An improved ORB algorithm for the Unmanned Aerial Vehicle (UAV) image stitching task

Qicheng Yan; Hao Qiu

doi:10.1117/12.2644208

9 September 2022 An improved ORB algorithm for the Unmanned Aerial Vehicle (UAV) image stitching task

Qicheng Yan, Hao Qiu

Proceedings Volume 12328, Second International Conference on Optics and Image Processing (ICOIP 2022); 123281I (2022) https://doi.org/10.1117/12.2644208
Event: Second International Conference on Optics and Image Processing (ICOIP 2022), 2022, Taian, China

Abstract

Based on the image chunking processing theory, this paper proposes an improved ORB-based UAV image stitching algorithm that takes the pyramidal optical flow characteristics into consideration to overcome the weaknesses of the traditional algorithm (proneness to feature point aggregation, uneven distribution, and low feature matching accuracy). Firstly, the ORB algorithm is used to detect the feature points using the image chunking method of constructing moving masks. Subsequently, the non-maximum suppression method is used to reject the feature points aggregated in each mask, and the Hamming distance is used for feature matching after traversal. Following the step, the pyramid optical flow method is used to correct the motion displacement vector of the feature points and reject the mis-matched pairs after matching. Finally, the RANSAC algorithm is used to filter the redundant pairs to further improve the accuracy, and then the image is stitched together using the optimal stitching seam strategy and the fade-in-and-fade-out fusion algorithm. Overall, the aforementioned procedure increased the alignment rate to approximately 97% in the alignment stage, achieving a better alignment accuracy and a more accurate stitching effect.

Citation Download Citation

Qicheng Yan and Hao Qiu "An improved ORB algorithm for the Unmanned Aerial Vehicle (UAV) image stitching task", Proc. SPIE 12328, Second International Conference on Optics and Image Processing (ICOIP 2022), 123281I (9 September 2022); https://doi.org/10.1117/12.2644208

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available