An Efficient Image Matching Algorithm Based on Adaptive Threshold and RANSAC

The education plays a more and more important role in disseminating knowledge because of the explosive growth of knowledge. As one kind of carrier delivering knowledge, image also presents an explosive growth trend and plays an increasingly important role in education, medical, advertising, entertai...

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Bibliographic Details
Published in:IEEE access 2018, Vol.6, p.66963-66971
Main Authors: Li, Hao, Qin, Jiaohua, Xiang, Xuyu, Pan, Lili, Ma, Wentao, Xiong, Neal N.
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive filters
Adaptive sampling
Adaptive threshold
Algorithms
Corner detection
Correlation
Cross correlation
digital campus
Education
Feature extraction
Harris corner detection
Image matching
Image registration
Matching
random sample consensus
Streaming media
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Summary:The education plays a more and more important role in disseminating knowledge because of the explosive growth of knowledge. As one kind of carrier delivering knowledge, image also presents an explosive growth trend and plays an increasingly important role in education, medical, advertising, entertainment, and so on. Aiming at the long time of massive image feature extraction in the construction of smart campus, the traditional Harris corner has problems, such as low detection efficiency and many non-maximal pseudocorner points. This paper proposes a Harris image matching method that combines adaptive threshold and random sample consensus (RANSAC). First, the Harris feature points are selected based on the adaptive threshold and the Forstner algorithm in this method. On the one hand, candidate points are filtered based on the adaptive threshold. On the other hand, the Forstner algorithm is used to further select the corner points. Second, the normalized cross correlation matching and the RANSAC are applied to precisely match the detected Harris corners. The experimental results show that compared with the existing algorithms, the proposed method not only obtains a matching accuracy higher than 20% of Cui's algorithm but also saves more than 30% detection time of corner detection and image matching. Furthermore, the proposed method obtains a matching accuracy higher than 50% of the Cui's algorithm and saves more than 50% detection time of corner detection and image matching.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2878147