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Calibration of a Non-single Viewpoint System

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Computer Vision

Synonyms

Non-central camera calibration

Related Concepts

Camera Calibration; Center of Projection

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-eye cameras, etc.) and camera models designed for these...

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

    Google Scholar 

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

    Article  Google Scholar 

  4. 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 

  5. Yu J, McMillan L (2004) General linear cameras. Proceedings of the 8th European conference on computer vision (ECCV), Prague, Czech Republic. pp 14–27

    Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  12. 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 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  20. 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 

  21. 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 

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

    Google Scholar 

  23. 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 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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Sturm, P. (2014). Calibration of a Non-single Viewpoint System. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_161

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