Work

Model Selection and Velocity Estimation Using Novel Priors for Motion Patterns

Year: 2008
Type: article
Abstract: 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.
Language: English
Open Access status: green
OpenAlex ID: W2163259821
FWCI:
Cites: 15
Cited by: 1
Related to: 10