摘要
无需重新初始化模型是一个著名的变分水平集模型,在演化过程中无需周期性地重新初始化水平集函数。然而,由于其边缘停止函数是基于梯度的,因此仍然存在一些缺点:对噪声较敏感,弱边缘处易出现边缘泄漏,不能提取不连续边缘等。采用局部熵和灰度变换构造该模型的边缘停止函数。实验结果表明,使用新的边缘停止函数,能够克服上述不足。
Level Set Evolution Without Re-initialization（LSEWR） is a well known variational level set model.It completely eliminates the re-initialization procedure of level set function.However,because its edge stopping function is built based on image gradient,it has still several disadvantages.It is highly sensitive to noise,and is prone to edge leakage when applied to images with weak edges.Finally,it is not available for images with discontinuous edges.A new edge stopping function is constructed based on local entropy and gray-scale transformation.Experiments show that the LSEWR model with the new edge stopping function can do a good work for overcoming the above-mentioned drawbacks.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第34期174-177,共4页
Computer Engineering and Applications
基金
重庆大学"211工程"三期创新人才培养计划建设项目(No.S-09110)
重庆大学中央高校基本科研业务费科研专项研究生科技创新基金(No.CDJXS10100024)
重庆市科委自然科学基金计划资助项目(No.CSTC
2010BB9218)
关键词
图像分割
水平集方法
活动轮廓模型
边缘停止函数
局部熵
灰度变换
image segmentation
level set method
active contour model
edge stopping function
local entropy
gray-scaletransformation
