Bio-inspired Algorithms to Reconstruct Stereoscopic Disparity

  • Sheena Sharma
  • C. M. Markan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 250)


Binocular disparity refers to the difference in image location of an object seen by the left and right eyes, resulting from the eyes’ horizontal separation. Bio-inspired systems aim to extract some interesting features from living beings, such as adaptability and fault tolerance, for including them in human-designed devices. The biological vision systems routinely accomplish complex visual tasks such as object recognition, stereoscopic vision and many more, which continue to challenge artificial systems. If any cell in the brain is dead, other cell takes over the dead cell and brain works in the normal way. Any bio-inspired system must be any day superior to any artificial method. In this paper, this paper presents some algorithms which are motivated from biological functioning, such as Cepstral filtering technique, phase method, reaction-diffusion algorithm. Further, this pa per compares cepstral filtering technique with phase method. These two algorithms are claimed as two different approaches, but in this paper we show that in essence they are same. Both the algorithms exploit only part of the functions used in the bio logical flow of data to reconstruct the depth perception. The algorithms look different as both follow different procedures and functions. If the computational steps are decomposed and compared then they are doing the same thing. Each step in both algorithms is same and only the functions used are different as they are just the mathematical way of representation. By comparing both the algorithms, the advantage of one can be benefited by the other. The equivalence condition has also been derived.


Stereo vision Cepstral filtering technique Gabor filters 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yeshurun, Schwartz, E.: Cepstral Filtering on a columnar Image Architecture: A fast Algorithm for binocular Stereo Segmentation. IEEE transaction on Pattern and Machine Intelligence 11(7), 759–767 (1989)CrossRefGoogle Scholar
  2. 2.
    Ludwig, K.-O., Neumann, H., Neumann, B.: Local Stereoscopic Depth Image and Vision Computing, vol. 12, pp. 16–35 (1994)Google Scholar
  3. 3.
    Nomura, A., Ichikaw, M., Miike, H.: Reaction-diffusion algorithm for stereo disparity detection. Machine Vision and Application 20, 175–187 (2009)CrossRefzbMATHGoogle Scholar
  4. 4.
    Nomura, A., Ichikaw, M., Okada, K., Miike, H.: Stereo algorithm with reaction-diffusion equation. In: Bhatti, A. (ed.) Stereo Vision, pp. 259–272 (2008)Google Scholar
  5. 5.
    Nomura, A., Ichikaw, M., Okada, K., Miike, H.: Reaction-diffusion algorithm for vision systems. In: Obinata, G., Dutta, A. (eds.) Vision Systems: Segmentation & Pattern Recognition, ch. 4, pp. 61–80. i-Tech Education and Publishing, Vienna, Austria (2007)Google Scholar
  6. 6.
    Sanger, T.D.: Stereo disparity compuation using Gabor filters. Biol. Cybern. 59, 405–418 (1998)CrossRefGoogle Scholar
  7. 7.
    Qian, N.: Computing Stereo disparity and motion with known binocular cell properties, vol.6 (3), pp. 390–404 (1994)Google Scholar
  8. 8.
    Qian, N., Mikaelian, S.: Relationship between phase and energy methods for disparity computation. Neural Comp. 12, 279–292 (2002)CrossRefGoogle Scholar
  9. 9.
    Bardsley, D.: Stereo Vision for 3D Face Recognition, Year 1 Annual Review, PhD Report, University of Nottingham (2005)Google Scholar
  10. 10.
    Website, http://www.middleburyedu/stereoGoogle Scholar
  11. 11.
    Olison, T.J., Coombs, D.J.: Real Time Vergence Control for Binocular Robots, Technical Report 348, Dept of Computer Science University of Rochester (1990)Google Scholar
  12. 12.
    Ohzawa, I., DeAngelis, G.C., Freeman, R.D.: Stereoscopic depth discrimination in the visual cortex: Neurons ideally suited as disparity detectors. Science 249, 1037–1041 (1990)CrossRefGoogle Scholar
  13. 13.
    Ohzawa, I., DeAngelis, G.C., Freeman, R.D.: Encoding of binocular disparity by simple cells in the cat’s visual cortex. J. Neurophysiol. 75, 1779–1805 (1996)Google Scholar
  14. 14.
    Ohzawa, I., DeAngelis, G.C., Freeman, R.D.: Encoding of binocular disparity by complex cells in the cat’s visual cortex. J. Neurophysiol. 77, 2879–2909 (1997)Google Scholar
  15. 15.
    Gabor, D.: Theory of Communication. J. Inst. Electr. Eng. 93 (1946)Google Scholar
  16. 16.
    Marcelja, S.: Mathematical Description of the Responses of Simple Cortical Cells. J. Opt. Soc. Am. 70 (1980)Google Scholar
  17. 17.
    Fleet, D.J., Jepson, A.D., Jenkin, M.: Phase-based disparity measurement. Comp. Vis. Graphics Image Proc. 53, 198–210 (1989)zbMATHGoogle Scholar
  18. 18.
    Maimone, M.W.: Characterizing Stereo Matching Problems using stereo Spatial Frequency, PhD thesis (1996)Google Scholar
  19. 19.
    Stefano, L.D., Marchionni, M., Mattoccia, S., Neri, G.: A fast area-based stereo matching algorithm. In: 15th International Conference on Vision Interface, vol. 22, pp. 983–1005 (2004)Google Scholar
  20. 20.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two frame stereo correspondence algorithms. International Journal of Computer Vision 47, 7–42 (2002)CrossRefzbMATHGoogle Scholar
  21. 21.
    Lazaros, N., Sirakoulis, G.C., Gasteratos, A.: Review of Stereo Vision Algorithms: From Software to Hardware. International Journal of Optomechatronics 2, 435–462 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sheena Sharma
    • 1
  • C. M. Markan
    • 1
  1. 1.Dept. of Phy. & Comp. Sc.Dayalbagh Educational InstituteAgraIndia

Personalised recommendations