Skip to main content

Advertisement

Springer Nature Link
Log in

A discriminant method of single-optical-axis omnidirectional vision system

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Generally, due to the instrumental error of omnidirectional camera, it is very difficult to satisfy the standard single-optical-axis request. Thus, it is necessary to evaluate whether a single-optical-axis camera lens is aligned or has tangent distortion. In this paper, we propose a discriminant function of single-optical-axis omnidirectional vision system based on checkerboard, which is only related to the image points and does not involve any camera parameters. Firstly, under single-optical-axis omnidirectional camera, the geometric invariance among image points of collinear and equidistant space points is derived. Next, based on the derived geometric invariance in a single image, we construct the object function to discriminate the standard single-optical-axis omnidirectional camera. Finally, a discriminant method of single-optical-axis omnidirectional vision system is proposed based on the checkerboard. Experimental results on both simulated and real data have demonstrated the usefulness and effectiveness of our method.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Baker S, Nayer S (1999) A theory of single-viewpoint catadioptric image formation. Int J Comput Vis 35:175–196

    Article  Google Scholar 

  2. Barreto JP, Araujo H (2005) Geometry properties of central catadioptric line images and application in calibration. IEEE Trans Pattern Anal Mach Intell 27:1327–1333

    Article  Google Scholar 

  3. Deng XM, Wu FC, Wu YH (2007) An easy calibration method for central catadioptric cameras. Acta Automat Sin 33:801–808

    Article  Google Scholar 

  4. Dress AWM, Wenzel W (1991) Grassmann-Plücker relations and matroids with coefficients. Adv Math 86:68–110

    Article  MathSciNet  Google Scholar 

  5. Duan FQ, Wang L (2010) Calibrating central catadioptric cameras based on spatial line projection constraint. In: International conference on systems, man and cybernetics, pp 2088–2093

  6. Duan HX, Wu YH (2011) Paracatadioptric camera calibration using sphere images. In: International conference on image processing, pp 649–652

  7. Duan HX, Wu YH (2011) Unified imaging of geometric entities under catadioptric camera and camera calibration. J Comput-Aided Des Comput Graph 23:891–898

    Google Scholar 

  8. Duan HX, Wu YH (2012) A calibration method for paracatadioptric camera from sphere images. Pattern Recogn Lett 33:677–684

    Article  Google Scholar 

  9. Duan HX, Mei L, Shang YF, Hu CP (2014) Calibrating focal length for paracatadioptric camera from one circle image. In: International conference on computer vision theory and application, pp 56–63

  10. Duan HX, Wu YH, Wang J, Song L, Liu N (2017) Fitting a cluster of line images under centeral catadioptric camera. Clust Comput 1–8

  11. Geyer C, Daniilidis K (1999) Catadioptric camera calibration. In: International conference on computer vision, pp 398–404

  12. Geyer C, Daniilidis K (2001) Catadioptric projective geometry. Int J Comput Vis 45:223–243

    Article  Google Scholar 

  13. Geyer C, Daniilidis K (2002) Paracatadioptric camera calibration. IEEE Trans Pattern Anal Mach Intell 24:687–695

    Article  Google Scholar 

  14. Habib A, Pullivelli A, Mitishita E, Ghanma M, Kim E (2006) Stability analysis of low-cost digital cameras for aerial mapping usin different georeferencing techniques. Photogramm Rec 21(113):29–43

    Article  Google Scholar 

  15. Hartley RI, Kang SB (2005) Parameter-free radial distortion correction with centre of distortion estimation. In: International conference on computer vision, pp 1834–1841

  16. Harvey JE, Bogunovic D, Krywonos A (2003) Aberrations of diffracted wave fields: distortion. Appl Opt 42(7):1167–1174

    Article  Google Scholar 

  17. Maeda PY, Catrysse PB, Wandell BA (2005) Integrating lens design with digital camera simulation. In: SPIE electronic imaging

  18. Mashita T, Iwai Y, Yachida M (2005) Calibration method for misaligned catadioptric. In: Workshop on OmnidirectionalVision, camera networks, and non-classical cameras

  19. Semple JG, Kneebone GT (1998) Algebraic projective geometry. Claredon Press, Oxford

    MATH  Google Scholar 

  20. Svoboda T, Pajdla T, Hlavac V (1997) Central panoramic cameras: geometry and design, Research report K335/97/147, Czech Technical University, Faculty of Electrical Engineering, Center for Machine Perception

  21. Vandeportaele B, Cattoen M, Marthon P, Gurdjo P (2006) A new linear calibration method for paracatadioptric cameras. In: International conference on pattern recognition, pp 647–651

  22. White N (1994) A tutorial on Gassmann-Cayley algebra. In: Invariant methods in discrete and computational geometry, pp 93–106

  23. Wu FC, Duan FQ, Hu ZY, Wu YH (2008) A new linear algorithm for calibrating central catadioptric cameras. Pattern Recogn 41:3166–3172

    Article  Google Scholar 

  24. Wu YH, Hu ZY, Li YF (2014) Radial distortion invariance and lens evaluation under a single-optical-axis omnidirectional camera. Comput Vis Image Underst 126:11–27

    Article  Google Scholar 

  25. Ying XH, Hu ZY (2004) Catadioptric camera calibration using geometric invariants. IEEE Trans Pattern Anal Mach Intell 26:1260–1271

    Article  Google Scholar 

  26. Ying XH, Zha HB (2005) Simultaneously calibrating catadioptric camera and detecting line features using hough transform. In: International conference on intelligent robots and systems, pp 412–417

Download references

Acknowledgments

This work is sponsored by the Shanghai Rising-Star Program (17QB1401000); and by Shanghai science and technology innovation action plan(17511108200); and by the National Natural Science Foundation of China (61403084, 61402116); and by the Application Innovation Plan of Ministry of Public Security (2017YYCXSXST030).

Author information

Authors and Affiliations

  1. Science College, East China University of Science and Technology, Shanghai, China

    Peng Tian

  2. Cyber Physical System R & D Center, The Third Research Institute of Ministry of Public Security, Shanghai, China

    Huixian Duan

  3. Shanghai International Technology & Trade United Co., Ltd., Shanghai, China

    Huixian Duan

  4. Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China

    Huixian Duan

Authors
  1. Peng Tian
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Huixian Duan
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Huixian Duan.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tian, P., Duan, H. A discriminant method of single-optical-axis omnidirectional vision system. Multimed Tools Appl 78, 1117–1130 (2019). https://doi.org/10.1007/s11042-018-6397-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6397-3

Keywords