Journal of Optics

, Volume 46, Issue 2, pp 108–115 | Cite as

2-D and 3-D lens model for computer vision

  • Y. V. Chavan
  • D. K. Mishra
  • D. S. Bormane
  • A. D. Shaligram
  • N. S. Mujumdar
Research Article
  • 118 Downloads

Abstract

The lenses are modified in its design to meet the refractive index used for various applications like automated vehicle driving, micro-scopes, telescope etc. These application work well along with correct and effective modeling and implementations (Adelson and Wang in IEEE Trans Pattern Anal Mach Intell 14(2):99–106, 12; Chavan and Mishra in Int J Math Sci Eng Appl 1(1):199–218, 13). In this paper one such modeling and its simulation is presented as 2-D and 3-D model of lens which can be used in camera for computer vision system. The model gives the results considering physical parameters as constants, (camera coordinates and image coordinates), and are based on the angle of the object with axes and focal distance. This model has been implemented using ‘C’ and results are plotted.

Keywords

2-D lens modeling 3-D lens modeling Image processing Computer vision Machine vision Camera modeling 

References

  1. 1.
    Y.V. Chavan, D.K. Mishra, Analysis of Blurring effect of lenses for Image  Processing. Int. J. Math. Sci. Eng. Appl. 1(1),49–69 (2007)Google Scholar
  2. 2.
    Y.V. Chavan, D.K. Mishra, Camera Model for Retinal System. Int. J. Eng. Res. Ind. Appl. 1(1), 191–223 (2008)Google Scholar
  3. 3.
    Y.V. Chavan, D.K. Mishra, Silicon model of the visual system for color constancy. Int. J. Eng. Res. Ind. Appl. 1(1), 197–223 (2008)Google Scholar
  4. 4.
    Y.V. Chavan, D.K. Mishra, Review of lenses for visual system. Int. J. Eng. Res. Ind. Appl. 1(1), 83–106 (2008)Google Scholar
  5. 5.
    G. Sharma (ed.), A Handbook on Digital Color Imaging (CRC Press, Boca Raton, 2003)Google Scholar
  6. 6.
    F. Pardo, B. Dierickx, D. Scheffer, CMOS foveated image sensor: signal scaling and small geometry effects. IEEE Trans. Electron. Devices 44(10), 1731–1737 (1997)ADSCrossRefGoogle Scholar
  7. 7.
    Y.V. Chavan, D.K. Mishra, Improved complementary metal oxide semiconductor-digital pixel sensor. J. Res. Inst. Electron. Telecommun. Eng. 55(5), 222–229 (2009)Google Scholar
  8. 8.
    L. Lee, R. Romano, G. Stein, Monitoring activities from multiple video streams: establishing a common coordinate frame. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 758–767 (2000)CrossRefGoogle Scholar
  9. 9.
    M. Asif, A.S. Malik, T.-S. Choi, 3D Shape recovery from image de-focus using wavelet analysis. In: IEEE International Conference on Image Processing (Genova, Italy 2005), pp. 1025–1028 Google Scholar
  10. 10.
    C. Munteanu, A. Rosa, Grey-scale image enhancement as an automatic process driven by evolution. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 34, 1292–1298 (2004)CrossRefGoogle Scholar
  11. 11.
    R. Tsai, A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J. Robot. Autom. 3(4), 323–344 (1987)CrossRefGoogle Scholar
  12. 12.
    E.H. Adelson, J.Y.A. Wang, Single lens stereo with a plenoptic camera. IEEE Trans. Pattern Anal. Mach Intell. 14(2), 99–106 (1992)CrossRefGoogle Scholar
  13. 13.
    Y.V. Chavan, D.K. Mishra, A comparative study of theories and hardware implementation approaches of machine vision system. Int. J. Math. Sci. Eng. Appl. 1(1), 199–218 (2007)MATHGoogle Scholar
  14. 14.
    D. Kesrarat, Enhancement of digital image by C-programming language. AUJ 10, 145–150 (2007)Google Scholar

Copyright information

© The Optical Society of India 2017

Authors and Affiliations

  • Y. V. Chavan
    • 1
  • D. K. Mishra
    • 2
  • D. S. Bormane
    • 3
  • A. D. Shaligram
    • 4
  • N. S. Mujumdar
    • 3
  1. 1.Electronics DepartmentGovernment PolytechnicOsmanabadIndia
  2. 2.SGSITSIndoreIndia
  3. 3.Rajarshi Shahu College of Engineering TathawadePuneIndia
  4. 4.Electronic ScienceUniversity of PunePuneIndia

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