Preview

Mekhatronika, Avtomatizatsiya, Upravlenie

Advanced search

A Method for Determination of the Extrinsic Camera Parameters from a Pair of Images with the Use of Dual Quaternions

https://doi.org/10.17587/mau.18.279-284

Abstract

Real-world problems associated with the use of the moving vehicles present a problem for estimation of the unknown motion parameters on the basis of the data obtained from a static camera set on the surface of those vehicles. The initial data (in absence of information about the vehicle motion) are the images obtained from different viewpoints. The traditional approach to estimation of the rotation and translation parameters, which consists in determination of the so-called fundamental matrix and the subsequent calculation of the required matrices has several drawbacks. In particular, if the largest portion of the predefined points in the images is located on the same plane, the fundamental matrix estimation involves serious errors, which, in turn, lead to errors in estimation of the camera parameters. Unlike in the traditional approach, in this paper the corresponding points are used to estimate not the fundamental matrix, but the internal and external camera parameters directly. Besides, this paper presents a multiple view geometry model, based on three-dimensional images and camera parameters in the form of dual quaternions. The proposed approach to the problem is a new method of estimation of the unknown camera parameters, which is more accurate and reliable compared with the traditional one. This method was implemented as a program in C + +. Using the developed program, the authors carried out an experiment to establish a correlation between the errors in the input data (points of coordinates on the planes of the camera) and the errors in the estimated rotation and translation parameters. As the result, it was confirmed that the accuracy of the parameters' estimation in most cases surpasses the quality of the results obtained by using the fundamental matrix.

About the Authors

Ye. V. Goshin
Samara National Research University; Image Processing Systems Institute, RAS, Branch of Crystallography and Photonics, RAS
Russian Federation


I. R. Useinova
Samara National Research University
Russian Federation


References

1. Caldini A., Fanfani M., Colombo C. Smartphone-Based Obstacle Detection for the Visually Impaired // Image Analysis and Processing- ICIAP 2015. Springer International Publishing, 2015. P. 480-488.

2. Callow N., Leopold M., May S. M. Surface and sub-surface anatomy of the landscape: integrating Unmanned Aerial Vehicle Structure from Motion (UAV-SfM) and Ground Penetrating Radar (GRP) to investigate sedimentary features in the field.-an example from NW Australia // EGU General Assembly Conference Abstracts. 2015. Т. 17. P. 8621.

3. Hesse R. Combining structure-from-motion with high and intermediate resolution satellite images to document threats to archaeological heritage in arid environments // Journal of Cultural Heritage. 2015. Т. 16. №. 2. P. 192-201.

4. Hartley R., Zisserman A. Multiple view geometry in computer vision // Cambridge university press. 2003. 271 p.

5. Форсайт Д., Понс Ж. Компьютерное зрение. Современный подход. М.: Издательский дом "Вильямс", 2004. 928 с.

6. Грузман И. С., Киричук В. С., Косых В. П. и др. Цифровая обработка изображений в информационных системах: учеб. пособ. Новосибирск: Изд-во НГТУ, 2002. 352 c.

7. Csurka G., Zeller C., Zhang Z., Faugeras O. Characterizing the uncertainty of the fundamental matrix // Computer Vision and Image Understanding. 1997. Vol. 68 (1). P. 18-36.

8. Hartley R. I. In defence of the 8-point algorithm // Proc. of the 5th International Conference on Computer Vision (Boston, MA, June). 1995. P. 1064-1070.

9. Гошин Е. В., Фурсов В. А. Реконструкция 3D-сцен по разноракурсным изображениям при неизвестных внешних параметрах съемки // Компьютерная оптика. 2015. Т. 39, № 5. С. 770-776.

10. Karlsson L., Tisseur F. Algorithms for hessenberg-triangular reduction of fiedler linearization of matrix polynomials // Society for Industrial and Applied Mathematics. 2015. C. 384-414.

11. Smith M. Applications of Dual Quaternions in Three Dimensional Transformation and Interpolation. 2013. N. 11. P. 6-14.

12. Schwartz S. E. Development of the kinematic model for an ultrasound scanning machine by means of dual quaternion transformations of screw coordinates // Massachusetts institute of technology. 1989. P. 8-13.


Review

For citations:


Goshin Ye.V., Useinova I.R. A Method for Determination of the Extrinsic Camera Parameters from a Pair of Images with the Use of Dual Quaternions. Mekhatronika, Avtomatizatsiya, Upravlenie. 2017;18(4):279-284. (In Russ.) https://doi.org/10.17587/mau.18.279-284

Views: 496


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1684-6427 (Print)
ISSN 2619-1253 (Online)