Abstract
Abstract. A UAV image matching method based on RANSAC (Random Sample Consensus) algorithm and SURF (speeded up robust features) algorithm is proposed. The SURF algorithm is integrated with fast operation and good rotation invariance, scale invariance and illumination. The brightness is invariant and the robustness is good. The RANSAC algorithm can effectively eliminate the characteristics of mismatched point pairs. The pre-verification experiment and basic verification experiment are added to the RANSAC algorithm, which improves the rejection and running speed of the algorithm. The experimental results show that compared with the SURF algorithm, SIFT (Scale Invariant Feature Transform) algorithm and ORB (Oriented FAST and Rotated BRIEF) algorithm, the proposed algorithm is superior to other algorithms in terms of matching accuracy and matching speed, and the robustness is higher.
Highlights
1.1 General InstructionsImage matching is a process of finding the same name point between two or more images by using a certain matching algorithm
In 2008, H.Baymmm improved on the basis of SIFT algorithm and proposed SURF algorithm, which greatly accelerated the speed of feature extraction
Experiments show that SURF algorithm is about three times faster than SIFT algorithm in operation speed, and its comprehensive performance is better than SIFT algorithm
Summary
Image matching is a process of finding the same name point between two or more images by using a certain matching algorithm. The image feature matching algorithms recognized at home and abroad include SIFT (Scale Invariant Feature Transform) algorithm (Lowe D G, 1999), SURF(Speeded Up Robust Features) algorithm (Bay H, et al,2008), ORB (Oriented Fast and Rotated Brief) algorithm. In 2008, H.Baymmm improved on the basis of SIFT algorithm and proposed SURF algorithm, which greatly accelerated the speed of feature extraction. Experiments show that SURF algorithm is about three times faster than SIFT algorithm in operation speed, and its comprehensive performance is better than SIFT algorithm. It has good rotation invariance, scale invariance, illumination invariance and robustness(Rongchao Q , et al, 2016; Fei X). The classical SURF image matching algorithm is greatly influenced by the local region pixel gradient direction when generating feature vectors describing feature points, which leads to inaccurate main
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