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