A unified probabilistic model of the perception of three-dimensional structure from optic flow |
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Authors: | Francis Colas Jacques Droulez Mark Wexler Pierre Bessière |
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Institution: | LPPA, Collège de France, 11, place Marcelin Berthelot, 75231 Paris Cedex 05, France. francis.colas@college-de-france.fr |
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Abstract: | Human observers can perceive the three- dimensional (3-D) structure of their environment using various cues, an important
one of which is optic flow. The motion of any point’s projection on the retina depends both on the point’s movement in space
and on its distance from the eye. Therefore, retinal motion can be used to extract the 3-D structure of the environment and
the shape of objects, in a process known as structure-from-motion (SFM). However, because many combinations of 3-D structure and motion can lead to the same optic flow, SFM is an ill-posed
inverse problem. The rigidity hypothesis is a constraint supposed to formally solve the SFM problem and to account for human
performance. Recently, however, a number of psychophysical results, with both moving and stationary human observers, have
shown that the rigidity hypothesis alone cannot account for human performance in SFM tasks, but no model is known to account
for the new results. Here, we construct a Bayesian model of SFM based mainly on one new hypothesis, that of stationarity,
coupled with the rigidity hypothesis. The predictions of the model, calculated using a new and powerful methodology called
Bayesian programming, account for a wide variety of experimental findings. |
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