• 1990 (Vol.4)
  • 1989 (Vol.3)
  • 1988 (Vol.2)
  • 1987 (Vol.1)

Volume 32 #1

Contents

  1. Variability of different functional songs types from Aelia acuminata L. (Heteroptera, Pentatomidae)
  2. Receptive field sizes of sustained ganglion cells in the retina of Carassius gibelio
  3. Fusion of radar, visible and thermal imagery with account for differences in brightness and chromaticity perception
  4. Modelling of the unmanned aerial vehicle navigation on the basis of two height-shifted onboard cameras
  5. Visual localisation of aerial images on vector map using colour-texture segmentation
  6. Comparison of the digitized pages of business documents by means of recognition
  7. The algorithm of epipole position estimation under pure camera translation
  8. Segmentation of vehicle registration plates based on dynamic time warping
  9. Finding the correspondence between closed curves under projective distortions
  10. Blur kernel estimation with algebraic tomography technique and intensity profiles of object boundaries
  11. Weighted search for a projective optical flow resistant to specular reflections
  12. Algebraic methods for tomography problem
  13. Quantitative analysis of the point light source virtual image doubling in underwater stereo vision

The algorithm of epipole position estimation under pure camera translation

© 2018 A.A. Ovchinkin, E.I. Ershov

Institute for Infromation Transmission Problems RAS 127051 Moscow, Bolshoy Karetny per., 19

Received 09 Aug 2017

In this paper we introduced a method of solving the problem of epipole position estimation for the case of camera pure translation with use of Fast Hough Transform (FHT). Moreover, we suggested a way of refining the result obtained with this method. In this case, FHT is used for effective outlier filtering. The paper contains accuracy and run-time comparison of the suggested method and classical technique of solving the problem using RANSAC and least squares methods. The results show the superiority of the proposed method.

Key words: epipolar geometry, vanishing point, epipole, Hough transform, camera calibration

DOI: 10.7868/S0235009218010079

Cite: Ovchinkin A. A., Ershov E. I. Algoritm opredeleniya polozheniya puchka epipolyarnykh linii dlya sluchaya pryamolineinogo dvizheniya kamery [The algorithm of epipole position estimation under pure camera translation]. Sensornye sistemy [Sensory systems]. 2018. V. 32(1). P. 42-49 (in Russian). doi: 10.7868/S0235009218010079

References:

  • Asvatov E.N., Ershov E.I., Nikolaev D.P. Robastnaya ortogonal'naya lineinaya regressiya dlya malomernykh gistogramm [Robust orthogonal linear regression on histogram in small-dimensional spaces]. Sensornye sistemy [Sensors systems]. 2017. V. 31(4). S. 331–342.
  • Bezmaternykh P.V., Khanipov T.M., Nikolaev D.P. Reshenie zadachi lineinoi regressii s pomoshch'yu bystrogo preobrazovaniya Khafa [Linear regression problem solution using fast Hough transform]. Informatsionnye tekhnologii i sistemy [Information technology and systems]. 2012. P. 354–359.
  • Brady M.L. A fast discrete approximation algorithm for the Radon transform. SIAM Journal on Computing. 1998. Т. 27. № 1. С. 107–119. DOI: 10.1137/S0097539793256673.
  • Caprile B., Torre V. Using vanishing points for camera calibration. International journal of computer vision. 1990. Т. 4. № 2. С. 127–139. DOI: 10.1007/BF00127813.
  • Chen Z., Pears N., McDermid J., Heseltine T. Epipole estimation under pure camera translation. DICTA. 2003. Т. 3. С. 849–858.
  • Geiger A., Lenz P., Urtasun R. Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. Conference on Computer Vision and Pattern Recognition (CVPR). 2012.
  • Hartley R., Zisserman A. Multiple view geometry in computer vision. Cambridge. Cambridge university press, 2003. DOI: 10.1017/CBO9780511811685.
  • Itseez G. Open Source Computer Vision Library. URL: https://github.com/itseez/opencv (accessed: 9.08.2017)
  • Li H., Hartley R. Five-point motion estimation made easy. 18th International Conference on Pattern Recognition. 2006. Т. 1. С. 630–633. DOI: 10.1109/ICPR.2006.579.
  • Levenberg K. A Method for the Solution of Certain NonLinear Problems in Least Squares. Quarterly of Applied Mathematics. 1944. Т. 2. С. 164–168. DOI: 10.1090/qam/10666.
  • Longuet-Higgins C. A computer algorithm for reconstructing a scene from two projections. Readings in Computer Vision: Issues, Problems, Principles, and Paradigms. 1987. С. 61–62. DOI: 10.1016/B978-0-08-051581-6.50012-X.
  • Nikolaev D.P., Karpenko S.M., Nikolaev I.P., Nikolayev P.P. Hough transform: underestimated tool in the computer vision field. Proceedings of the 22th European Conference on Modelling and Simulation. 2008. С. 238–246. DOI: 10.7148/2008-0238.
  • Nister D. An efficient solution to the five-point relative pose problem. IEEE transactions on pattern analysis and machine intelligence. 2004. Т. 26. № 6. P. 756–770. DOI: 10.1109/TPAMI.2004.17.
  • Ovchinkin A.A., Ershov E.I. Statistical analysis of the characteristics of high degree polynomial solving methods used in the five-point algorithm. 2016 International Conference on Robotics and Machine Vision. International Society for Optics and Photonics. 2017. С. 102530L‑102530L. DOI: 10.1117/12.2266366.
  • Zhang Z., Luong Q., Faugeras O. Motion of an uncalibrated stereo rig: Self-calibration and metric reconstruction. IEEE transactions on Robotics and Automation. 1996. С. 12. № 1. С. 103–113. DOI: 10.1109/70.481754.