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dc.contributor.advisorChad Sweet and Seth Teller.en_US
dc.contributor.authorKayombya, Guy-Richarden_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2011-02-23T14:22:33Z
dc.date.available2011-02-23T14:22:33Z
dc.date.copyright2010en_US
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/61162
dc.descriptionThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.en_US
dc.descriptionPage 46 blank. Cataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 45).en_US
dc.description.abstractSIFT describes local features in image used for object recognition in a vast array of application, such as augmented reality, panorama stitching. These applications are becoming very popular on Smartphones but also require considerable amount of computing power. GPUs offer a significant amount of untapped computing power that can help increase performance and improve user experience. We explore the feasibility of parallel heterogeneous computing on current generation of Smartphone. We show that the CPU and GPU can work in tandem to solve complex problems. However the mobile platform remains very restrictive requires a lot of effort from the programmer but does not achieve the same performance gains as observed on the PC.en_US
dc.description.statementofresponsibilityby Guy-Richard Kayombya.en_US
dc.format.extent46 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.titleSIFT feature extraction on a Smartphone GPU using OpenGL ES2.0en_US
dc.title.alternativeScale Invariant Feature Transform feature extraction on a Smartphone Graphics Processing Unit OpenGL ES2.0en_US
dc.typeThesisen_US
dc.description.degreeM.Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc698260952en_US


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