Blind radial distortion compensation in a single image using fast Hough transform
I.A. Kunina, S.A. Gladilin, D.P. Nikolaev

 

 Institute for Information Transmission Problems RAS, Moscow, Russia

Full text of article: Russian language.

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Abstract:
In this paper, we present an automatic technique for compensation of radial distortion, which is characteristic of wide-angle lenses. The proposed method estimates distortion parameters using a single image from unknown source. No calibration objects are required, but it is assumed that the original scene contains straight lines. The method is based on finding such radial distortion parameters, that maximize total length of linear segments. We employ a fast Hough transform to estimate the overall curvature of lines without selecting any. The proposed algorithm is tested on real images obtained using calibrated camera lenses with different radial distortion.
For the formal evaluation of the algorithm, we propose a quality measure for geometric distortion compensation, which works correctly even in the case when the problem of determining the coefficients is ill-conditioned.

Keywords:
digital image processing, image analysis, lens system design, radial distortion, automatic calibration, fast Hough transform.

Citation:
Kunina IA, Gladilin SA, Nikolaev DP. Blind radial distortion compensation in a single image using fast Hough transform. Computer Optics 2016; 40(3): 395-403. DOI: 10.18287/2412-6179-2016-40-3-395-403.

References:

  1. Zhang Z. A flexible new technique for camera calibration. Pattern Analysis and Machine Intelligence, IEEE Transactions on 2000; 22(11): 1330-1334. DOI: 10.1109/34.888718.
  2. Mironova TV. Analysis of deformations, optical inhomogeneities and distortion in digital photography using artificial speckles [In Russian]: dis. … cand. sci. (phys.-math.). Moscow: Lebedev Physical Institute; 2005.
  3. Hartley RI. Self-calibration of stationary cameras. International Journal of Computer Vision 1997; 22(1): 5-23. DOI: 10.1023/A:1007957826135.
  4. Stein GP. Accurate internal camera calibration using rotation, with analysis of sources of error. Computer Vision, Proceedings, Fifth International Conference on; 1995. DOI: 10.1109/ICCV.1995.466781.
  5. Farid H, Popescu AC. Blind removal of lens distortion. JOSA A 2001; 18(9): 2072-2078. DOI: 10.1364/JOSAA.18.002072.
  6. Zhang Z, Matsushita Y, Ma Y. Camera calibration with lens distortion from low-rank textures. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on; 2011. DOI: 10.1109/CVPR.2011.5995548.
  7. Karpenko S, Konovalenko I, Miller A, Miller B, Nikolaev D. UAV Control on the Basis of 3D Landmark Bearing-Only Observations. Sensors 2015; 15(12): 29802-29820. DOI: 10.3390/s151229768.
  8. Karpenko S, Konovalenko I, Miller A, Miller B, Nikolaev D. Visual navigation of the UAVs on the basis of 3D natural landmarks. Eighth International Conference on Machine Vision, International Society for Optics and Photonics 2015; 9875: 9875I. DOI: 10.1117/12.2228793.
  9. Konovalenko I, Miller A, Miller B, Nikolaev D. UAV navigation on the basis of the feature points detection on underlying surface. Proceedings of the 29th European Conference on Modeling and Simulation (ECMS 2015), Albena (Varna), Bulgaria, 2015: 26-29. DOI: 10.7148/2015-0499.
  10. Rosten E, Loveland R. Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy. Machine Vision and Applications 2011; 22(1): 77-85. DOI: 10.1007/s00138-009-0196-9.
  11. Alemán-Flores, M, Alvarez L, Gomez L, Santana-Cedres D. Automatic Lens Distortion Correction Using One-Parameter Division Models. Image Processing On Line 2014; 4: 327-343. DOI: 10.1007/s10851-012-0342-2.
  12. Karpenko, SM, Gladilin DP, Nikolaev DP. Method for recovery of radial distorted images [In Russian]. Proceedings of the conference Information Technologies and Systems ITaS, 2008: 502-505.
  13. Kanuki Y, Ohta N, Nagai A. Automatic compensation of radial distortion by minimizing entropy of histogram of oriented gradients. Pattern Recognition (ACPR), 2013 2nd IAPR Asian Conference on, 2013: 912-916. DOI: 10.1109/ACPR.2013.167.
  14. Wang A, Qiu T, Shao L. A simple method of radial distortion correction with centre of distortion estimation. Journal of Mathematical Imaging and Vision 2009; 35(3): 165-172. DOI: 10.1007/s10851-009-0162-1.
  15. Nikolaev D, Karpenko S, Nikolaev I, Nikolayev P. Hough transform: underestimated tool in the computer vision field. Proceedings of the 22th European Conference on Modelling and Simulation 2008; 238-246.
  16. Brady ML. A fast discrete approximation algorithm for the Radon transform. SIAM Journal on Computing 1998; 27(1): 107-119. DOI: 10.1137/S0097539793256673.
  17. Brown DC. Close-range camera calibration. Photogrammetric engineering 1971; 37(8): 855-866.
  18. Duda RO, Hart PE. Use of the Hough transformation to detect lines and curves in pictures. Communications of the ACM 1972; 15(1): 11-15. DOI: 10.1145/361237.361242.
  19. Hough PVC. Method and means for recognizing complex patterns. US Patent 3069654 of Dec 18, 1962.
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