DocumentCode
2719943
Title
ATR System Based on the K-M BP Neural Network
Author
Yuan, Zhijie ; Mu, Chengpo ; Chen, Yuanqian ; Song, Jia
Author_Institution
Beijing Inst. of Technol., Beijing, China
fYear
2012
fDate
11-13 Aug. 2012
Firstpage
1197
Lastpage
1200
Abstract
Automatic target recognition (ATR) is an important issue in the military field, the topic of the ATR system is the pattern recognition and classification. In the paper, we present an approach for building an ATR system with improved artificial neural network to recognize and classify the typical targets in the army field. The invariant features of Hu invariant moments and roundness were selected to be the input of the neural network for they have the invariance of rotation, translation and scaling. The pictures of the targets are generated by the 3-D models to improve the recognition rate for it is necessary to provide enough pictures for training the artificial neural network. The simulations prove that the approach can implement the task of ATR system in high recognition rate and real time.
Keywords
backpropagation; image classification; military systems; neural nets; object recognition; solid modelling; 3D models; ATR system; Hu invariant moments; K-M BP neural network; army field; artificial neural network; automatic target recognition; invariant features; military system; neural network training; pattern classification; pattern recognition; recognition rate improvement; rotation invariance; roundness; scaling invariance; target classification; target picture generation; translation invariance; Artificial neural networks; Biological neural networks; Image recognition; Neurons; Solid modeling; Target recognition; Training; ATR syste; BP neural network; Hu invariant; pattern recognition; pictures generation; roundness;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science & Service System (CSSS), 2012 International Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4673-0721-5
Type
conf
DOI
10.1109/CSSS.2012.303
Filename
6394541
Link To Document