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NIPS
2008

Model selection and velocity estimation using novel priors for motion patterns

11 years 1 months ago
Model selection and velocity estimation using novel priors for motion patterns
Psychophysical experiments show that humans are better at perceiving rotation and expansion than translation. These findings are inconsistent with standard models of motion integration which predict best performance for translation [6]. 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 [17] (e.g. Green functions of differential operators). The theory gives good agreement with the trends observed in human experiments.
Shuang Wu, Hongjing Lu, Alan L. Yuille
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Shuang Wu, Hongjing Lu, Alan L. Yuille
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