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Parallelizing Scale Invariant Feature Transform On A Distributed Memory Cluster

Year: 2011

Type: article

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.

Source: Journal of the Arkansas Academy of Science

Author: Stanislav Bobovych

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2185654323

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Cites: 25

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Related to: 10