摘要
在分析现有模板匹配算法存在问题的基础上 ,提出一种基于多尺度变形模板的新方法 .它在已有的 Snake算法基础上 ,加入了形状约束 ,并利用小波变换的多尺度特性 ,使得匹配过程在由粗至精的尺度上进行 ,从而使运算速度大大提高 ,对噪声的敏感程度也相应下降 .而轮廓初始化是在较粗的尺度上 ,利用 Hausdorff距离初步匹配得到的 ,漏警概率较低 .实验结果与理论分析相吻合 ,验证了算法对多类目标适用 ,具有速度快、精度高和对图像畸变、噪声与遮挡不敏感的优点 .
A number of different algorithms have been described in the literature about template matching. Nevertheless, up to now no satisfactory solutions have been found to be both fast and immune to image deformation. A new approach based on a multiscale deformable template is presented. Compared with the basic active contour models, a shape restriction is added. Also, the multiscale property of wavelet transform is used to lead the matching process implemented from a coarse-to-fine scale. The initialization of the contour is done in a coarse scale, which can reduce the missing possibility and the compute complexity. The processing procedure includes three steps. First, multiscale edges of an image are detected based on wavelet transform; second, object contour is initiated in the input image by comparing Hausdorff distance in the coarsest scale; finally, the exact object contour is extracted via a coarse-to-fine scale implementation of the deformable template matching scheme. Experimental results with various kinds of image data have proved the algorithm to be efficient, precise and very immune to image deformation, noise and occlusion.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2002年第10期1325-1330,共6页
Journal of Computer Research and Development
