Skip to main content

Calibration of a Non-single Viewpoint System

  • Living reference work entry
  • First Online:
Computer Vision
  • 121 Accesses

Related Concepts

Definition

A non-single viewpoint system refers to a camera for which the light rays that enter the camera and contribute to the image produced by the camera do not pass through a single point. The analogous definition holds for models for non-single viewpoint systems. Hence, a non-single viewpoint camera or model does not possess a single center of projection. Nevertheless, a non-single viewpoint model (NSVM), like any other camera model such as the pinhole model, enables to project points and other geometric primitives into the image and to back-project image points or other image primitives, to 3D. Calibration of a non-single viewpoint model consists of a process that allows to compute the parameters of the model.

Background

There exist a large variety of camera technologies (“regular” cameras, catadioptric cameras, fish-eyes, etc.)...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

References

  1. Sturm P, Ramalingam S, Tardif JP, Gasparini S, Barreto J (2011) Camera models and fundamental concepts used in geometric computer vision. Found Trends Comput Graph Vis 6(1–2):1–183

    Google Scholar 

  2. Pless R (2003) Using many cameras as one. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Madison, vol 2, pp 587–593

    Google Scholar 

  3. Caspi Y, Irani M (2002) Aligning non-overlapping sequences. Int J Comput Vis 48(1):39–51

    Article  Google Scholar 

  4. Chen NY (1979) Visually estimating workpiece pose in a robot hand using the feature points method. Ph.D. thesis, University of Rhode Island, Kingston

    Google Scholar 

  5. Chen NY, Birk J, Kelley R (1980) Estimating workpiece pose using the feature points method. IEEE Trans Autom Control 25(6):1027–1041

    Article  Google Scholar 

  6. Martins H, Birk J, Kelley R (1981) Camera models based on data from two calibration planes. Comput Graph Image Process 17:173–180

    Article  Google Scholar 

  7. Yu J, McMillan L (2004) General linear cameras. In: Proceedings of the 8th European conference on computer vision, Prague, vol 2, pp 14–27

    Google Scholar 

  8. Pajdla T (2002) Stereo with oblique cameras. Int J Comput Vis 47(1–3):161–170

    Article  Google Scholar 

  9. Ponce J (2009) What is a camera? In: Proceedings of the IEEE conference on computer vision and pattern recognition, Miami

    Book  Google Scholar 

  10. Seitz S, Kim J (2002) The space of all stereo images. Int J Comput Vis 48(1):21–38

    Article  Google Scholar 

  11. Batog G, Goaoc X, Ponce J (2010) Admissible linear map models of linear cameras. In: Proceedings of the IEEE conference on computer vision and pattern recognition, San Francisco

    Book  Google Scholar 

  12. Gupta R, Hartley R (1997) Linear pushbroom cameras. IEEE Trans Pattern Anal Mach Intell 19(9): 963–975

    Article  Google Scholar 

  13. Pajdla T (2002) Geometry of two-slit camera. Technical Report CTU-CMP-2002-02, Center for Machine Perception, Czech Technical University, Prague

    Google Scholar 

  14. Zomet A, Feldman D, Peleg S, Weinshall D (2003) Mosaicing new views: the crossed-slit projection. IEEE Trans Pattern Anal Mach Intell 25(6):741–754

    Article  Google Scholar 

  15. Feldman D, Pajdla T, Weinshall D (2003) On the epipolar geometry of the crossed-slits projection. In: Proceedings of the 9th IEEE international conference on computer vision, Nice, pp 988–995

    Google Scholar 

  16. Gennery D (2006) Generalized camera calibration including fish-eye lenses. Int J Comput Vis 68(3): 239–266

    Article  Google Scholar 

  17. Grossberg M, Nayar S (2005) The raxel imaging model and ray-based calibration. Int J Comput Vis 61(2):119–137

    Article  Google Scholar 

  18. Gremban K, Thorpe C, Kanade T (1988) Geometric camera calibration using systems of linear equations. In: Proceedings of the IEEE international conference on robotics and automation, Philadelphia, pp 562–567

