NDLI: Extracting multi-size local descriptors by GPU computing

Content Provider	IEEE Xplore Digital Library
Author	Ichimura, N.
Copyright Year	2011
Description	Author affiliation: National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1, Umezono, Tsukuba, Ibaraki 305-8568, Japan (Ichimura, N.)
Abstract	This paper presents fast computational techniques for extracting local descriptors from multiple local regions associated with an image feature such as a feature point. Multiple local regions with different sizes are detected by multiplying multiple scale factors to the characteristic scale of the image feature. The descriptors obtained from multiple local regions are called multi-size local descriptors. Multi-size local descriptors enable us to use various types of feature representation and matching schemes based on many different spatial sizes, which is a promising way to control the balance among the robustness against for occlusions, the invariance, and the distinctiveness of the descriptors to the contents of scenes. Because multi-size local descriptors increases the computational costs of feature extraction, we introduce parallel computational techniques for extracting the multi-size local descriptors consisting of the histograms of gradient orientations through the use of a graphics processing unit (GPU). In particular, we demonstrate that orientation maps are useful for efficient extraction of the multi-size local descriptors. Using orientation maps, we can calculate the descriptors by a table look-up manner. We show implementation details and then conclude with the experimental results that demonstrate the usefulness of GPU computing with orientation maps.
Starting Page	1
Ending Page	6
File Size	2315441
Page Count	6
File Format	PDF
ISBN	9781612843483
ISSN	19457871
e-ISBN	9781612843506
e-ISBN	9781612843490
DOI	10.1109/ICME.2011.6012157
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-11
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Graphics processing unit Histograms Image edge detection Image resolution Robustness Computational efficiency GPU computing local invariant features local regions orientation maps
Content Type	Text
Resource Type	Article

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