Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision
In order to estimate the motion of an object, the visual system needs to combine multiple local measurements, each of which carries some degree of ambiguity. We present a model of motion perception whereby measurements from different image regions are combined according to a Bayesian estimator --- t...
| Main Authors: | , |
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| Language: | en_US |
| Published: |
2004
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| Online Access: | http://hdl.handle.net/1721.1/7252 |
| _version_ | 1826191682075361280 |
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| author | Weiss, Yar Adelson, Edward H. |
| author_facet | Weiss, Yar Adelson, Edward H. |
| author_sort | Weiss, Yar |
| collection | MIT |
| description | In order to estimate the motion of an object, the visual system needs to combine multiple local measurements, each of which carries some degree of ambiguity. We present a model of motion perception whereby measurements from different image regions are combined according to a Bayesian estimator --- the estimated motion maximizes the posterior probability assuming a prior favoring slow and smooth velocities. In reviewing a large number of previously published phenomena we find that the Bayesian estimator predicts a wide range of psychophysical results. This suggests that the seemingly complex set of illusions arise from a single computational strategy that is optimal under reasonable assumptions. |
| first_indexed | 2024-09-23T08:59:41Z |
| id | mit-1721.1/7252 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:59:41Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/72522019-04-10T20:07:05Z Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision Weiss, Yar Adelson, Edward H. In order to estimate the motion of an object, the visual system needs to combine multiple local measurements, each of which carries some degree of ambiguity. We present a model of motion perception whereby measurements from different image regions are combined according to a Bayesian estimator --- the estimated motion maximizes the posterior probability assuming a prior favoring slow and smooth velocities. In reviewing a large number of previously published phenomena we find that the Bayesian estimator predicts a wide range of psychophysical results. This suggests that the seemingly complex set of illusions arise from a single computational strategy that is optimal under reasonable assumptions. 2004-10-20T21:04:17Z 2004-10-20T21:04:17Z 1998-02-01 AIM-1624 CBCL-158 http://hdl.handle.net/1721.1/7252 en_US AIM-1624 CBCL-158 7828604 bytes 1388106 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | Weiss, Yar Adelson, Edward H. Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision |
| title | Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision |
| title_full | Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision |
| title_fullStr | Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision |
| title_full_unstemmed | Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision |
| title_short | Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision |
| title_sort | slow and smooth a bayesian theory for the combination of local motion signals in human vision |
| url | http://hdl.handle.net/1721.1/7252 |
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