一种起始点无关的小波系数形状匹配

小波变换的多分辨率特征使其在计算机视觉中得到广泛的应用,在形状匹配中,小波变换对起始点的依赖制约了小波变换的应用。为了克服小波变换对起始点的依赖,引入Zernike矩,提出一种起始点无关的小波系数形状匹配算法。对输入图像进行预处理后提取目标轮廓,生成具有平移、尺度不变的形状链状表达,并通过小波变换进行多尺度分析。最后计算各个尺度下的各阶Zernike矩,来解决小波变换的起始点问题,实现形状表达的旋转不变性。实验结果表明该算法适用于轮廓较明显的目标,同时具有速度快、精度高、鲁棒性强的优点。

A Novel Starting-point-independent Wavelet Coefficient Shape Matching

In many computer vision tasks,in order to improve the accuracy and robustness to the noise,wavelet analysis is preferred for its natural multi-resolution property.However,the wavelet representation suffers from the dependency on the starting point in shape matching.For overcoming the problem,the Zernike moments are introduced,and a novel Starting-Point-Independent wavelet coefficient shape matching algorithm is presented.The proposed matching algorithm firstly gains the object contours,and gives the translation and scale invariant object shape representation.The object shape representation is converted to dyadic wavelet representation by wavelet transform,and then the Zernike moments of wavelet representation in different scales are calculated.With respect to property of rotation invariant of Zernike moments,consider the Zernike moments as the feature vector to calculate the similarity between the object and template image,which overcoming the problem of dependency on starting point.The experimental results indicates that the proposed algorithm is efficient,precise,and robust.