针对傅里叶描述子对物体轮廓细节特征描述的不足, 提出了综合傅里叶描述子和角点特征的轮廓描述方法。利用尺度空间 DOG算子或傅里叶变换逆差方法检测轮廓角点提高检测速度, 采用最小循环距离方法对角点序列进行比对, 增强了匹配鲁棒性。实验结果表明, 与傅里叶描述子方法相比, 该方法准确率有了较大提高, 与 CSS方法相比, 运算速度提高了近 10倍。

Aiming at the problem that Fourier descriptor has not enough information for shape contour’s fine details, an approach combining Fourier descriptor and corner feature into one kind of improved shape contour descriptor was proposed. Difference of Gaussians (DOG) operator of scale space or difference of inverse Fourier transform were used to detect corner in order to improve the calculation speed, and the method of minimum cycle distance was used to compare corner sequence in order to makes the matching more robust. Experimental results verify that the approach greatly improves accuracy rate compared with Fourier descriptor approach and improves calculation speed nearly 10 times compared with Curvature Scale Space (CSS) approach.