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dc.contributor.authorAgrawal, Harshen_US
dc.date.accessioned2016-06-20T17:53:43Z
dc.date.available2016-06-20T17:53:43Z
dc.date.issued2016-06-20en_US
dc.identifier.othervt_gsexam:7886en_US
dc.identifier.urihttp://hdl.handle.net/10919/71381
dc.description.abstractWe are witnessing a proliferation of massive visual data. Visual content is arguably the fastest growing data on the web. Photo-sharing websites like Flickr and Facebook now host more than 6 and 90 billion photos, respectively. Unfortunately, scaling existing computer vision algorithms to large datasets leaves researchers repeatedly solving the same algorithmic and infrastructural problems. Designing and implementing efficient and provably correct computer vision algorithms is extremely challenging. Researchers must repeatedly solve the same low-level problems: building and maintaining a cluster of machines, formulating each component of the computer vision pipeline, designing new deep learning layers, writing custom hardware wrappers, etc. This thesis introduces CloudCV, an ambitious system that contain algorithms for end-to-end processing of visual content. The goal of the project is to democratize computer vision; one should not have to be a computer vision, big data and deep learning expert to have access to state-of-the-art distributed computer vision algorithms. We provide researchers, students and developers access to state-of-art distributed computer vision and deep learning algorithms as a cloud service through web interface and APIs.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.subjectDeep Learningen_US
dc.subjectComputer Visionen_US
dc.subjectCloud Computingen_US
dc.titleCloudCV: Deep Learning and Computer Vision on the Clouden_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Engineeringen_US
dc.contributor.committeechairBatra, Dhruven_US
dc.contributor.committeememberAbbott, Amos L.en_US
dc.contributor.committeememberParikh, Devien_US


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