Parallelizing Scale Invariant Feature Transform On A Distributed Memory Cluster

Author

Stanislav Bobovych, University of Arkansas, FayettevilleFollow

Date of Graduation

5-2011

Document Type

Thesis

Degree Name

Bachelor of Science in Computer Engineering

Degree Level

Undergraduate

Department

Computer Science and Computer Engineering

Advisor/Mentor

Apon, Amy

Committee Member/Reader

Cothren, Jackson

Committee Member/Second Reader

Parkerson, James

Abstract

Scale Invariant Feature Transform (SIFT) is a computer vision algorithm that is widely-used to extract features from images. We explored accelerating an existing implementation of this algorithm with message passing in order to analyze large data sets. We successfully tested two approaches to data decomposition in order to parallelize SIFT on a distributed memory cluster.

Keywords

Scale Invariant Feature Transform; Data Decomposition

Citation

Bobovych, S. (2011). Parallelizing Scale Invariant Feature Transform On A Distributed Memory Cluster. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/40