JACIII Vol.11 No.6 pp. 648-654
doi: 10.20965/jaciii.2007.p0648


Edge Detection Model Based on Involuntary Tremors and Drifts of the Eye

András Róka, Ádám Csapó, Barna Reskó,
and Péter Baranyi

Computer and Automation Research Institute, Hungarian Academy of Sciences, H-1111 Budapest, Kende u. 13-17, Hungary

January 15, 2007
March 20, 2007
July 20, 2007
mage contour detection, non-overlapping receptive field, artificial involuntary eye-movements

Recent results in retinal research have shown that ganglion cell receptive fields cover the mammalian retina in a mosaic arrangement, with insignificant amounts of overlap in the central fovea. This means that the biological relevance of traditional and widely adapted edge-detection algorithms with convolution-based overlapping operator architectures has been disproved. However, using traditional filters with non-overlapping operator architectures leads to considerable losses in contour information. This paper introduces a novel, tremor- and drift-based edge-detection algorithm that reconciles these differences between the physiology of the retina and the overlapping architectures used by today’s widely adapted algorithms. The algorithm takes into consideration data convergence, as well as the dynamic properties of the retina, by incorporating a model of involuntary eye tremors and drifts and the impulse responses of ganglion cells. Based on the evaluation of the model, two hypotheses are formulated on the highly debated role of involuntary eye tremors: 1) The role of involuntary eye movements has information theoretical implications 2) From an information processing point of view, the functional role of involuntary eye movements extends to more than just the maintenance of action potentials. Involuntary eye-movements may be responsible for the compensation of information losses caused by a non-overlapping receptive field architecture.

Cite this article as:
András Róka, Ádám Csapó, Barna Reskó, and
and Péter Baranyi, “Edge Detection Model Based on Involuntary Tremors and Drifts of the Eye,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.6, pp. 648-654, 2007.
Data files:
  1. [1] H. Barlow, “Summation and inhibition of the frog’s retina,” Journal of Physiology, 119, pp. 69-88, 1953.
  2. [2] S. Devries and D. Baylor, “Mosaic arrangement of ganglion cell receptive fields in rabbit retina,” J. Neurphysiol., 78(4), pp. 2048-2060, October, 1997.
  3. [3] C. Grigorescu, N. Petkov, and M. Westenberg, “Contour and boundary detection improved by surround suppression of texture edges,” Image and Vision Computing, 22(8), pp. 609-622, 2004.
  4. [4] M. H. Hennig and F. Worgotter, “The effect of fixational eye movements on hyperacuity,” Computational Neuroscience, Dept. of Psychology, University of Stirling.
  5. [5] S. Kuffler, “Discharge patterns and functional organization of mammalian retina,” Journal of Neurophysiology, 16, pp. 37-68, 1953.
  6. [6] S. Martinez-Conde, S. L. Macknik, and D. H. Hubel, “The Role of Fixational Eye Movements in Visual Perception,” Nature Reviews Neuroscience, 5(3), pp. 229-240, 2004.
  7. [7] M. Meister, “Multineural codes in retinal signaling,” Proc. Natl. Acad. Sci. USA, 93(2), pp. 609-614, January, 1996.
  8. [8] V. Navalpakkam and L. Itti, “An integrated model of top-down and bottom-up attention for optimal object detection,” In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2049-2056, 2006.
  9. [9] O. Packer and D. Dacey, “Receptive field structure of H1 horizontal cells in macaque monkey retina,” Journal of Vision, 2(4), pp. 279-292, 2002.
  10. [10] R. Sekuler and R. Blake, “Perception,” McGraw Hill, 1994.
  11. [11] J. Thiem, C. Wolff, and G. Hartmann, “Biology-Inspired Early Vision System for a Spike Processing Neurocomputer,” In Biologically Motivated Computer Vision, pp. 387-396, 2000.

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