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Proceedings Paper

A target recognition algorithm based on a support vector machine
Author(s): Yan Ding; Weiqi Jin; Yuhong Yu; Han Wang
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Paper Abstract

In order to meet the accuracy requirement of a target recognition system, a target recognition algorithm based on support vector machine is proposed in this paper. In the algorithm, firstly, a fast image multi-threshold segmentation method is accomplished by using a novel searching path of particle swarm optimization to separate the target from the background. Then some characteristics of target samples such as moment feature, affine invariant feature and texture feature based on co-occurrence matrix are extracted. Thus, the parameter optimizing selection is achieved according to the corresponding rule. After comparing with other kernel functions, the radial basis function kernel is selected to build a target classifier for one particular typical target. Meanwhile, a BP neural network based target recognition system is implemented to facilitate comparison. Finally, the target recognition method presented in this paper is applied to the airplane recognition. The experimental results show that the algorithm given in this paper can effectively detect and recognize the image target automatically. It can be applied to both single target and multi-objective recognition. Moreover, real-time target recognition can be achieved for single target.

Paper Details

Date Published: 28 January 2009
PDF: 10 pages
Proc. SPIE 7156, 2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments, 71563G (28 January 2009); doi: 10.1117/12.816954
Show Author Affiliations
Yan Ding, Beijing Institute of Technology (China)
Weiqi Jin, Beijing Institute of Technology (China)
Yuhong Yu, Beijing Institute of Technology (China)
Han Wang, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 7156:
2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments
Yunlong Sheng; Yongtian Wang; Lijiang Zeng, Editor(s)

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