Application of an Improved Orb Algorithm in Image Matching
DOI:
https://doi.org/10.71222/5wwfaf29Keywords:
orb algorithm, information entropy, feature point, LBP algorithm, gradientAbstract
An improved ORB feature matching algorithm is proposed to solve the problem of high mismatching rate of non-uniform distribution of feature points extracted by traditional ORB algorithms. First of all, to extract feature points of image block, and then calculate each block information entropy, the entropy value dynamically setting the threshold of each block to extract the feature points, make to extract the feature points of distribution more uniform, improve the subsequent registration accuracy. Secondly, for the problem of high mismatch rate caused by insufficient use of local information by the rBRIEF descriptor in the ORB algorithm, on the basis of introducing gradient information to improve the LBP algorithm, an improved descriptor combining rBRIEF and LBP is proposed. The 128-bit descriptor generated by the improved LBP algorithm is used to replace the 128-bit descriptor with the smallest variance in the original rBRIEF to improve the stability of the feature point description. Finally, RANSAC was used to delete the mismatched points. Results Compared with the traditional ORB algorithm, the improved algorithm reduces the time overhead by 8% and the average mismatch rate by about 10%. The improved orb algorithm has lower mismatch rate and higher registration speed.References
1. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, vol. 60, no. 2, pp. 91-110, 2004. doi: 10.1023/b:visi.0000029664.99615.94
2. H. Bay, T. Tuytelaars, and L. Van Gool, "Surf: Speeded up robust features," In European conference on computer vision, May, 2006, pp. 404-417. doi: 10.1007/11744023_32
3. E. Rosten, R. Porter, and T. Drummond, "Faster and better: A machine learning approach to corner detection," IEEE transactions on pattern analysis and machine intelligence, vol. 32, no. 1, pp. 105-119, 2008.
4. M. S. Shang, "An image registration method based on SIFT and ORB," In 2022 2nd Conference on High Performance Computing and Communication Engineering (HPCCE 2022), April, 2023, pp. 366-373.
5. C. Wang, W. Jiao, S. Zhang, and Y. Song, "Improved ORB Image Matching Algorithm Based on Local Information Entropy," In 2023 5th International Conference on Frontiers Technology of Information and Computer (ICFTIC), November, 2023, pp. 658-663. doi: 10.1109/icftic59930.2023.10456220
6. B. Li, J. Wang, J. Chen, and Z. Sun, "OGIL: An Improved ORB Algorithm Based on GMS, Information Entropy and LC," In 2023 35th Chinese Control and Decision Conference (CCDC), May, 2023, pp. 1164-1169.
7. W. Wang, Z. Xiong, and Z. Liu, "ORB Feature Uniform Distribution Algorithm Integrating Quadtree and Adaptive Thresholding," In Journal of Physics: Conference Series, May, 2025, p. 012016. doi: 10.1088/1742-6596/3024/1/012016
8. Z. Tang, Y. Dou, X. Wang, and L. Zhang, "Enhanced Feature Matching: Entropy-Guided ORB with Adaptive Descriptors," 2025. doi: 10.21203/rs.3.rs-7535082/v1
9. Q. Liu, X. Xi, W. Zhang, L. Yang, and N. Hanajima, "Improved image matching algorithm based on LK optical flow and grid motion statistics," International Journal of Computer Applications in Technology, vol. 68, no. 1, pp. 49-57, 2022.
10. L. Li, L. Wu, and Y. Gao, "Improved image matching method based on ORB," In 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), May, 2016, pp. 465-468. doi: 10.1109/snpd.2016.7515941
11. C. Liu, and S. Dang, "The research and application of improved ORB feature matching algorithm," In International Conference on Guidance, Navigation and Control, August, 2022, pp. 4246-4255. doi: 10.1007/978-981-19-6613-2_413
12. C. Ma, X. Hu, L. Fu, and G. Zhang, "An improved ORB algorithm based on multi-feature fusion," In 2018 IEEE 27th International Symposium on Industrial Electronics (ISIE), June, 2018, pp. 729-734. doi: 10.1109/isie.2018.8433586
13. Y. Zhang, C. Li, C. Cao, and Y. Gao, "An improved Orb feature point matching algorithm," In Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence, December, 2018, pp. 207-211. doi: 10.1145/3297156.3297184
14. L. Zhao, J. Yang, Y. Zhang, and J. Huang, "Research on feature matching of an improved orb algorithm," In 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC), March, 2022, pp. 765-769. doi: 10.1109/itoec53115.2022.9734583
15. H. Sun, L. Wei, Y. Jiang, Y. Su, S. Deng, H. Sun, and H. Wang, "Research on Mobile Robot Localization Algorithm with Improved ORB Extraction Matching," In 2024 IEEE International Conference on Mechatronics and Automation (ICMA), August, 2024, pp. 351-356. doi: 10.1109/icma61710.2024.10633169
16. Q. Huang, X. Guo, Y. Wang, H. Sun, and L. Yang, "A survey of feature matching methods," IET Image Processing, vol. 18, no. 6, pp. 1385-1410, 2024. doi: 10.1049/ipr2.13032
17. C. Yao, H. Zhang, J. Zhu, D. Fan, Y. Fang, and L. Tang, "ORB feature matching algorithm based on multi-scale feature description fusion and feature point mapping error correction," IEEE Access, vol. 11, pp. 63808-63820, 2023. doi: 10.1109/access.2023.3288594
18. S. Wang, A. Zhang, and Y. Li, "Feature extraction algorithm based on improved ORB with adaptive threshold," In 2023 IEEE International Conference on Industrial Technology (ICIT), April, 2023, pp. 1-6. doi: 10.1109/icit58465.2023.10143089
19. K. Mikolajczyk, and C. Schmid, "A performance evaluation of local descriptors," IEEE transactions on pattern analysis and machine intelligence, vol. 27, no. 10, pp. 1615-1630, 2005. doi: 10.1109/tpami.2005.188
20. Y. Cong, "Image stitching technology for police drones using an improved image registration method incorporating ORB algorithm," Informatica, vol. 48, no. 2, 2024. doi: 10.31449/inf.v48i2.5877
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Copyright (c) 2026 Xiaofei Deng, Joan Lazaro (Author)
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