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An OpenCL-based feature matcher
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AI
DOI：10.1016/j.image.2012.06.002.png)
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Abstract
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Nowadays visual search is one of the most active branches of computer vision. It relies on finding invariant points inside images, describing them into features and then matching these features against a reference database to identify objects in the scene or the entire photo (environment). In this paper, we discuss an approach to feature matching that exploits the capabilities of modern GPUs to speed up the aforementioned and that keeps low the number of false matches. (c) 2012 Elsevier B.V. All rights reserved.
Keywords:
Visual search
GPU computing
Invariant features
Feature matching
OpenCL
SIFT
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