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dc.contributor.advisorRuth Rosenholtz and Edward Adelson.en_US
dc.contributor.authorRaj, Alvin Andrewen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2009-06-30T16:32:34Z
dc.date.available2009-06-30T16:32:34Z
dc.date.copyright2008en_US
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/45886
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.en_US
dc.descriptionAt head of title on title-page: Master of Science thesis.en_US
dc.descriptionIncludes bibliographical references (p. 43-44).en_US
dc.description.abstractThis thesis extends the Statistical Saliency Model to include motion as a feature, enabling it to compute the saliency of video sequences more effectively. The motion feature is represented as optical flow and incorporated into the model. The model is validated by testing its capability in predicting reaction time performance in a driving simulator. We find that the model does help predict reaction time and some eye-movements in some simulated driving tasks.en_US
dc.description.statementofresponsibilityby Alvin Raj.en_US
dc.format.extent53 p.en_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.titleStatistical Saliency Model incorporating motion saliency and an application to drivingen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc320434597en_US


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