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基于小波矩和支持向量机的装甲车辆识别研究
引用本文:黄应清,赵锴,蒋晓瑜,田宏亮,魏磊.基于小波矩和支持向量机的装甲车辆识别研究[J].装甲兵工程学院学报,2012,26(3):61-64.
作者姓名:黄应清  赵锴  蒋晓瑜  田宏亮  魏磊
作者单位:装甲兵工程学院控制工程系,北京,100072
摘    要:分析了应用小波矩特征进行地面复杂背景下装甲车辆识别的理论依据,实地采集了某型坦克和某型步兵战车的灰度图像,提取其小波矩特征,采用支持向量机进行分类识别,进行了性能测试实验。结果表明:归一化后的图像的小波矩特征具有良好的不变性;小波矩特征对噪声和局部遮挡有较强的适应性,识别率比较稳定;支持向量机方法具有良好的分类识别能力。

关 键 词:小波矩  Hu矩  支持向量机  识别

Study on Recognition for Armored Vehicle Based on Wavelet Moment and SVM
HUANG Ying-qing,ZHAO Kai,JIANG Xiao-yu,TIAN Hong-liang,WEI Lei.Study on Recognition for Armored Vehicle Based on Wavelet Moment and SVM[J].Journal of Armored Force Engineering Institute,2012,26(3):61-64.
Authors:HUANG Ying-qing  ZHAO Kai  JIANG Xiao-yu  TIAN Hong-liang  WEI Lei
Institution:(Department of Control Engineering,Academy of Armored Force Engineering,Beijing 100072,China)
Abstract:The theory that the wavelet moment could be applied to solve the problem of recognizing armored vehicles under complex ground background is analyzed.True gray images of certain tank and infantry fighting vehicle are acquired,the features of wavelet moment are extracted,and classification and recognition are carried out by using Support Vector Machine(SVM).Performance test experiments are made,the results show that: the features of wavelet moments of the normalization images are stable;the characteristics of wavelet moment have better adaptation to noise and partial-cover,and the recognition rate is more stable;the method of SVM has better capabilities of classification and recognition.
Keywords:wavelet moment  Hu moment  Support Vector Machine(SVM)  recognition
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