Integrating Harris Corner Detector and SIFT Descriptor for High-Accuracy Feature Matching in Image Processing
DOI:
https://doi.org/10.29304/jqcsm.2025.17.32455Keywords:
Harris Corner, SIFT DescriptorAbstract
Feature matching is one of the primary operations in image processing and computer vision. In this work, the limitations of the Harris corner detector—sensitivity to scale and noise—are highlighted and its performance enhanced through the integration of the SIFT descriptor that is invariant to rotation and scale. The aim is to make feature matching more accurate and robust under varied conditions. The approach that was suggested was implemented on the Ishtar Gate, the Egyptian Pyramids, and the Save Iraqi Culture Monument photos. The process involved keypoint detection with Harris, description with SIFT, and feature matching using the nearest neighbor distance ratio. Precise and accurate results were obtained, particularly with structured images like the Ishtar Gate (100% precision, 99% accuracy). The joint approach is strong in overcoming conventional detector limitations and improving reliability in image matching. There is potential for further research in extending the model to medical imaging and real-time applications.
Downloads
References
Fu, Y., Lei, Y., Wang, T., Curran, W. J., Liu, T., & Yang, X. (2020). Deep learning in medical image registration: a review. Physics in Medicine & Biology, 65(20), 20TR01.
Bekkanti, A., Karimunnisa, S., Gogulamudi, S., Kumar, K. T. P. S., & Basha, C. Z. (2020, September). Enhanced computerized bone fracture detection using Harris corner detection. In 2020 International Conference on Smart Electronics and Communication (ICOSEC) (pp. 572-576). IEEE.
Harris, C. G., Jursik, N. J., Rochefort, W. E., & Walker, T. W. (2019). Additive manufacturing with soft TPU–adhesion strength in multimaterial flexible joints. Frontiers in Mechanical Engineering, 5, 37.
Bojanić, D., Bartol, K., Pribanić, T., Petković, T., Donoso, Y. D., & Mas, J. S. (2019, September). On the comparison of classic and deep keypoint detector and descriptor methods. In 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) (pp. 64-69). IEEE.
J. Qin et al., “An Encrypted Image Retrieval Method Based on Harris Corner Optimization and LSH in Cloud Computing,” IEEE Access, vol. 7, pp. 24626–24633, 2019, doi: 10.1109/ACCESS.2019.2894673.
Feng, J., Ai, C., An, Z., Zhou, Z., & Shi, Y. (2019, July). A feature detection and matching algorithm based on the Harris Algorithm. In 2019 International Conference on Communications, Information Systems and Computer Engineering (CISCE) (pp. 616-621). IEEE.
Efe, U., Ince, K. G., & Alatan, A. (2021). Dfm: A performance baseline for deep feature matching. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 4284-4293).
F. Bellavia and D. Mishkin, “Harris Corner Selection for Next-Gen Image Matching Pipelines,” no. 1981, pp. 1–8, 2021, [Online]. Available: http://arxiv.org/abs/2109.12925
F. Bellavia and D. Mishkin, “HarrisZ$^+$: Harris Corner Selection for Next-Gen Image Matching Pipelines,” no. 1981, pp. 1–8, 2021, [Online]. Available: http://arxiv.org/abs/2109.12925.
Wang, Z., Yu, A., Dong, Z., Zhang, B., & Chen, X. (2022). Performance Evaluation of Interest Point Detectors for Heterologous Image Matching. Remote Sensing, 14(15), 3724.
Sharma, S. K., Jain, K., & Shukla, A. K. (2023). A comparative analysis of feature detectors and descriptors for image stitching. Applied Sciences, 13(10), 6015.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ali Salam Al-jaberi, Sura Fadhil Rahman, Ihsan Faisal Raheem
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
