DocumentCode
2850044
Title
A Bayesian framework for crowding effect
Author
Cheng, Zhenbo ; Chen, Wenfeng ; Ran, Tian ; Deng, Zhidong ; Fu, Xiaolan
Author_Institution
Dept. of Comput. Sci., Tsinghua Univ., Beijing, China
fYear
2010
fDate
26-28 May 2010
Firstpage
486
Lastpage
490
Abstract
In crowding, neighboring distractors impair the visual perception of a presented ta get. We study influences by the configuration of distractors on the bias to perceive the orientation of a target. Our results show that: (a) when distractors are similar to each other but different from target, crowding is decreased; (b) when distractors form a subjective contour, crowding is also reduced. These results illustrate that crowding is weak whenever the target stands out from the context and strong when the target is grouped into the context as a part of a global percept. In addition, we show how a Bayesian model, based on the principle of spatial resolution of attention that is modulated by the large size of receptive fields, can account for the behavioral data.
Keywords
Bayes methods; image processing; object detection; visual perception; Bayesian framework; crowding effect; distractor configuration; receptive fields; spatial attention resolution; visual perception; Bayesian methods; Cognitive science; Computational modeling; Computer science; Humans; Information science; Intelligent systems; Laboratories; Shape; Spatial resolution; Bayesian Model; Crowding Effect; Generative Model; Inferential Model;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location
Xuzhou
Print_ISBN
978-1-4244-5181-4
Electronic_ISBN
978-1-4244-5182-1
Type
conf
DOI
10.1109/CCDC.2010.5499009
Filename
5499009
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