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dc.contributor.advisorWilliam T. Freeman.en_US
dc.contributor.authorRubinstein, Michael, Ph. D. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2014-06-13T22:34:04Z
dc.date.available2014-06-13T22:34:04Z
dc.date.copyright2014en_US
dc.date.issued2014en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/87934
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 113-118).en_US
dc.description.abstractOur world is constantly changing, and it is important for us to understand how our environment changes and evolves over time. A common method for capturing and communicating such changes is imagery - whether captured by consumer cameras, microscopes or satellites, images and videos provide an invaluable source of information about the time-varying nature of our world. Due to the great progress in digital photography, such images and videos are now widespread and easy to capture, yet computational models and tools for understanding and analyzing time-varying processes and trends in visual data are scarce and undeveloped. In this dissertation, we propose new computational techniques to efficiently represent, analyze and visualize both short-term and long-term temporal variation in videos and image sequences. Small-amplitude changes that are difficult or impossible to see with the naked eye, such as variation in human skin color due to blood circulation and small mechanical movements, can be extracted for further analysis, or exaggerated to become visible to an observer. Our techniques can also attenuate motions and changes to remove variation that distracts from the main temporal events of interest. The main contribution of this thesis is in advancing our knowledge on how to process spatiotemporal imagery and extract information that may not be immediately seen, so as to better understand our dynamic world through images and videos.en_US
dc.description.statementofresponsibilityby Michael Rubinstein.en_US
dc.format.extent118 pagesen_US
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/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleAnalysis and visualization of temporal variations in videoen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc880144326en_US


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