    Google Scholar 

  19. Champleboux G, Lavallée S, Sautot P, Cinquin P (1992) Accurate calibration of cameras and range imaging sensors: the NPBS method. In: Proceedings of the IEEE international conference on robotics and automation, Nice, pp 1552–1558

    Google Scholar 

  20. Sturm P, Ramalingam S (2004) A generic concept for camera calibration. In: Proceedings of the 8th European conference on computer vision, Prague, pp 1–13

    Google Scholar 

  21. Ramalingam S, Sturm P, Lodha S (2005) Towards complete generic camera calibration. In: Proceedings of the IEEE conference on computer vision and pattern recognition, San Diego, vol 1, pp 1093–1098

    Google Scholar 

  22. Dunne A, Mallon J, Whelan P (2010) Efficient generic calibration method for general cameras with single centre of projection. Comput Vis Image Underst 114(2):220–233

    Article  Google Scholar 

  23. Nishimura M, Nobuhara S, Matsuyama T, Shimizu S, Fujii K (2015) A linear generalized camera calibration from three intersecting reference planes. In: Proceedings of the IEEE international conference on computer vision, Santiago, pp 2354–2362

    Google Scholar 

  24. Tardif JP, Sturm P, Trudeau M, Roy S (2009) Calibration of cameras with radially symmetric distortion. IEEE Trans Pattern Anal Mach Intell 31(9): 1552–1566

    Article  Google Scholar 

  25. Ying X, Hu Z (2004) Distortion correction of fisheye lenses using a non-parametric imaging model. In: Proceedings of the Asian conference on computer vision, Jeju Island, pp 527–532

    Google Scholar 

  26. Rosebrock D (2016) “The Surface Model” – an uncertain continuous representation of the generic camera model and its calibration. Ph.D thesis, Technische Universität Braunschweig

    Google Scholar 

  27. Szabolcs P, Csanád S, Lehel C (2019) Distortion estimation through explicit modeling of the refractive surface. In: Proceedings of the 28th international conference on artificial neural networks, Munich, pp 17–28

    Google Scholar 

  28. Lébraly P, Royer E, Ait-Aider O, Dhome M (2010) Calibration of non-overlapping cameras – application to vision-based robotics. In: Proceedings of the British machine vision conference, Aberystwyth

    Book  Google Scholar 

  29. Chen CS, Chang WY (2004) On pose recovery for generalized visual sensors. IEEE Trans Pattern Anal Mach Intell 26(7):848–861

    Article  Google Scholar 

  30. Ramalingam S, Lodha S, Sturm P (2004) A generic structure-from-motion algorithm for cross-camera scenarios. In: Proceedings of the 5th workshop on omnidirectional vision, camera networks and non-classical cameras, Prague, pp 175–186

    Google Scholar 

  31. Nistér D, Stewénius H (2007) A minimal solution to the generalised 3-point pose problem. J Math Imaging Vision 27(1):67–79

    Article  MathSciNet  Google Scholar 

  32. Stewénius H, Åström K (2004) Structure and motion problems for multiple rigidly moving cameras. In: Proceedings of the 8th European conference on computer vision, Prague, vol 3, pp 252–263

    Google Scholar 

  33. Stewénius H, Nistér D, Oskarsson M, Åström K (2005) Solutions to minimal generalized relative pose problems. In: Proceedings of the 6th workshop on omnidirectional vision, camera networks and non-classical cameras, Beijing

    Google Scholar 

  34. Ventura J, Arth C, Lepetit V (2015) An efficient minimal solution for multi-camera motion. In: Proceedings of the IEEE international conference on computer vision, Santiago, pp 747–755

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peter Sturm .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Sturm, P. (2020). Calibration of a Non-single Viewpoint System. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_161-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03243-2_161-1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03243-2

  • Online ISBN: 978-3-030-03243-2

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics