| dc.contributor.advisor | Chad Sweet and Seth Teller. | en_US |
| dc.contributor.author | Kayombya, Guy-Richard | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2011-02-23T14:22:33Z | |
| dc.date.available | 2011-02-23T14:22:33Z | |
| dc.date.copyright | 2010 | en_US |
| dc.date.issued | 2010 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/61162 | |
| dc.description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. | en_US |
| dc.description | Page 46 blank. Cataloged from PDF version of thesis. | en_US |
| dc.description | Includes bibliographical references (p. 45). | en_US |
| dc.description.abstract | SIFT 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.statementofresponsibility | by Guy-Richard Kayombya. | en_US |
| dc.format.extent | 46 p. | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | M.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.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | SIFT feature extraction on a Smartphone GPU using OpenGL ES2.0 | en_US |
| dc.title.alternative | Scale Invariant Feature Transform feature extraction on a Smartphone Graphics Processing Unit OpenGL ES2.0 | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | M.Eng. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| dc.identifier.oclc | 698260952 | en_US |
