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一种飞机图像目标多特征信息融合识别方法

李新德 杨伟东 DEZERT Jean

李新德, 杨伟东, DEZERT Jean. 一种飞机图像目标多特征信息融合识别方法. 自动化学报, 2012, 38(8): 1298-1307. doi: 10.3724/SP.J.1004.2012.01298
引用本文: 李新德, 杨伟东, DEZERT Jean. 一种飞机图像目标多特征信息融合识别方法. 自动化学报, 2012, 38(8): 1298-1307. doi: 10.3724/SP.J.1004.2012.01298
LI Xin-De, YANG Wei-Dong, DEZERT Jean. An Airplane Image Target's Multi-feature Fusion Recognition Method. ACTA AUTOMATICA SINICA, 2012, 38(8): 1298-1307. doi: 10.3724/SP.J.1004.2012.01298
Citation: LI Xin-De, YANG Wei-Dong, DEZERT Jean. An Airplane Image Target's Multi-feature Fusion Recognition Method. ACTA AUTOMATICA SINICA, 2012, 38(8): 1298-1307. doi: 10.3724/SP.J.1004.2012.01298

一种飞机图像目标多特征信息融合识别方法

doi: 10.3724/SP.J.1004.2012.01298
详细信息
    通讯作者:

    李新德

An Airplane Image Target's Multi-feature Fusion Recognition Method

  • 摘要: 提出了一种基于概率神经网络(Probabilistic neural networks, PNN)和DSmT推理 (Dezert-Smarandache theory)的飞机图像目标多特征融合识别算法. 针对提取的多个图像特征量,利用数据融合的思想对来自图像目标各个特征量提供的信息进行融合处理.首先,对图像进行二值化预处理,并提取Hu矩、归一化转动惯量、 仿射不变矩、轮廓离散化参数和奇异值特征5个特征量;其次, 针对DSmT理论中信度赋值构造困难的问题,利用PNN网络,构造目标识别率矩阵,通过目标识别率矩阵对证据源进行信度赋值;然后,用DSmT组合规则在决策级层进行融合,从而完成对飞机目标的识别;最后,在目标图像小畸变情形下, 将本文提出的图像多特征信息融合方法和单一特征方法进行了对比测试实验,结果表明本文方法在同等条件下正确识别率得到了很大提高,同时达到实时性要求,而且具有有效拒判能力和目标图像尺寸不敏感性. 即使在大畸变情况下,识别率也能达到89.3%.
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出版历程
  • 收稿日期:  2011-10-19
  • 修回日期:  2012-03-26
  • 刊出日期:  2012-08-20

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