• 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