摘要： 基于偏微分方程的图像处理技术由于能够获得连续单像素的边缘而受到重视,其中梯度向量场与气球力混合作用的改进GAC 模型———GAC_GVF&B 克服了传统GAC 模型的缺点,能准确地收敛到多目标图像和形状复杂图像的目标边界。但该算法的运行时间较长,影响算法的实际应用。为此,利用半隐式方案的加性算子分裂(AOS)算法,适当增大时间步长,降低迭代次数,对GAC_GVF&B 模型的计算进行加速,在保证算法分割准确性的同时提高算法的收敛速度。实验结果表明,采用半隐式方案的AOS 算法具有较好的图像分割效果,可有效减少所需的迭代次数,降低迭代时间和CPU 运行时间,提高运行速率。
关键词:
图像分割,
测地活动轮廓模型,
梯度向量场,
追赶法,
水平集方法,
加性算子分裂算法
Abstract: The Partial Differential Equation(PDE) image processing is an advanced technology due to the ability of
obtaining continuous and one-pixel edges. The improved Geodesic Active Contour(GAC) model by using the gradient
vector field and a balloon force———GAC_GVF&B is an important one,because it can convergence accurately to target edges on images with multi-object or complex objects. But the model suffers from long running time which blocks its application. By appropriately increasing the time step and reducing the number of iterations,a semi-implicit additive split operator———Additive Operator Splitting (AOS) is used to speed up the computing of the GAC _GVF&B model and improve the convergence rate with the same accuracy of the segmentation. Experimental results show that the AOS algorithm is correct and effective,can reduces the number of iterations required,and lowers iteration time and cpu time. Furthermore,it speeds up the segmentation.
Key words:
image segmentation,
Geodesic Active Contour (GAC) model,
gradient vector flield,
chasing method,
level set method,
Additive Operator Splitting(AOS) algorithm
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