Projective distortion correction algorithm at low altitude photographing
I.S. Kholopov

 

Ryazan State Radio Engineering University,
Joint Stock Company Ryazan State Instrument-making Enterprise

Full text of article: Russian language.

 PDF

Abstract:
An algorithm for projective geometric distortion correction of images at low altitude shooting with a virtual camera is considered. The algorithm is based on the orientation of the virtual camera so that its optical axis is collinear with the normal to the plane of shooting and evaluating a homography matrix linking the coordinates of corresponding pixels of the real and virtual cameras. The homography matrix evaluation is possible based on information from additional sensors – an angular orientation sensor and a rangefinder, or altimeter. Their signals allow one to estimate the camera angular orientation and the distance to the observed plane. The impact of the angular orientation error, estimated using a triaxial microelectromechanical accelerometer, on a criterion-based correction quality is investigated. The experimental results with pre-calibrated cameras Logitech C270 and uEye RE 5240 show that when the roll and pitch errors of the camera orientation are no more than 1° then the correction algorithm with the virtual camera provides a quality index value of at least 0.97.

Keywords:
projective distortion, homography matrix, affine transformations, Euler angles, three-axis accelerometer.

Citation:
Kholopov IS. Projective distortion correction algorithm at low altitude photographing. Computer Optics 2017; 41(2): 284-290. DOI: 10.18287/0134-2452-2017-41-2-284-290.

References:

  1. Makarenko AA, Turnetskiy LS. Correction of projective distortions of the maps at low-level opto-electronic aerophotography [In Russian]. Izvestiya vuzov. Priborostroenie 2008; 51(5): 64-70.
  2. Li M, Zhao C, Hou Y, Ren M. A new lane line segmentation and detection method based on inverse perspective mapping. International Journal of Digital Content Technology and its Applications 2011; 5(4): 230-236. DOI: 10.4156/jdcta.vol5.issue4.28.
  3. Fursov VA, Goshin YeV. Information technology for digital terrain model reconstruction from stereo images. Computer Optics 2014; 38(2): 335-342.
  4. Favorskaya MN, Novikov DS. Compensation of projective distortion for building of panoramic images [In Russian]. Technical Vision 2014; 1(5): 60-67.
  5. Efimov AI, Novikov AI. An algorithm for multistage projective transformation adjustment for image superimposition. Computer Optics 2016; 40(2): 258-265. DOI: 10.18287/2412-6179-2016-40-2-258-265.
  6. Williem W, Simon C, Cho S, Park IK. Fast and robust perspective rectification of document images on a smartphone. In: Proc CVPRW 2014: 197-198. DOI: 10.1109/CVPRW.2014.37.
  7. Hartley R, Zisserman A. Multiple view geometry in computer vision. Cambridge, UK: Cambridge University Press; 2003. DOI: 10.1017/CBO9780511811685.
  8. Xu G, Zhang Z. Epipolar geometry in stereo, motion and object recognition. A unified approach. Dordrecht: Kluwer Academic Publishers; 1996. DOI: 10.1007/978-94-015-8668-9.
  9. Goshin YeV, Kotov AP, Fursov VA. Two-stage formation of a spatial transformation for image matching. Computer Optics 2014; 38(4): 886-891.
  10. Jung J-I, Ho Y-S. Geometric and colorimetric error compensation for multi-view images. Journal of Visual Communication and Image Representation 2014; 25(4): 698-708. DOI: 10.1016/j.jvcir.2013.04.008.
  11. Wang X, Klette R, Rosenhahn B. Geometric and photometric correction of projected rectangular pictures. In: Proc Image Vision Computing 2005: 223-228.
  12. Geetha KA, Murali S. Automatic rectification of perspective distortion from a single image using plane homography. International Journal on Computational Sciences and Applications 2013; 3(5): 47-58. DOI: 10.5121/ijcsa.2013.3506.
  13. Alpatov BA, Babayan PV. Digital adjustment of multispectral images observation [In Russian]. Digital signal processing 2003; 1: 24-26.
  14. Szeliski R, Golland P. Stereo matching with transparency and matting. International Journal of Computer Vision 1999; 32(1): 45-61. DOI: 10.1023/A:1008192912624.
  15. Szeliski R. Computer vision: Algorithms and applications. London: Springer; 2011. ISBN: 978-1-84882-934-3.
  16. Kian ST, Awad M, Dehghani A, Zahedi S. Triaxial accelerometer static calibration. In: Proc. of the World Congress on Engineering 2011; III: 2164-2167.
  17. Tilt sensing using a three-axis accelerometer. Source: <https://www.nxp.com/files/sensors/doc/app_note/AN3461.pdf>.
  18. Chelnokov YuN. Quaternion and Biquaternion Models and Methods of Mechanics of Solids and Their Applications [In Russian]. Moscow: “FIZMATLIT” Publisher; 2006. ISBN 5-9221-0680-5.
  19. Rohac J, Sipos M, Simanek J. Calibration of low-cost triaxial inertial sensors. IEEE Instrumentation and Measurement Magazine 2015; 18(6): 32-38. DOI: 10.1109/MIM.2015.7335836.
  20. Kholopov IS. Development of strapdown inertial navigation system with MEMS sensors, barometric altimeter and ultrasonic range meter. IOP Conference Series: Materials Science and Engineering 2015, 93(1): 012060. DOI: 10.1088/1757-899X/93/1/012060.
  21. Zhang Z. A Flexible New Technique for Camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000; 22(11): 1330-1334. DOI: 10.1109/34.888718.
  22. Camera calibration toolbox for Matlab. Source: <http://www.vision.caltech.edu/bouguetj/calib_doc/>.

© 2009, IPSI RAS
Institution of Russian Academy of Sciences, Image Processing Systems Institute of RAS, Russia, 443001, Samara, Molodogvardeyskaya Street 151; E-mail: journal@computeroptics.ru; Phones: +7 (846) 332-56-22, Fax: +7 (846) 332-56-20