DocumentCode :
2449838
Title :
Bayesian Visual Inference and Attention
Author :
Zhao, Jieyu ; You, Jian
fYear :
2009
fDate :
25-26 April 2009
Firstpage :
390
Lastpage :
393
Abstract :
The massive amount of visual inputs could easily go beyond the systempsilas computational limit. Visual attention provides an efficient way to discard the trivial inputs and achieve good performance. In this paper we develop a unified and consistent theoretical framework for the visual attention and inference with Bayesian decision theory. A visual attention scheme is simulated with an artificial retina mimicking biological ones. We test our model on a real world video clip. The experimental results indicate the promise of this visual attention model in complicated vision applications.
Keywords :
Bayes methods; decision theory; Bayesian visual inference; artificial retina; decision theory; visual attention; Bayesian methods; Belief propagation; Biological system modeling; Biology computing; Decision theory; Eyes; Humans; Propagation losses; Retina; Visual perception; Bayesian Framework; Visual Attention; Visual Inference;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Intelligence, 2009. JCAI '09. International Joint Conference on
Conference_Location :
Hainan Island
Print_ISBN :
978-0-7695-3615-6
Type :
conf
DOI :
10.1109/JCAI.2009.207
Filename :
5159023
Link To Document :
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