Discovering hierarchical motion structure
DC Element | Wert | Sprache |
---|---|---|
dc.contributor.author | Gershman, Samuel J. | |
dc.contributor.author | Tenenbaum, Joshua B. | |
dc.contributor.author | Jaekel, Frank | |
dc.date.accessioned | 2021-12-23T16:10:59Z | - |
dc.date.available | 2021-12-23T16:10:59Z | - |
dc.date.issued | 2016 | |
dc.identifier.issn | 00426989 | |
dc.identifier.uri | https://osnascholar.ub.uni-osnabrueck.de/handle/unios/9488 | - |
dc.description.abstract | Scenes filled with moving objects are often hierarchically organized: the motion of a migrating goose is nested within the flight pattern of its flock, the motion of a car is nested within the traffic pattern of other cars on the road, the motion of body parts are nested in the motion of the body. Humans perceive hierarchical structure even in stimuli with two or three moving dots. An influential theory of hierarchical motion perception holds that the visual system performs a ``vector analysis'' of moving objects, decomposing them into common and relative motions. However, this theory does not specify how to resolve ambiguity when a scene admits more than one vector analysis. We describe a Bayesian theory of vector analysis and show that it can account for classic results from dot motion experiments, as well as new experimental data. Our theory takes a step towards understanding how moving scenes are parsed into objects. (C) 2015 Elsevier Ltd. All rights reserved. | |
dc.description.sponsorship | Deutsche Forschungsgemeinschaft (DFG)German Research Foundation (DFG) [JA 1878/1-1]; ONR MURIMURIOffice of Naval Research [N00014-07-1-0937]; IARPA ICARUS program; MIT Intelligence Initiative; NSF STC awardNational Science Foundation (NSF) [CCF-1231216]; We thank Ed Vul, Liz Spelke, Jeff Beck, Alex Pouget, Yair Weiss, Ted Adelson, Rick Born, and Peter Battaglia for helpful discussions. This work was supported by the Deutsche Forschungsgemeinschaft (DFG JA 1878/1-1), ONR MURI N00014-07-1-0937, IARPA ICARUS program, the MIT Intelligence Initiative, and the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. A preliminary version of this work was presented at the 35th annual Cognitive Science Society meeting (Gershman, Jakel, & Tenenbaum, 2013). | |
dc.language.iso | en | |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | |
dc.relation.ispartof | VISION RESEARCH | |
dc.subject | Bayesian inference | |
dc.subject | INTEGRATION | |
dc.subject | MODEL | |
dc.subject | Motion perception | |
dc.subject | Neurosciences | |
dc.subject | Neurosciences & Neurology | |
dc.subject | Ophthalmology | |
dc.subject | PERCEPTION | |
dc.subject | Psychology | |
dc.subject | SEGMENTATION | |
dc.subject | Structure learning | |
dc.title | Discovering hierarchical motion structure | |
dc.type | journal article | |
dc.identifier.doi | 10.1016/j.visres.2015.03.004 | |
dc.identifier.isi | ISI:000382712600021 | |
dc.description.volume | 126 | |
dc.description.issue | SI | |
dc.description.startpage | 232 | |
dc.description.endpage | 241 | |
dc.identifier.eissn | 18785646 | |
dc.publisher.place | THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND | |
dcterms.isPartOf.abbreviation | Vision Res. | |
dcterms.oaStatus | Bronze |
Seitenaufrufe
1
Letzte Woche
0
0
Letzter Monat
0
0
geprüft am 17.05.2024