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Abstract:
Scale invariant feature transform (SIFT) is a local feature descriptor that searches from extreme points in spatial scales. Aiming at the speeding up the matching process of the traditional Scale-invariant Feature Transform (SIFT) algorithm, a fast image matching method based on improved SIFT is proposed. It eliminates some invalid feature points by calculating the two-dimensional entropy of feature points. in the feature extraction stage, eliminating the feature points with larger two-dimensional entropy. In the feature point matching stage, a reliable matching pair is found by traversing the difference of two-dimensional entropy between a feature point of the reference image and a feature point of the image to be matched. this will reduce the computation of Euclidean distance in matching process. The experimental results show that the improved algorithm has a capacity to effectively improve the image matching speed on the premise of maintaining stability, and it is 1.6 times as much as the original matching speed. © 2019, The Press of NUC. All right reserved.
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Journal of North University of China (Natural Science Edition)
ISSN: 1673-3193
Year: 2019
Issue: 1
Volume: 40
Page: 63-69
Cited Count:
WoS CC Cited Count: 0
SCOPUS Cited Count: 2
ESI Highly Cited Papers on the List: 0 Unfold All
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30 Days PV: 0
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