基础上,利用子区域像素值的高斯统计性质,提出了一种有监督的图像背景学习方法。该算法能够通过对少量人工标注的图像样本的学习,获得刻画背景子区域规律的统计模型。在此基础上对新图片中隶属于背景的子区域进行判断和合并,从而达到区分目标与背景的目的。实验验证了算法的有效性。
The traditional watershed segmentation algorithm is a kind of unsupervised segmentation algorithms,which produces sub-regions without semantic representation.A supervised image segmentation algorithm is proposed,which is based on Gaussian statistical property of sub-regions obtained by watershed segmentation.The proposed algorithm can learn the statistical model of background with a few labeled images,and then correctly separates the objects from background by merging the sub-regions which are judged members of the background.Experiments verify the validity of the proposed method.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第21期205-209,共5页
Computer Engineering and Applications
基金
国家自然科学基金No.61070033
广东省自然科学基金重点项目(No.9251009001000005)
广东高校优秀青年创新人才培育项目(No.LYM09068)~~
关键词
背景学习
有监督
分割
分水岭算法
background learning
supervised
segmentation
watershed algorithm
