摘要
提出了一种二维傅里叶小波描述子。其改进之处在于在目标图像的直角坐标 极坐标转换过程中 ,预先将图像中的冗余点去除 ,并对像素点进行细化处理 ,以减少计算时间 ,提高变换精度。在飞机型号识别实验中 ,采用改进的二维傅里叶小波描述子对样本进行描述 ,并使用神经网络分类器进行分类 ,获得了很高的识别率 ,而运算时间显著减少 ,证明它是一种十分有效的目标描述方法。
A revised 2D Fourier-wavelet descriptor for the object recognition is proposed. The main revision lies in the process of transforming the object image into polar coordinate system. For the purpose of reducing calculating time and improving transforming precision, the redundant pixels in the image are deprived and the rest of them are fractionlized. Using a neural network classifier, this revised descriptor evidently reduces the calculating time and improves the rate of recognition in the experiment of plane type recognition. It is proofed to be a very effective method for feature description of object.
出处
《红外与激光工程》
EI
CSCD
北大核心
2002年第4期290-293,共4页
Infrared and Laser Engineering
