Table of Contents Author Guidelines Submit a Manuscript
Journal of Robotics
Volume 2011 (2011), Article ID 943137, 9 pages
Research Article

A Novel Bioinspired Vision System: A Step toward Real-Time Human-Robot Interactions

1Department of System Design Engineering, Graduate School of Engineering, University of Fukui, Fukui 910-8507, Japan
2Embodiment and Consciousness Unit, Brain Science Institute, BTCC RIKEN, Nagoya 463-0003, Japan
3Research and Education Program for Life Science, University of Fukui, Fukui 910-8507, Japan

Received 2 December 2010; Revised 2 May 2011; Accepted 30 May 2011

Academic Editor: Yuan Zheng

Copyright © 2011 Abdul Rahman Hafiz et al. 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.

Linked References

  1. D. Floreano and C. Mattiussi, Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies, The MIT Press, Cambridge, UK, 2008.
  2. V. Manian and R. Vasquez, “Multiresolution edge detection algorithm applied to SAR images,” in Proceedings of the IEEE Geoscience and Remote Sensing Symposium, vol. 2, pp. 1291–1293, July 1999. View at Scopus
  3. A. Rydberg and G. Borgefors, “Extracting multispectral edges in satellite images over agricultural fields,” in Proceedings of the International Conference on Image Analysis and Processing, pp. 786–791, 1999.
  4. A. H. Tewfik, F. A. Assaad, and M. Deriche, “Edge detection using spectral estimation techniques,” in Proceedings of the 6th Multidimensional Signal Processing Workshop, pp. 34–35, 1989.
  5. J. C. Stewien and T. L. J. Ferris, “The asterisk operator. An edge detection operator addressing the problem of clean edges in bone X-ray images,” in Proceedings of the 2nd International Conference on Knowledge-Based Intelligent Electronic Systems (KES '98), vol. 3, pp. 28–31, April 1998. View at Scopus
  6. K. Karantzalos and D. Argialas, “Improving edge detection and watershed segmentation with anisotropic diffusion and morphological levellings,” International Journal of Remote Sensing, vol. 27, no. 24, pp. 5427–5434, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. L. Meylan, D. Alleysson, and S. Süsstrunk, “Model of retinal local adaptation for the tone mapping of color filter array images,” Journal of the Optical Society of America A, vol. 24, no. 9, pp. 2807–2816, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. I. Sobel and G. Feldman, “A 3x3 isotropic gradient operator for image processing, presented at a talk at the Stanford Artificial Project,” in Pattern Classification and Scene Analysis, R. Duda and P. Hart, Eds., pp. 271–272, John Wiley & Sons, 1968. View at Google Scholar
  9. J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, 1986. View at Google Scholar · View at Scopus
  10. B. Resko, A. Roka, A. Csapo, and P. Baranyi, “Edge detection model based on involuntary tremors and drifts of the eye,” Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 11, pp. 648–654, 2007. View at Google Scholar
  11. Y. Becerikli and H. Engin, “Alternative neural network based edge detection,” Neural Information Processing, vol. 10, pp. 193–199, 2006. View at Google Scholar
  12. G. Metta, “An attentional system for a humanoid robot exploiting space variant vision,” in Proceedings of the IEEE-RAS International Conference on Humanoid Robots, pp. 359–366, Tokyo, Japan, 2001.
  13. M. Shamoto, K. Kato, and K. Yamamoto, “A implementation of Humanoid Vision-analysis of eye movement and implementation to Robot,” in Proceedings of the Annual Conference of the Society of Instrument and Control Engineers (SICE '07), pp. 744–747, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Kolesnik, A. Barlit, and E. Zubkov, “Iterative tuning of simple cells for contrast invariant edge enhancement,” in Biologically Motivated Computer Vision, vol. 2525 of Lecture Notes in Computer Science, pp. 91–106, 2002. View at Google Scholar
  15. R. J. Krauzlis, “The control of voluntary eye movements: new perspectives,” The Neuroscientist, vol. 11, no. 2, pp. 124–137, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. R. Nelson and H. Kolb, “Synaptic patterns and response properties of bipolar and ganglion cells in the cat retina,” Vision Research, vol. 23, no. 10, pp. 1183–1195, 1983. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Verweij, E. P. Hornstein, and J. L. Schnapf, “Surround antagonism in macaque cone photoreceptors,” Journal of Neuroscience, vol. 268, pp. 1053–1056, 2003. View at Google Scholar · View at Scopus
  18. D. M. Dacey, “Primate retina: cell types, circuits an colour opponency,” Progress in Retinal and Eye Research, vol. 19, no. 5, pp. 647–648, 2000. View at Publisher · View at Google Scholar · View at Scopus
  19. F. Werblin, “Synaptic connections, receptive fields, and patterns of activity in the tiger salamander retina,” Investigative Ophthalmology and Visual Science, vol. 32, no. 3, pp. 459–483, 1991. View at Google Scholar · View at Scopus
  20. John Moran Eye Center, University of Utah,
  21. B. Cassin and S. Solomon, Dictionary of Eye Terminology, Triad Publishing, Gainsville, Fla, USA, 1990.
  22. I. Israel, “Memory-guided saccades: what is memorized?” Experimental Brain Research, vol. 90, no. 1, pp. 221–224, 1992. View at Google Scholar · View at Scopus
  23. S. J. Judge, R. H. Wurtz, and B. J. Richmond, “Vision during saccadic eye movements. I. Visual interactions in striate cortex,” Journal of Neurophysiology, vol. 43, no. 4, pp. 1133–1155, 1980. View at Google Scholar · View at Scopus
  24. X. J. Wang, “A neural circuit basis for spatial working memory,” Neuroscientist, vol. 10, no. 6, pp. 553–565, 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. J. D. Coltz, M. T. V. Johnson, and T. J. Ebner, “Population code for tracking velocity based on cerebellar Purkinje cell simple spike firing in monkeys,” Neuroscience Letters, vol. 296, no. 1, pp. 1–4, 2000. View at Publisher · View at Google Scholar · View at Scopus
  26. ATR-Robotics,
  27. I. Boaventure and A. Gonzaga, “Method to evaluate the performance of edge detector,” in Proceedings of the The Brazilian Symposium on Computer Graphics and Image Processing, Sibgrapi, Brazil, 2009.
  28. Face Recognition OpenCV,
  29. Microsoft-speech,