JACIII Vol.10 No.4 pp. 527-533
doi: 10.20965/jaciii.2006.p0527


Cognitive Vision Inspired Contour and Vertex Detection

Barna Reskó*, Ádám B. Csapó**, and Péter Baranyi*

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

**Budapest University of Technology and Economics, H-1111 Budapest, Egry J. utca 18, Hungary

September 22, 2005
January 27, 2006
July 20, 2006
image contour detection, visual feature array, negative filtering

This paper presents a visual cortex inspired cognitive model for contour and vertex detection. The model is strongly based on the receptive field characteristics of cortical neurons of the visual cortex. As a step forward compared to the previous version of the model, a new dimension has been added, which replaces the binary signals and operations by operations on real values. The resulting system yields a better approximation of the biological system, as well as provides stronger and more distinct contour lines and vertices. The contour detection and vertex extraction is performed by a vast network of simple units of computation simultaneously processing the visual data. The computational units are organized in a special structure, the Visual Feature Array (VFA), which allows the structural representation of complex operations. The goal of the model is to extract abstract information from an image, which in turn may be used as input for the recognition process of even more abstract visual objects. In order to achieve constant time execution of the model, the aspects of hardware implementation are also treated in this paper.

Cite this article as:
Barna Reskó, Ádám B. Csapó, and Péter Baranyi, “Cognitive Vision Inspired Contour and Vertex Detection,” J. Adv. Comput. Intell. Intell. Inform., Vol.10, No.4, pp. 527-533, 2006.
Data files:
  1. [1] H. B. Barlow, “Summation and inhibition of the frog’s retina,” J. Physiology, 119, pp. 69-88, 1953.
  2. [2] I. Biederman, “Recognition-by-Components: A Theory of Human Image Understanding,” Psychological Review, 94, pp. 115-147, 1987.
  3. [3] H. J. A. Dartnall, J. K. Bowmaker, and J. D. Mollon, “Human visual pigments: Microspectrophotometric results from the eyes of seven persons,” Proc. of Royal Society of London, B., 220, pp. 115-130, 1983.
  4. [4] C. Grigorescu, N. Petkov, and M. A. Westenberg, “Contour and boundary detection improved by surround suppression of texture edges,” Image and Vision Computing, 22(8), pp. 609-622, 2004.
  5. [5] A. Grinvald, D. Malonek, A. Shmuel, D. Glaser, I. Vanzetta, E. Shtoyerman, D. Shoham, and A. Arieli, “Imaging of Neuronal Activity,” Cold Spring Harbor Laboratory, 1999.
  6. [6] A. Grinvald, D. Malonek, A. Shmuel, D. Glaser, I. Vanzetta, E. Shtoyerman, D. Shoham, and A. Arieli, “Imaging of Neuronal Activity,” chapter Intrinsic signal imaging in the neocortex, pp. 1-17, Cold Spring Harbor Laboratory, 1999.
  7. [7] D. Hubel, “Eye, Brain and Vision,” W. H. Freeman & Company, 1995.
  8. [8] D. H. Hubel, and T. N. Wiesel, “Receptive fields of single neurones in the cat’s striate cortex,” J. Physiology, 148, pp. 574-591, 1959.
  9. [9] D. H. Hubel, and T. N. Wiesel, “Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex,” J. Physiology, 160, pp. 106-154, 1962.
  10. [10] D. H. Hubel, and T. N. Wiesel, “Receptive field and functional architecture in two nonstriate visual areas (18-19) of the cat,” J. Neurophysiology, 28, pp. 229-289, 1965.
  11. [11] D. H. Hubel, and T. N. Wiesel, “Receptive fields and functional architecture of monkey striate cortex,” J. Physiology, 195, pp. 215-243, 1968.
  12. [12] S. W. Kuffler, “Discharge patterns and functional organization of mammalian retina,” J. Neurophysiology, 16, pp. 37-68, 1953.
  13. [13] A. Polans, W. Baehr, and K. Palczewski, “Turned on by Ca2+! The physiology and pathology of Ca2+ –binding proteins in the retina,” Trends in Neurosciences, 19(12), pp. 547-554, 1996.
  14. [14] B. Reskó, Z. Petres, A. Róka, and P. Baranyi, “Visual Cortex inspired Intelligent Contour Detection,” Journal of Advanced Computational Intelligence, 2005 (accepted).
  15. [15] E. M. S. Grossberg, “Neural dynamics of perceptual grouping: textures, boundaries, and emergent segmentation,” Perceptional Psychophysics, 38, pp. 141-171, 1985.
  16. [16] L. Tao, M. Shelley, D. McLaughlin, and R. Shapley, “An egalitarian network model for the emergence of simple and complex cells in visual cortex,” PNAS, 101(1), pp. 366-371, 2004.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 05, 2021