An Improved SIFT Matching Algorithm

Can Ding; Chang Wen Qu; Feng Su

doi:10.4028/www.scientific.net/AMM.239-240.1232

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 239-240An Improved SIFT Matching Algorithm

An Improved SIFT Matching Algorithm

Abstract:

The high dimension and complexity of feature descriptor of Scale Invariant Feature Transform (SIFT), not only occupy the memory spaces, but also influence the speed of feature matching. We adopt the statistic feature point’s neighbor gradient method, the local statistic area is constructed by 8 concentric square ring feature of points-centered, compute gradient of these pixels, and statistic gradient accumulated value of 8 directions, and then descending sort them, at last normalize them. The new feature descriptor descend dimension of feature from 128 to 64, the proposed method can improve matching speed and keep matching precision at the same time.

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Periodical:

Applied Mechanics and Materials (Volumes 239-240)

Pages:

1232-1237

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.239-240.1232

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Online since:

December 2012

Authors:

Can Ding, Chang Wen Qu, Feng Su

Keywords:

Dog, Feature Descriptor, Image Matching, SIFT Algorithm

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