基于改进活动轮廓模型的图像边界分割

Image Edge Segmentation Based on Improved Active Contour Model

针对传统活动轮廓模型分割精度低和鲁棒性差的缺陷,提出一种改进的活动轮廓模型的图像边界分割方法.首先在图像分割中引入灰度信息,解决灰度不均匀性带来的不利影响,然后引入了图像梯度信息,避免噪声对边界分割的干扰,并通过最小化能量函数保证轮廓向目标边缘不断演化,最后采用多种类型的图像对其有效性和优越性进行仿真测试.结果表明,改进后的模型可以对灰度不均匀性和噪声图像进行准确的边界分割,分割精度优于传统活动轮廓模型,并且有良好的通用性.

In view of the defects of low accuracy and poor robustness of traditional active contour model,an improved active contour model is proposed.First in the image segmentation in gray information,solve the intensity in homogeneity bring adverse effects,then the gradient information of image is introduced,to avoid the interference of noise on the boundary segmentation,and by minimizing the energy function guarantee contour to the edge of the target evolution,finally using multiple types of image on the validity and superiority of the simulation test.The results show that the model can accurately segment the gray level and the noise image,which is very universal,and the segmentation accuracy is better than that of the traditional active contour model.