Advertisement

Improved Contour Detection by Non-classical Receptive Field Inhibition

  • Cosmin Grigorescu
  • Nicolai Petkov
  • Michel A. Westenberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2525)

Abstract

We propose a biologically motivated computational step, called nonclassical receptive field (non-CRF) inhibition, to improve the performance of contour detectors. We introduce a Gabor energy operator augmented with non-CRF inhibition, which we call the bar cell operator.We use natural images with associated ground truth edge maps to assess the performance of the proposed operator regarding the detection of object contours while suppressing texture edges. The bar cell operator consistently outperforms the Canny edge detector.

Keywords

Simple Cell Edge Pixel Canny Edge Detector Object Contour Texture Edge 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex. J. Physiology (London) 160 (1962) 106–154Google Scholar
  2. 2.
    Daugman, J.G.: Uncertainty relations for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Optical Society of America A 2 (1985) 1160–1169CrossRefGoogle Scholar
  3. 3.
    Knierim, J.J., van Essen, D.C.: Neuronal responses to static texture patterns in area V1 of the alert macaque monkey. J. Neurophysiology 67 (1992) 961–980Google Scholar
  4. 4.
    Schiller, P.H., Finlay, B.L., Volman, S.F.: Quantitative studies of single-cell properties in monkey striate cortex. III. spatial frequencies. J. Neurophysiology 39 (1976) 1334–1351Google Scholar
  5. 5.
    von der Heydt, R., Peterhans, E., Dürsteler, M.R.: Periodic-pattern-selective cells in monkey visual cortex. J. Neuroscience 12 (1992) 1416–1434Google Scholar
  6. 6.
    Petkov, N., Kruizinga, P.: Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells. Biological Cybernetics 76 (1997) 83–96CrossRefzbMATHGoogle Scholar
  7. 7.
    Blakemore, C., Carpenter, R.H.S., Georgeson, M.A.: Lateral inhibition between orientation detectors in the human visual system. Nature 228 (1970) 37–39CrossRefGoogle Scholar
  8. 8.
    Kanizsa, G.: Organization inVision, Essays on Gestalt Perception. Praeger, NewYork (1979)Google Scholar
  9. 9.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Analysis and Machine Intelligence 24 (2002) 509–522CrossRefGoogle Scholar
  10. 10.
    Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence 8 (1986) 679–698CrossRefGoogle Scholar
  11. 11.
    Tagare, H., deFigueiredo, R.: On the localization performance measure and optimal edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence 12 (1990) 1186–1190CrossRefGoogle Scholar
  12. 12.
    Simoncelli, E.P., Schwartz, O.: Modeling surround suppression in V1 neurons with a statistically-derived normalization model. In Kearns, M.S., Solla, S.A., Cohn, D.A., eds.: Advances in Neural Information Processing Systems 11, Cambridge, MA, MIT Press (1999) 153–159Google Scholar
  13. 13.
    Heitger, F.: Feature detection using suppression and enhancement. Technical Report TR-163, Communication Technology Laboratory, Swiss Federal Institute of Technology (1995)Google Scholar
  14. 14.
    Petkov, N., Kruizinga, P., Lourens, T.: Lateral inhibition in cortical filters. In: Proc. Int. Conf. on Digital Signal Processing, Nicosia, Cyprus (1993) 122–129Google Scholar
  15. 15.
    Petkov, N., Lourens, T.: Interacting cortical filters for object recognition. In: Proc. of the Asian Conference on Computer Vision, Osaka, Japan (1993) 583–586Google Scholar
  16. 16.
    Nothdurft, H.C., Gallant, J., van Essen, D.: Response modulation by texture surround in primate area V1: Correlates of “popout” under unesthesia. Visual Neuroscience 16 (1999) 15–34CrossRefGoogle Scholar
  17. 17.
    Grigorescu, C., Petkov, N., Westenberg, M.A.: Contour detection based on non-classical receptive field inhibition. submitted to IEEE Trans. Image Processing (2002)Google Scholar
  18. 18.
    Kapadia, M., Westheimer, G., Gilbert, C.: Spatial distribution of contextual interactions in primary cortex and in visual perception. Journal of Neurophysiology 84 (2000) 2048–2062Google Scholar
  19. 19.
    Walker, G., Ohzawa, I., Freeman, R.: Asymmetric suppression outside the classical receptive field of the visual cortex. Journal of Neuroscience 23 (1999) 10536–10553Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Cosmin Grigorescu
    • 1
  • Nicolai Petkov
    • 1
  • Michel A. Westenberg
    • 1
  1. 1.Institute of Mathematics and Computing ScienceUniversity of GroningenGroningenThe Netherlands

Personalised recommendations