Quick sift(QSIFT), an approach to reduce SIFT computational cost

Zahra Fazel, M. Famouri, A. Nazemi, Z. Azimifar
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引用次数: 1

Abstract

SIFT has been proven to be the most robust local rotation and illumination invariant feature descriptor. Being fully scale invariant is the most important advantage of this descriptor. The major drawback of SIFT is time complexity which prevents utilizing SIFT in real-time applications. This paper describes a method to increase the speed of SIFT feature extraction using keypoint estimation and approximation instead of keypoint calculation in various scales. This research attempts to decrease SIFT computational cost without sacrificing performance and propose quick SIFT method (QSIFT). The recent researches in this area have approved that direct feature value computation is more expensive than the value extrapolation. Consequently, the contribution of this research is to reduces the time execution without losing accuracy.
快速筛选(QSIFT)是一种降低sift计算成本的方法
SIFT已被证明是最鲁棒的局部旋转和光照不变性特征描述子。全尺度不变性是该描述符最重要的优点。SIFT的主要缺点是时间复杂性,这阻碍了在实时应用中利用SIFT。本文提出了一种在不同尺度下,利用关键点估计和近似代替关键点计算来提高SIFT特征提取速度的方法。本研究试图在不牺牲性能的前提下降低SIFT计算成本,提出快速SIFT方法(QSIFT)。近年来在该领域的研究表明,直接计算特征值比外推值成本更高。因此,本研究的贡献是在不损失准确性的情况下减少执行时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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