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dc.contributor.authorChavali, Neelimaen_US
dc.date.accessioned2015-09-18T20:07:10Z
dc.date.available2015-09-18T20:07:10Z
dc.date.issued2015-09-09en_US
dc.identifier.othervt_gsexam:6229en_US
dc.identifier.urihttp://hdl.handle.net/10919/56590
dc.description.abstractObject recognition is a central problem in computer vision which deals with both localizing and identifying objects in images. Object proposals have recently become an important part of the object recognition process. Object proposals are algorithms used for localizing objects in images. This thesis is a study in object proposals and is composed of three parts. First, we present a new data-driven approach for generating object proposals. Second, we release a MATLAB library which can be used to generate object proposals using all the existing algorithms. The library can also be used for evaluating object proposals using the three most commonly used metrics. Finally, we identify previously unnoticed bias in the existing protocol for evaluating object proposals and propose ways to alleviate this bias.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectObject proposalsen_US
dc.subjectevaluationen_US
dc.subjectcomputer visionen_US
dc.titleObject Proposals in Computer Visionen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMSen_US
thesis.degree.nameMSen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical Engineeringen_US
dc.contributor.committeechairBatra, Dhruven_US
dc.contributor.committeememberParikh, Devien_US
dc.contributor.committeememberAbbott, Amos Len_US


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