Analytical Assessment of Image Registration Accuracy after Projective Transformation Compensation
https://doi.org/10.15514/ISPRAS-2026-38(3)-40
Abstract
The problem of analytical assessment of image registration accuracy for projective transformations is addressed. A mathematical framework for computing the covariance matrix of transformation parameters based on linearization of the nonlinear model using Jacobian is proposed. An algorithm for constructing variance maps , and covariance for each image pixel is developed. Method validation using Monte Carlo numerical approach demonstrated high accuracy of analytical estimates (correlation > 0.999) and significant computational speedup (more than 5000 times). The scientific novelty lies in developing an analytical method for assessing local registration accuracy for nonlinear projective transformations.
About the Authors
Nikita Andreevich KHODAKOV
Ryazan State Radio Engineering University named after V. F. Utkin
Russian Federation
Russian Federation
Student at the Department of Automation and Information Technologies in Control, Ryazan State Radio Engineering University named after V.F. Utkin. Area of scientific interests: projective transformations, management cybernetics.
Pavel Vartanovich BABAYAN
Ryazan State Radio Engineering University named after V. F. Utkin
Russian Federation
Russian Federation
Cand. Sci. (Tech.), Head of the Department of Automation and Information Technologies in Control at Ryazan State Radio Engineering University (RSREU) since 2016. Area of scientific interests: computer vision, image processing, object detection and tracking, video scene modeling.
References
1. Han Y., Javed A., Jung S., Liu S. Object-Based Change Detection of Very High-Resolution Images by Fusing Pixel-Based Change Detection Results Using Weighted Dempster–Shafer Theory. Remote Sens, 2020. 12. 983. DOI: 10.3390/rs12060983
2. Zitová B., Flusser J. Image registration methods: a survey. Image and Vision Computing. 2003, vol. 21, no. 11, pp. 977-1000.
3. Sengupta D. A Comparative Study of Some Well Known Image Registration Techniques. 2018 Conference on Information and Communication Technology (CICT). IEEE, 2018, pp. 1-5.
4. Xiong Z., Zhang Y. A critical review of image registration methods. International Journal of Image and Data Fusion, 2010, vol. 1, no. 2, pp. 137-158.
5. Kuppala K., Banda S., Barige T. R. An overview of deep learning methods for image registration with focus on feature-based approaches. International Journal of Image and Data Fusion, 2020, vol. 11, no. 2, pp. 113-135.
6. Жгутова Е.С., Бабаян П.В. Алгоритмы и программное обеспечение для оценки точности совмещения изображений. GraphiCon-2024 материалы 34-й Междунар. конф. по компьютерной графике и машинному зрению (Россия, Омск, 17–19 сент. 2024 г.). Ом. гос. техн. ун-т; редкол.: Е. В. Любчинов (отв. ред.) [и др.]. Омск: Изд-во ОмГТУ, 2024, с. 484-490. DOI: 10.25206/978-5-8149-3873-2-2024-484-490.
7. Бабаян П.В., Кожина (Жгутова) Е.С. Влияние точности оценивания координат опорных участков на точность совмещения изображений. GraphiCon-2023: труды 33-й Междунар. конф. по компьютерной графике и машинному зрению (Москва, 19–21 сент. 2023 г.). М.: Институт прикладной математики им. М.В. Келдыша РАН, 2023, с. 474-481. DOI: 10.20948/graphicon-2023-474-481.
8. Бабаян П.В., Кожина (Жгутова) Е.С. Влияние точности оценивания координат опорных участков на точность совмещения изображений в системах дистанционного зондирования Земли // 8-ая международная научно-техническая конференция «В.Ф. Уткин – 100 лет со дня рождения. Космонавтика. Радиоэлектроника. Геоинформатика»: Мат. докл. Рязанс. гос. радиотехн. Университет им. В.Ф. Уткина. Рязань, 2023, с. 360-363. ISBN 978-5-7722-0388-0.
9. Ghojogh B., Nekoei H., Ghojogh A., Karray F., Crowley M. Sampling Algorithms, from Survey Sampling to Monte Carlo Methods: Tutorial and Literature Review. arXiv: Methodology. 2020. Available at: https://api.semanticscholar.org/CorpusID:226227376, accessed: 29.12.2025.
10. Fauster E., O'Leary P. L. Methods of statistical uncertainty analysis applied to evaluation algorithms of a video-extensometer system. Proceedings of SPIE, 2008, vol. 6813. DOI: 10.1117/12.766273.
Review
For citations:
KHODAKOV N.A., BABAYAN P.V. Analytical Assessment of Image Registration Accuracy after Projective Transformation Compensation. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2026;38(3):125-134. (In Russ.) https://doi.org/10.15514/ISPRAS-2026-38(3)-40
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