Motion Integration Using Competitive Priors

  • Shuang Wu
  • Hongjing Lu
  • Alan Lee
  • Alan Yuille
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5604)


Psychophysical experiments show that humans are better at perceiving rotation and expansion than translation [5][9]. These findings are inconsistent with standard models of motion integration which predict best performance for translation. To explain this discrepancy, our theory formulates motion perception at two levels of inference: we first perform model selection between the competing models (e.g. translation, rotation, and expansion) and then estimate the velocity using the selected model. We define novel prior models for smooth rotation and expansion using techniques similar to those in the slow-and-smooth model [23] (e.g. Green functions of differential operators). The theory gives good agreement with the trends observed in four human experiments.


Green Function Radial Motion Rotation Model Translation Model Psychophysical Experiment 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Shuang Wu
    • 1
  • Hongjing Lu
    • 2
  • Alan Lee
    • 2
  • Alan Yuille
    • 1
  1. 1.Department of StatisticsUCLAUSA
  2. 2.Department of PsychologyUCLAUSA

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