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Contour detection improved by context-adaptive surround suppression

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  • Qiang Sang
  • Biao Cai
  • Hao Chen

Abstract

Recently, many image processing applications have taken advantage of a psychophysical and neurophysiological mechanism, called “surround suppression” to extract object contour from a natural scene. However, these traditional methods often adopt a single suppression model and a fixed input parameter called “inhibition level”, which needs to be manually specified. To overcome these drawbacks, we propose a novel model, called “context-adaptive surround suppression”, which can automatically control the effect of surround suppression according to image local contextual features measured by a surface estimator based on a local linear kernel. Moreover, a dynamic suppression method and its stopping mechanism are introduced to avoid manual intervention. The proposed algorithm is demonstrated and validated by a broad range of experimental results.

Suggested Citation

  • Qiang Sang & Biao Cai & Hao Chen, 2017. "Contour detection improved by context-adaptive surround suppression," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-13, July.
    • Handle: RePEc:plo:pone00:0181792
    DOI: 10.1371/journal.pone.0181792
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    References listed on IDEAS

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    1. Jonathan B. Levitt & Jennifer S. Lund, 1997. "Contrast dependence of contextual effects in primate visual cortex," Nature, Nature, vol. 387(6628), pages 73-76, May.
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