DocumentCode :
2040925
Title :
Surface targets recognition method based on LVQ neutral network
Author :
Peng Li ; Yihui Zhang ; Chao Wang ; Shuangmiao Li
Author_Institution :
Coll. of Autom. of Harbin Eng., Univ. of Harbin, Harbin, China
fYear :
2015
fDate :
2-5 Aug. 2015
Firstpage :
676
Lastpage :
680
Abstract :
These A method to identify the different surface targets with combination features was proposed on the conditions of pretreatment that the video image sequence was preprocessed by removing noise and image stabilization. Firstly, Targets and background were separated by segmenting the clearer images. Secondly, the geometrical feature and the moment invariant feature in different targets were extracted. The LVQ (Learning Vector Quantization) neutral network was trained to identify surface targets by using combination features. Finally, the simulation study of identifying test targets was done. The results of simulation research show that the proposed method based on combination features of different surface targets can recognizes the three types of common surface targets effectively. And, the convergence speed of LVQ neural network is fast compared with the BP neural network and the recognition has a good effect.
Keywords :
feature extraction; image denoising; image recognition; image segmentation; image sequences; learning (artificial intelligence); neural nets; vector quantisation; LVQ neural network; geometrical feature extraction; image denoising; image segmentation; image stabilization; learning vector quantization neutral network; moment invariant feature extraction; surface target recognition method; video image sequence; Biological neural networks; Feature extraction; Marine vehicles; Neurons; Target recognition; Training; LVQ neutral network; combination features; surface targets; targets recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Mechatronics and Automation (ICMA), 2015 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-7097-1
Type :
conf
DOI :
10.1109/ICMA.2015.7237566
Filename :
7237566
Link To Document :
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