[1]张 涛,费树岷,李晓东.基于GARBF神经网络及边界不变特征的车辆识别[J].智能系统学报,2009,4(3):278-272.
ZHANG Tao,FEI Shu-min,LI Xiao-dong.Vehicle recognition using boundary invariants and a genetic algorithm trained radial basis function neural network[J].CAAI Transactions on Intelligent Systems,2009,4(3):278-272.
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ZHANG Tao,FEI Shu-min,LI Xiao-dong.Vehicle recognition using boundary invariants and a genetic algorithm trained radial basis function neural network[J].CAAI Transactions on Intelligent Systems,2009,4(3):278-272.
基于GARBF神经网络及边界不变特征的车辆识别
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
4
期数:
2009年第3期
页码:
278-272
栏目:
学术论文—机器感知与模式识别
出版日期:
2009-06-25
- Title:
- Vehicle recognition using boundary invariants and a genetic algorithm trained radial basis function neural network
- 文章编号:
- 1673-4785(2009)03-0278-05
- Keywords:
- vehicle recognition; genetic algorithms; radial basis function neural network; boundary invariant moments
- 分类号:
- TP391.41
- 文献标志码:
- A
- 摘要:
- 修正的边界不变矩在目标旋转、缩放和平移过程中能保持不变性.将其作为车辆目标的识别特征，并且利用遗传算法(GA)优化径向基函数(RBF)神经网络参数，能很好地实现对车辆目标的识别.实验表明，该方法在复杂背景下对目标的识别具有很强的鲁棒性，能快速准确地识别车辆类型；并且边界不变特征的引入，减少了数据运算量，提高了识别效率.
- Abstract:
- A method for vehicle recognition using the modified boundary invariant moments and a genetic algorithm trained radial basis function (GARBF) neural network was developed. The modified boundary invariant moments have the accustomed invariance for rotation, scaling and translation of targets, which can be used as the invariant characteristic vectors. Using these features as the inputs of a neural network, the vehicle targets can then be recognized accurately. In order to improve recognition accuracy and speed, the genetic algorithm (GA) was used to optimize the RBF parameters: centers, variance, and numbers of hidden nodes. Experimental results indicated that this method, which introduces invariants based on boundaries, yields robust target recognition with greatly reduced computation time and improved efficiency.
- 参考文献/References:
-
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备注/Memo
收稿日期：2008-03-10.
基金项目：国家自然科学基金重点资助项目(60835001).
通信作者：张 涛.E-mail:tzhangcn@gmail.com.
作者简介：
张 涛，男，1981年生，博士研究生，主要研究方向为计算机视觉、图像处理、模式识别等.发表学术论文6篇.
?费树岷，男，1961年生，教授，博士生导师，博士后，主要研究方向为非线性控制系统设计和综合，混杂系统分析、建模与控制,神经网络控制,时滞系统控制等.近年来，参与国家自然科学基金（含重点）项目4项、国家“863”高科技发展计划项目2项，实际应用项目10项.发表学术论文90余篇，出版专著（合著）1部.
?李晓东，男，1974年生，博士研究生，主要研究方向为图像处理、模式识别等.发表学术论文9篇.
更新日期/Last Update:
2009-09-14