Skip to main content

An analog VLSI computational engine for early vision tasks

  • Part VIII: Implementations
  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

Included in the following conference series:

Abstract

The computational capabilities of linear lattice network has been investigated through the analysis, synthesys and realization of CMOS circuits able to implement the 1-D convolution with Gabor-like operators. The area of the single processing cell is 83μm × 70 μm. The feasibility of the approach has been verified experimentally over a network of various sizes, up to 36 cells. Applications to stereopsys and motion analysis are described.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. E.H. Adelson and J.R. Bergen. Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Amer., 2:284–321, 1985.

    Google Scholar 

  2. W.T. Freeman and E.H. Adelson. The design and use of steerable filters. IEEE Trams. Pattern Anal. Mach. Intell., 13(9):891–906, 1991.

    Google Scholar 

  3. C. Koch, J. Marroquin, and A. Yuille. Analog “neuronal” networks in early vision. Proc. Natl. Acad. Sci., 83:4263–4267, 1986.

    Google Scholar 

  4. C.A. Mead. Analog VLSI and Neural Systems. Addison-Wesley, Reading, 1989.

    Google Scholar 

  5. T. Poggio, V. Torre, and C. Koch. Computational vision and regularization theory. Nature, 317:638–643, 1985.

    Google Scholar 

  6. L. Raffo. Analysis and synthesis of resistive networks for distributed visual elaborations. Electronics Letters, 32(8):743, April 1996.

    Google Scholar 

  7. T.D. Sanger. Stereo disparity computation using Gabor filters. Biol. Cybern., 59:405–418, 1988.

    Google Scholar 

  8. E.A. Vittoz. Analog VLSI signal processing: Why, where, and how? J. of VLSI Signal Processing, 8:27–44, 1994.

    Google Scholar 

  9. J.L. Wyatt, C. Keast, M. Seidel, D. Standley, B. Horn, Knight T., C. Sodini, H.-S. Lee, and T. Poggio. Analog VLSI systems for image acquisition and fast early vision processing. Int. J. of Computer Vision, 8:217–230, 1992.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bisio, G.M., Bo, G.M., Confalone, M., Raffo, L., Sabatini, S.P., Zizola, M.P. (1997). An analog VLSI computational engine for early vision tasks. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020310

Download citation

  • DOI: https://doi.org/10.1007/BFb0020310

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics