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dc.contributor.advisorEdward H. Adelson.en_US
dc.contributor.authorWeiss, Yairen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Brain and Cognitive Sciences.en_US
dc.date.accessioned2005-08-22T20:33:01Z
dc.date.available2005-08-22T20:33:01Z
dc.date.copyright1998en_US
dc.date.issued1998en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/9354
dc.descriptionThesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 1998.en_US
dc.descriptionIncludes bibliographical references (leaves 195-204).en_US
dc.description.abstractEstimating motion in scenes containing multiple moving objects remains a difficult problem in computer vision yet is solved effortlessly by humans. In this thesis we present a computational investigation of this astonishing performance in human vision. The method we use throughout is to formulate a small number of assumptions and see the extent to which the optimal interpretation given these assumptions corresponds to the human percept. For scenes containing a single motion we show that a wide range of previously published results are predicted by a Bayesian model that finds the most probable velocity field assuming that (1) images may be noisy and (2) velocity fields are likely to be slow and smooth. The predictions agree qualitatively, and are often in remarkable agreement quantitatively. For scenes containing multiple motions we introduce the notion of "smoothness in layers". The scene is assumed to be composed of a small number of surfaces or layers, and the motion of each layer is assumed to be slow and smooth. We again formalize these assumptions in a Bayesian framework and use the statistical technique of mixture estimation to find the predicted a surprisingly wide range of previously published results that are predicted with these simple assumptions. We discuss the shortcomings of these assumptions and show how additional assumptions can be incorporated into the same framework. Taken together, the first two parts of the thesis suggest that a seemingly complex set of illusions in human motion perception may arise from a single computational strategy that is optimal under reasonable assumptions.en_US
dc.description.abstract(cont.) The third part of the thesis presents a computer vision algorithm that is based on the same assumptions. We compare the approach to recent developments in motion segmentation and illustrate its performance on real and synthetic image sequences.en_US
dc.description.statementofresponsibilityby Yair Weiss.en_US
dc.format.extent204 leavesen_US
dc.format.extent24589667 bytes
dc.format.extent24589424 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectBrain and Cognitive Sciences.en_US
dc.titleBayesian motion estimation and segmentationen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Brain and Cognitive Sciences.en_US
dc.identifier.oclc44442135en_US


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