SIFT feature extraction on a Smartphone GPU using OpenGL ES2.0

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2010

Author dc:creator

• Kayombya, Guy-Richard

Advisor dc:contributor.advisor

• Chad Sweet and Seth Teller.

• Electrical Engineering and Computer Science.

Statement 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/61162

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/61162