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  • Research Article
  • Open Access

Embedded Active Vision System Based on an FPGA Architecture

EURASIP Journal on Embedded Systems20062007:035010

  • Received: 2 May 2006
  • Accepted: 14 September 2006
  • Published:


In computer vision and more particularly in vision processing, the impressive evolution of algorithms and the emergence of new techniques dramatically increase algorithm complexity. In this paper, a novel FPGA-based architecture dedicated to active vision (and more precisely early vision) is proposed. Active vision appears as an alternative approach to deal with artificial vision problems. The central idea is to take into account the perceptual aspects of visual tasks, inspired by biological vision systems. For this reason, we propose an original approach based on a system on programmable chip implemented in an FPGA connected to a CMOS imager and an inertial set. With such a structure based on reprogrammable devices, this system admits a high degree of versatility and allows the implementation of parallel image processing algorithms.


  • Vision System
  • Parallel Image
  • Visual Task
  • Vision Problem
  • Active Vision

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Authors’ Affiliations

Laboratoire des Sciences et Matériaux pour l'Elecronique, et d'Automatique (LASMEA), UMR 6602 du CNRS, Université Blaise-Pascal, 24 Avenue des Landais, Aubiere, Cedex 63177, France


  1. DeHon A: Density advantage of configurable computing. Computer 2000,33(4):41-49. 10.1109/2.839320View ArticleGoogle Scholar
  2. Benedetti A, Perona P: Real-time 2-D feature detection on a reconfigurable computer. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '98), June 1998, Santa Barbara, Calif, USA 586-593.Google Scholar
  3. Woodfill J, Von Herzen B: Real-time stereo vision on the PARTS reconfigurable computer. Proceedings of 5th Annual IEEE Symposium on FPGAs for Custom Computing Machines (FCCM '97), April 1997, Napa Valley, Calif, USA 201-210.Google Scholar
  4. Benitez D, Cabrera J: Reactive computer vision system with reconfigurable architecture. Proceedings of 1st International Conference on Computer Vision Systems (ICVS '99), January 1999, Las Palmas, Gran Canaria, Spain 348-360.Google Scholar
  5. Böhm W, Hammes J, Draper B, et al.: Mapping a single assignment programming language to reconfigurable systems. Journal of Supercomputing 2002,21(2):117-130. 10.1023/A:1013623303037MATHView ArticleGoogle Scholar
  6. Marr D: Vision. W. H. Freeman, San Francisco, Calif, USA; 1982.Google Scholar
  7. Yarbus AL: Eye Movements and Vision. Plenum Press, New York, NY, USA; 1967.View ArticleGoogle Scholar
  8. Aloimonos J, Weiss I, Bandyopadhyay A: Active vision. Proceedings of 1st International Conference on Computer Vision, June 1987, London, UK 35-54.Google Scholar
  9. Bajcsy R: Active perception. Proceedings of the IEEE 1988,76(8):996-1005. 10.1109/5.5968View ArticleGoogle Scholar
  10. Ballard DH: Animate vision. Artificial Intelligence 1991,48(1):57-86. 10.1016/0004-3702(91)90080-4View ArticleGoogle Scholar
  11. Andersen CS: A framework for control of a camera head, Ph.D. thesis. Laboratory of image analysis, Aalborg Universtity, Aalborg, Denmark; 1996.Google Scholar
  12. Vieville T: A Few Steps Towards 3D Active Vision, Springer Series in Information Sciences. Volume 33. Springer, New York, NY, USA; 1997.View ArticleGoogle Scholar
  13. Van der Spiegel J, Kreider G, Claeys C, et al.: A foveated retina-like sensor based on CCD technology. In Analog VLSI Implementation of Neural Systems. Edited by: Mead C, Ismail M. Kluwer Academic, Boston, Mass, USA; 1989:189-210.View ArticleGoogle Scholar
  14. Rojer AS, Schwartz EL: Design considerations for a space-variant visual sensor with complex-logarithmic geometry. Proceedings of the 10th International Conference on Pattern Recognition, June 1990, Atlantic City, NJ, USA 2: 278-285.View ArticleGoogle Scholar
  15. Tistarelli M, Sandini G: On the advantages of polar and log-polar mapping for direct estimation of time-to-impact from optical flow. IEEE Transactions on Pattern Analysis and Machine Intelligence 1993,15(4):401-410. 10.1109/34.206959View ArticleGoogle Scholar
  16. Bederson BB: A miniature space-variant active vision system: cortex-I, Ph.D. thesis. New York University, New York, NY, USA; 1992.Google Scholar
  17. Kuniyoshi Y, Kita N, Rougeaux S, Suehiro T: Active stereo vision system with foveated wide angle lenses. Proceedings of 2nd Asian Conference on Computer Vision (ACCV '95), December 1995, Singapore 191-200.Google Scholar
  18. Sharkey PM, Murray DW, Heuring JJ: On the kinematics of robot heads. IEEE Transactions on Robotics and Automation 1997,13(3):437-442. 10.1109/70.585904View ArticleGoogle Scholar
  19. Truong S, Kieffer J, Zelinsky A: A cable-driven pan-tilt mechanism for active vision. Proceedings of Australian Conference on Robotics and Automation (ACRA '99), March-April 1999, Brisbane, Australia 172-177.Google Scholar
  20. Koch C, Ullman S: Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology 1985,4(4):219-227.Google Scholar
  21. Itti L, Koch C, Niebur E: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998,20(11):1254-1259. 10.1109/34.730558View ArticleGoogle Scholar
  22. Chalimbaud P, Berry F: Contrast optimization in a multi-windowing image processing architecture. Proceedings of IAPR Conference on Machine Vision Applications (MVA '05), May 2005, Tsukuba Science City, Japan Google Scholar
  23. Chalimbaud P, Berry F, Marmoiton F, Alizon S: Design of a hybrid visuo-inertial smart sensor. Proceedings of IEEE International Conference on Robotics and Automation Workshop on Integration of Vision and Inertial Sensors (InerVis '05), April 2005, Barcelona, Spain Google Scholar
  24. Bresenham J: Algorithm for computer control of a digital plotter. IBM Systems Journal 1965,4(1):25-30. 10.1147/sj.41.0025View ArticleGoogle Scholar
  25. Bond C: A New Line Drawing Algorithm: Based on Sample Rate Conversion. 2002. Google Scholar
  26. Tomasi C, Kanade T: Detection and tracking of point features. In Tech. Rep. CMU-CS-91-132. Carnegie Mellon University, Pittsburgh, Pa, USA; 1991.Google Scholar
  27. Demigny D, Boudouani N, Bourguiba R, Kessal L: Vers une méthodologie pour la programmation des architectures à reconfiguration dynamique. Actes du Workshop Adéquation Algorithmes Architectures En Traitement Du Signal et de L'image, Janvier 2000 15-20.Google Scholar


© P. Chalimbaud and F. Berry. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.