
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.
Signal processing, Feature matching, Feature detector, natural image statistics, real-time, Cameras, Computational efficiency, Time complexity, Feature extraction, Feature descriptor, Real-time systems, Interest points, Lighting
Signal processing, Feature matching, Feature detector, natural image statistics, real-time, Cameras, Computational efficiency, Time complexity, Feature extraction, Feature descriptor, Real-time systems, Interest points, Lighting
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