摘要
边缘信息对图像分割是十分重要的。把图像的边缘信息融入C-V模型(active contours without edges),提出一个新的几何模型,它同时利用同质区域信息和边缘信息使演化曲线在目标边缘处停止。实验显示:新模型能够克服C-V模型的一些缺点;在减少分割时间的同时,对目标灰度不均匀或背景灰度不均匀、含弱边缘或强噪声的图像,分割效果不仅优于C-V模型,也优于C-V模型的两个最新改进模型(LBF和GACV)。
Edge information is crucial for image segmentation. A novel geometric model is proposed, which incorporates edge information into C-V model （active contours without edges）. It utilizes both the information of homogeneous regions and the edge information to stop the active contours on the object boundaries. The experimental resuits show that the proposed model can overcome some disadvantages of C-V model, and obtain better results with respect to images that have the intensity inhomogeneity in objects or backgrounds, weak edges, and/or high noises while significantly reducing segmentation time. Besides, it has many advantages over other two improved C-V mod- els（LBF and GACV）.
出处
《计算机工程与应用》
CSCD
2012年第18期181-186,共6页
Computer Engineering and Applications
关键词
图像分割
几何活动轮廓模型
C-V模型
水平集方法
偏微分方程
image segmentation
geometric active contour model
C-V model
level set method
partial differential equation
