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An Imaging Architecture Based on Derivative Estimation Sensors

  • Maria Petrou
  • Flore Faille
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

An imaging architecture is proposed, where the first and second derivatives of the image are directly computed from the scene. Such an architecture bypasses the problems of estimating derivatives from sampled and digitised data. It, therefore, allows one to perform more accurate image processing and create more detailed image representations than conventional imaging. This paper examines the feasibility of such an architecture from the hardware point of view.

Keywords

Analog Circuit Temporal Derivative Electronic Noise Pattern Noise Analog Memory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Chong, C.P., Salama, C.A.T., Smith, K.C.: Image-motion detection using analog VLSI. IEEE Journal of Solid-State Circuits 27(1), 93–96 (1992)CrossRefGoogle Scholar
  2. 2.
    Delbrück, T., Mead, C.A.: Time-derivative adaptive silicon photoreceptor array. In: Proceedings SPIE, vol. 1541, pp. 92–99 (1991)Google Scholar
  3. 3.
    Deutschmann, R.A., Koch, C.: An analog VLSI velocity sensor using the gradient method. In: Proceedings of the 1998 International Symposium on Circuits and Systems (ISCAS 1998), pp. VI 649–VI 652 (1998)Google Scholar
  4. 4.
    Dubois, J., Ginhac, D., Paindavoine, M.: Design of a 10000 frames/s CMOS sensor with in situ image processing. In: Proceedings of the 2nd International Workshop on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoc 2006), pp. 177–182 (2006)Google Scholar
  5. 5.
    Etienne-Cummings, R., Van der Spiegel, J., Mueller, P.: A focal plane visual motion measurement sensor. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 44(1), 55–66 (1997)CrossRefGoogle Scholar
  6. 6.
    Funatsu, E., Nitta, Y., Kyuma, K.: A 128 x 128-pixel artificial retina LSI with two-dimensional filtering functions. Japanese Journal of Applied Physics 38(8B), L938–L940 (1999)CrossRefGoogle Scholar
  7. 7.
    Higgins, C.M., Deutschmann, R.A., Koch, C.: Pulse-based 2-D motion sensors. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 46(6), 677–687 (1999)CrossRefGoogle Scholar
  8. 8.
    Hoshino, K., Mura, F., Shimoyama, I.: Design and performance of a micro-sized biomorphic compound eye with a scanning retina. Journal of Microelectromechanical Systems 9(1), 32–37 (2000)CrossRefGoogle Scholar
  9. 9.
    Kimura, H., Shibata, T.: A motion-based analog VLSI saliency detector using quasi-two-dimensional hardware algorithm. In: Proceedings of the 2002 International Symposium on Circuits and Systems (ISCAS 2002), pp. III 333–III 336 (2002)Google Scholar
  10. 10.
    Kramer, J.: Compact integrated motion sensor with three-pixel interaction. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(4), 455–460 (1996)CrossRefGoogle Scholar
  11. 11.
    Kramer, J.: An integrated optical transient sensor. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 49(9), 612–628 (2002)CrossRefGoogle Scholar
  12. 12.
    Kramer, J., Sarpeshkar, R., Koch, C.: An analog VLSI velocity sensor. In: Proceedings of the 1995 International Symposium on Circuits and Systems (ISCAS 1995), pp. 413–416 (1995)Google Scholar
  13. 13.
    Kramer, J., Sarpeshkar, R., Koch, C.: Pulse-based analog VLSI velocity sensors. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 44(2), 86–101 (1997)CrossRefGoogle Scholar
  14. 14.
    Landolt, O., Mitros, A., Koch, C.: Visual sensor with resolution enhancement by mechanical vibrations. In: Proc. of the 2001 Conference on Advanced Research in VLSI (ARVLSI 2001), pp. 249–264 (2001)Google Scholar
  15. 15.
    Lichsteiner, P., Posch, C., Delbruck, T.: A 128x128 120dB 30mW asynchronous vision sensor that responds to relative intensity change. In: Proc. of the 2006 IEEE International Solid-State Circuits Conference (2006)Google Scholar
  16. 16.
    Ma, S.-Y., Chen, L.-G.: A single-chip CMOS APS camera with direct frame difference output. IEEE Journal of Solid-State Circuits 34(10), 1415–1418 (1999)CrossRefGoogle Scholar
  17. 17.
    Mead, C.: Analog VLSI and Neural Systems. Addison-Wesley Publishing Company, Reading (1989)zbMATHGoogle Scholar
  18. 18.
    Mehta, S., Etienne-Cummings, R.: Normal optical flow chip. In: Proceedings of the 2003 International Symposium on Circuits and Systems (ISCAS 2003), pp. IV 784–IV 787 (2003)Google Scholar
  19. 19.
    Moini, A., Bouzerdoum, A., Yakovleff, A., Abbott, D., Kim, O., Eshraghian, K., Bogner, R.E.: An analog implementation of early visual processing in insects. In: Proceedings of the 1993 International Symposium on VLSI Technology, Systems and Applications (VLSITSA 1993), pp. 283–287 (1993)Google Scholar
  20. 20.
    Paillet, F., Mercier, D., Bernard, T.M.: Second generation programmable artificial retina. In: Proceedings of the 12th annual IEEE International ASIC/SOC Conference, pp. 304–309 (1999)Google Scholar
  21. 21.
    Stanacevic, M., Cauwenberghs, G.: Micropower gradient flow acoustic localizer. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 52(10), 2148–2157 (2005)CrossRefGoogle Scholar
  22. 22.
    Stocker, A.A.: Compact integrated transconductance amplifier circuit for temporal differentiation. In: Proceedings of the 2003 International Symposium on Circuits and Systems (ISCAS 2003), pp. I 25–I 28 (2003)Google Scholar
  23. 23.
    Giannakidis, A., Kotoulas, L., Petrou, M.: Improved 2D Vector Field Reconstruction using Virtual Sensors and the Radon Transform. In: International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, Canada, August 20–24 (2008)Google Scholar
  24. 24.
    Joshi, N.B.: Non-parametric probability density function estimation for medical images, PhD thesis, University of Oxford (2007)Google Scholar
  25. 25.
    Petrou, M.: A new imaging architecture and an alternative interpretation of the structure of the human retina. In: Zaman, H.B., Sembok, T.M.T., van Rijsbergen, K., Zadeh, L., Bruza, P., Shih, T., Taib, M.N. (eds.) Proceedings of the International Symposium on Information Technology, Kuala Lumpur Convention Centre, Malaysia, August 26–29, vol. 1, pp. 9–17, IEEE Cat. No CFP0833E-PRT, ISBN 978-1-4244-2327-9Google Scholar
  26. 26.
    Varnavas, A.: Signal processing methods for EEG data classification, PhD thesis, Imperial College London (2008)Google Scholar
  27. 27.
    Verges-Llahi, J.: Colour Constancy and Image Segmentation Techniques for Applications to Mobile Robotics, PhD thesis, University Politecnica de Catalunya, Barcelona, Spain (2005)Google Scholar
  28. 28.
    Unser, M.: A Guided Tour of Splines for Medical Imaging. In: Plenary talk, Twelfth Annual Meeting on Medical Image Understanding and Analysis (MIUA 2008), Dundee UK, Scotland, July 2-3 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Maria Petrou
    • 1
  • Flore Faille
    • 1
  1. 1.Imperial College LondonUK

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