摘要
梯度矢量流主动轮廓线模型是广泛应用于数字图像处理的一种目标轮廓跟踪算法,但其计算速度不尽如人意,且易受干扰噪声及虚假边缘的影响。本文在结合贪婪算法的基础上,利用一种改进的快速梯度矢量流场算法,提出了一种新的主动轮廓线模型。实验表明,该方法在保持了GVF模型的原有特性的基础上大大提高了模型收敛的速度,并且较好的限制了非目标边缘和噪声干扰的影响。
Gradient vector flow（GVF） snake has been used more and more widely in applications of image analysis and computer vision,but the calculative speed is a hard question in the practice of application,and the present model often subject to the influence of noises and fake edges.Based on the greedy search algorithm,the improved fast gradient vector flow is introduced to present a fast active contour model.Experiments prove that the new model show fast speed on the basis of keeping the characteristic of GVF model,and limits the influence of false edge and noise disturbance.
出处
《微计算机信息》
2010年第35期247-248,205,共3页
Control & Automation
关键词
梯度矢量流
贪婪算法
图像分割
gradient vector flow
greedy search algorithm
image segmentation
