A Coutour Detection Model Based on Surround Inhibition with Multiple Cues

  • Kaifu Yang
  • Yongjie Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)


Sufficient physiological studies have revealed that surround inhibition substantially occurs when difference exists between the classical receptive field (CRF) and its surrounding (i.e. non-CRF) of most neurons in primary visual cortex (V1) for any local visual features. In this paper, we propose an improved contour detection model based on the biologically-plausible computational steps with non-CRF inhibition (also called surround inhibition) in V1. Through principal component analysis (PCA) we combine multiple local cues, including orientation, luminance and contrast, to improve contour detection in natural images. The results on a commonly used large image dataset demonstrate that surround inhibition combining multiple local cues can remarkably improve contour detection in complex scenes.


contour detection surround inhibition receptive filed multiple local cues V1 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology 160(1), 106–154 (1962)Google Scholar
  2. 2.
    Allman, J., Miezin, F., McGuinness, E.: Stimulus specific responses from beyond the classical receptive field: neurophysiological mechanisms for local-global comparisons in visual neurons. Annual Review of Neuroscience 8(1), 407–430 (1985)CrossRefGoogle Scholar
  3. 3.
    Li, C.-Y., Li, W.: Extensive integration field beyond the classical receptive field of cat’s striate cortical neurons–classification and tuning properties. Vision Research 34(18), 2337–2355 (1994)CrossRefGoogle Scholar
  4. 4.
    Walker, G.A., Ohzawa, I., Freeman, R.D.: Suppression outside the classical cortical receptive field. Visual Neuroscience 17(03), 369–379 (2000)CrossRefGoogle Scholar
  5. 5.
    Jones, H., Grieve, K., Wang, W., Sillito, A.: Surround suppression in primate V1. Journal of Neurophysiology 86(4), 2011–2028 (2001)Google Scholar
  6. 6.
    Shen, Z.M., Xu, W.F., Li, C.Y.: Cue invariant detection of centre–surround discontinuity by V1 neurons in awake macaque monkey. The Journal of Physiology 583(2), 581–592 (2007)CrossRefGoogle Scholar
  7. 7.
    Grigorescu, C., Petkov, N., Westenberg, M.A.: Contour detection based on nonclassical receptive field inhibition. IEEE Transactions on Image Processing 12(7), 729–739 (2003)CrossRefGoogle Scholar
  8. 8.
    Canny, J.: A computational approach to edge detection. Readings in Computer Vision: Issues, Problems, Principles, and Paradigms 184, 87–116 (1986)Google Scholar
  9. 9.
    Rivest, J., Cabanagh, P.: Localizing contours defined by more than one attribute. Vision Research 36(1), 53–66 (1996)CrossRefGoogle Scholar
  10. 10.
    Landy, M.S., Kojima, H.: Ideal cue combination for localizing texture-defined edges. JOSA A 18(9), 2307–2320 (2001)CrossRefGoogle Scholar
  11. 11.
    Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5), 530–549 (2004)CrossRefGoogle Scholar
  12. 12.
    Tang, Q., Sang, N., Zhang, T.: Extraction of salient contours from cluttered scenes. Pattern Recognition 40(11), 3100–3109 (2007)zbMATHCrossRefGoogle Scholar
  13. 13.
    Zeng, C., Li, Y.J., Li, C.Y.: Center-surround interaction with adaptive Inhibition: a computational model for contour detection. NeuroImage 55(1), 49–66 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kaifu Yang
    • 1
  • Yongjie Li
    • 1
  1. 1.Key Laboratory for Neuroinformation of Ministry of EducationUniversity of Electronic Science and Technology of ChinaChengduChina

Personalised recommendations