DocumentCode
3209737
Title
Infrared target detection based on fuzzy ART neural network
Author
Bingwen Chen ; Wenwei Wang ; Qianqing Qin
Author_Institution
Coll. of Electron. Inf., Wuhan Univ., Wuhan, China
Volume
2
fYear
2010
fDate
13-14 Sept. 2010
Firstpage
240
Lastpage
243
Abstract
The infrared target detection is a challenge task. In order to solve the lower signal-to-noise ratio, the lower resolution and the halo effect problems, we propose a novel detection approach based on fuzzy ART neural network. The fuzzy ART neural network is capable of rapid stable learning of recognition categories, and it can determine the total number of categories adaptively. At first, in the background modeling stage, the fuzzy ART neural networks were applied to classify the background and non-background categories, and the non-background categories were discarded so as to build the background model. Then the background model was combined with fuzzy ART neural networks to detect the targets. Experiments have been carried out and the results demonstrate that the proposed approach is robust to noise, and can eliminate the halo effectively. It can detect the targets effectively without much more post-process.
Keywords
ART neural nets; fuzzy neural nets; infrared imaging; learning (artificial intelligence); object detection; fuzzy ART neural network; halo effect problems; infrared target detection; stable learning; Fuzzy logic; Image segmentation;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Natural Computing Proceedings (CINC), 2010 Second International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-7705-0
Type
conf
DOI
10.1109/CINC.2010.5643745
Filename
5643745
Link To Document