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Improved SIFT Algorithm Image Matching

High-dimensional and complex feature descriptor of SIFT not only occupies a large memory space, but also affects the speed of feature matching. We adopt the statistic feature point’s neighbor gradient way in which the local statistic area is constructed by 8 concentric square ring feature of points-centered, and the…

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High-dimensional and complex feature descriptor of SIFT not only occupies a large memory space, but also affects the speed of feature matching. We adopt the statistic feature point’s neighbor gradient way in which the local statistic area is constructed by 8 concentric square ring feature of points-centered, and the pixels gradient as well the statistic gradient accumulated value of eight directions are computed before sorting them in descending order and standardizing them. The new feature descriptor descends feature dimension of feature from 128 to 64. The experiment reveals that the proposed method can improve matching speed and keep matching precision at the same time. http://www.joa-journal.org/paperInfo.aspx?ID=1863

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Improved SIFT Algorithm Image Matchingaspx28 KB
High-dimensional and complex feature descriptor of SIFT not only occupies a large memory space, but also affects the speed of feature matching. We adopt the statistic feature point’s neighbor gradient way in which the local statistic area is constructed by 8 concentric square ring feature of points-centered, and the pixels gradient as well the statistic gradient accumulated value of eight directions are computed before sorting them in descending order and standardizing them. The new feature descriptor descends feature dimension of feature from 128 to 64. The experiment reveals that the proposed method can improve matching speed and keep matching precision at the same time.
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