傅里叶描述子相位与幅值融合方式比较  

Comparison of Fusion Methods of Fourier Descriptor's Phase and Amplitude

作　　者：许强[1] 马登武[1] 

机构地区：[1]海军航空工程学院兵器科学与技术系,山东烟台264001

出　　处：《光电工程》2014年第3期67-72,共6页Opto-Electronic Engineering

基　　金：航空科学基金资助项目

摘　　要：傅里叶描述子是物体轮廓分类的重要方法,当前大多数研究只利用了其幅值特征,不能满足轮廓分类的精确度要求。针对这一问题,提出了三种融合傅里叶描述子幅值和相位特征的方法对轮廓进行分类,对三种融合方法的准确性进行了比较。实验表明,基于像素级的融合方法对轮廓分类最为有效,相比于只采用幅值特征的方法准确率提高了10%,但需要的特征向量维数大大增加,判别的时效性有所降低。通过像素级融合的傅里叶描述子与曲率尺度空间描述子以及形状上下文特征在目标识别应用中的比较可以看出,傅里叶描述子具有较高的时效性。Fourier descriptor is an important classification method of object contours. At present, most of researchers use only the amplitude of Fourier descriptor whose accuracy is not enough. Aiming at the problem, we presented three methods of fusion of Fourier descriptor’s phase and amplitude to classification of object contours and compared their results. Experimental results show that pixel-level fusion is the most effective method whose classification accuracy is 10%higher than the method for only using the amplitude of Fourier descriptor, but the pixel-level fusion method is time-consuming because its number of feature vector’s dimensions is much. Comparing Fourier descriptors of pixel level fusion with curvature scale space descriptors and shape context, it is seen that Fourier descriptors are higher timeliness.

关 键 词：傅里叶描述子 相位 幅值 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]