摘要
针对传统的C-V模型对于含有多灰度级目标的图像难以准确分割并且分割速度缓慢等问题,提出了在C-V模型中引入梯度信息的图像分割算法。该算法在C-V模型的偏微分方程中加入了基于梯度信息的加速因子和弱目标边界控制力,加速因子的引入可以显著地提高C-V模型的分割速度,弱目标边界控制力可以有效地防止弱目标边界泄漏和漏分割。实验结果表明:该算法能够有效分割出弱目标和提高图像分割速度。
The traditional C-V model can not accurately segment an image including multi-gray level objects and the segmentation speed is slow.In order to solve the problems,an image segmentation algorithm based on C-V model and gradient information is proposed.The algorithm introduces a speedup item and a weak object boundary control force in the partial differential equation of C-V model.The speedup item can effectively improve the segmentation speed,and the weak object boundary control force can avoid the weak objects boundary leaking and omitting segmentation.Experimental results demonstrate that the proposed algorithm can effectively segment the weak objects and reduce the segmentation time.
出处
《光电子．激光》
EI
CAS
CSCD
北大核心
2010年第3期452-455,460,共5页
Journal of Optoelectronics·Laser
基金
国家自然科学基金资助项目(60672128)
关键词
C-V模型
水平集方法
梯度
偏微分方程
C-V model
level set method
gradient
partial differential equation
