Parallelizing Scale Invariant Feature Transform on a Distributed Memory Cluster

Authors

Stanislav Bobovych, University of Arkansas, FayettevilleFollow

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.

Recommended Citation

Bobovych, Stanislav (2011) "Parallelizing Scale Invariant Feature Transform on a Distributed Memory Cluster," Inquiry: The University of Arkansas Undergraduate Research Journal: Vol. 12, Article 8.

Available at: https://scholarworks.uark.edu/inquiry/vol12/iss1/8