Abstract:
In this paper, we study methods for determining parameters of camera movement from a set of corresponding points. Unlike the traditional approach, the corresponding points in this paper are not used to determine the fundamental matrix, but directly to determine motion parameters. In addition, in this work, we use a multi-angle image formation model based on the representation of three-dimensional images and motion parameters in the form of quaternions. We propose method for determining motion parameters, including the selection of the most noise-free matches using the RANSAC method. The study presents results of an experiment on the “Middlebury” and “ETH3D” test kits, which contains a set of images with known values of the motion parameters. Using a program written in Python, a comparative experiment was conducted to evaluate the accuracy and reliability of the estimates obtained using the proposed method under conditions of a small number of corresponding points and a shallow depth of the scene. In the course of experimental studies, it was shown that under the above-described conditions, the reliability of parameter determination using the proposed method significantly exceeds the reliability of traditional methods for estimating motion parameters based on the calculation of the fundamental matrix.
This work was supported by the Russian Foundation for Basic Research (projects No. 17-29-03112, 19-29-01235) and the RF Ministry of Science and Higher Education within a state contract with the “Crystallography and Photonics” Research Center of the RAS under agreement 007-ГЗ/Ч3363/26.
Received: 23.12.2019 Accepted: 26.02.2020
Document Type:
Article
Language: Russian
Citation:
E. V. Goshin, A. P. Kotov, “Method for camera motion parameter estimation from a small number of corresponding points using quaternions”, Computer Optics, 44:3 (2020), 446–453
\Bibitem{GosKot20}
\by E.~V.~Goshin, A.~P.~Kotov
\paper Method for camera motion parameter estimation from a small number of corresponding points using quaternions
\jour Computer Optics
\yr 2020
\vol 44
\issue 3
\pages 446--453
\mathnet{http://mi.mathnet.ru/co808}
\crossref{https://doi.org/10.18287/2412-6179-CO-683}
Linking options:
https://www.mathnet.ru/eng/co808
https://www.mathnet.ru/eng/co/v44/i3/p446
This publication is cited in the following 9 articles:
V. A. Fursov, E. V. Goshin, E. Yu. Minaev, D. A. Zherdev, A. P. Kotov, “Methods for Processing and Understanding Image Sequences in Autonomous Navigation Problems”, Pattern Recognit. Image Anal., 33:4 (2023), 1104
Y. Goshin, “Coplanarity-Based Approach for Camera Motion Estimation Invariant to the Scene Depth”, Opt. Mem. Neural Networks, 31:S1 (2022), 22
Daniil Kozlov, Vladislav Myasnikov, 2021 International Conference on Information Technology and Nanotechnology (ITNT), 2021, 1
Igor A. Kudinov, Mikhail B. Nikiforov, Ivan S. Kholopov, 2021 International Conference on Information Technology and Nanotechnology (ITNT), 2021, 1
I. A. Kudinov, M. B. Nikiforov, I. S. Kholopov, “Strategies for generating panoramic video images without information about scene correspondences for multispectral distributed aperture systems”, Computer Optics, 45:4 (2021), 589–599
Vladimir Fursov, Evgeniy Minaev, Anton Kotov, 2021 International Conference on Information Technology and Nanotechnology (ITNT), 2021, 1
Larisa Zherdeva, Evgeniy Minaev, Denis Zherdev, Vladimir Fursov, 2021 International Conference on Information Technology and Nanotechnology (ITNT), 2021, 1
Anton Kotov, Yegor Goshin, 2021 International Conference on Information Technology and Nanotechnology (ITNT), 2021, 1
Sergei Stepanenko, Pavel Yakimov, 2020 International Conference on Information Technology and Nanotechnology (ITNT), 2020, 1
Citation in format AMSBIB
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