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Quick sift(QSIFT), an approach to reduce SIFT computational cost
Fazel, Zahra; Famouri, Mahmoud; Nazemi, Azadeh; Azimifar, Zohreh
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.
Keyword(s): Interest points; Feature detector; Feature descriptor; Feature extraction; Feature matching; natural image statistics; real-time; Lighting; Cameras; Real-time systems; Signal processing; Computational efficiency; Time complexity
Publication Date:
2018
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Fazel, Zahra, Famouri, Mahmoud, Nazemi, Azadeh ORCID: 0000-0002-1138-309X <https://orcid.org/0000-0002-1138-309X> and Azimifar, Zohreh (2018) Quick sift(QSIFT), an approach to reduce SIFT computational cost. In: 2017 Artificial Intelligence and Signal Processing Conference (AISP), 25-27 Oct. 2017, Shiraz, Iran.
Publisher(s): IEEE
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/23502/1/qsift.pdf,
http://dx.doi.org/10.1109/aisp.2017.8515128
First Indexed: 2019-07-11 06:06:24 Last Updated: 2020-09-04 06:11:57