Advertisement

A Computational Model for Boundary Detection

  • Gopal Datt Joshi
  • Jayanthi Sivaswamy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)

Abstract

Boundary detection in natural images is a fundamental problem in many computer vision tasks. In this paper, we argue that early stages in primary visual cortex provide ample information to address the boundary detection problem. In other words, global visual primitives such as object and region boundaries can be extracted using local features captured by the receptive fields. The anatomy of visual cortex and psychological evidences are studied to identify some of the important underlying computational principles for the boundary detection task. A scheme for boundary detection based on these principles is developed and presented. Results of testing the scheme on a benchmark set of natural images, with associated human marked boundaries, show the performance to be quantitatively competitive with existing computer vision approaches.

Keywords

Visual Cortex Human Visual System Natural Image Lateral Geniculate Nucleus Primary Visual Cortex 
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.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. of International Conference on Computer Vision (2001)Google Scholar
  2. 2.
    Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using brightness and texture. IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (5), 530–549 (2004)CrossRefGoogle Scholar
  3. 3.
    Ma, W.Y., Manjunath, B.S.: Edgeflow: A technique for boundary detection and segmentation. IEEE Transactions on Image Processing 9 (8), 1375–1388 (2000)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. International Journal of Computer Vision 42 (1), 7–27 (2001)CrossRefGoogle Scholar
  5. 5.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)CrossRefGoogle Scholar
  6. 6.
    Yen, S., Finkel, L.: Extraction of perceptually salient contours by striate cortical networks. Vision Research 38(5), 719–741 (1998)CrossRefGoogle Scholar
  7. 7.
    Grigorescu, C., Petkov, N., Westenberg, M.: Contour detection based on nonclassical receptive field inhibition. IEEE Transactions on Image Processing 12(7), 729–739 (2003)CrossRefGoogle Scholar
  8. 8.
    Joshi, G.D., Sivaswamy, J.: A simple scheme for contour detection. In: Proc. of the Conference on Computer Vision Theory and Applications, pp. 236–242 (2006)Google Scholar
  9. 9.
    Marr, D., Hildreth, E.: Theory of edge detection. Proceedings of the Royal Society of London, Series B 207, 187–217 (1980)CrossRefGoogle Scholar
  10. 10.
    Hoffmann, K.P., Stone, J.: Conduction velocity of afferents to cat visual cortex: a correlation with cortical receptive field properties. Brain Research 34, 460–466 (1971)CrossRefGoogle Scholar
  11. 11.
    Martinez, L., Alonso, J.M.: Complex receptive fields in primary visual cortex. The Neuroscientist 9(5), 317–331 (2003)CrossRefGoogle Scholar
  12. 12.
    Bruce, V., Green, P.R., Georgeson, M.A.: Visual Perception: physiology, psychology and ecology, 4th edn. Psychology Press (2004)Google Scholar
  13. 13.
    Lennie, P., Trevarthen, C., Essen, D.V., Wassle, H.: Parallel processing of visual information. Visual Perception-The Neurophysiological Foundations 92 (1990)Google Scholar
  14. 14.
    Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Psychology 160, 106–154 (1962)Google Scholar
  15. 15.
    Alonso, J.M., Martinez, L.M.: Functional connectivity between simple cells and complex cells in cat striate cortex. Nature Neuroscience 1(5), 395–403 (1998)CrossRefGoogle Scholar
  16. 16.
    Baumann, R., van der Zwan, R., Peterhans, E.: Figure-ground segregation at contours: a neural mechanism in the visual cortex of the alert monkey. European Journal of Neuroscience 9(6), 1290–1303 (1997)CrossRefGoogle Scholar
  17. 17.
    Dobbins, A., Zucker, S.W., Cynader, M.S.: Endstopped neurons in the visual cortex as a substrate for calculating curvature. Nature 329(6138), 438–441 (1987)CrossRefGoogle Scholar
  18. 18.
    von der Heydt, R., Peterhans, E., Dürsteler, M.R.: Grating cells in monkey visual cortex: coding texture? In: Blum, B. (ed.) Channels in the Visual Nervous System: Neurophysiology, Psychophysics and Models, pp. 53–73 (1991)Google Scholar
  19. 19.
    Kruizinga, P., Petkov, N.: Nonlinear operator for oriented texture. IEEE Transactions on Image Processing 8(10), 1395–1407 (1999)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Alonso, J.M.: The microcircuitry of complex cells in cat striate cortex. Society for Neuroscience 22(198.1), 489 (1996)Google Scholar
  21. 21.
    Mel, B.W., Ruderman, D.L., Archie, K.A.: Translation-invariant orientation tuning in visual Complex Cells could derive from intradendritic computations. The Journal of Neuroscience 18(11), 4325–4334 (1998)Google Scholar
  22. 22.
    Liu, X., Wang, D.: A spectral histogram model for textons and texture discrimination. Vision Research 42(23), 2617–2634 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gopal Datt Joshi
    • 1
  • Jayanthi Sivaswamy
    • 1
  1. 1.Centre for Visual Information TechnologyIIIT HyderabadIndia

Personalised recommendations