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A Biologically Motivated Multiresolution Approach to Contour Detection

  • Giuseppe Papari
  • Patrizio Campisi
  • Nicolai Petkov
  • Alessandro Neri
Open Access
Research Article
Part of the following topical collections:
  1. Image Perception

Abstract

Standard edge detectors react to all local luminance changes, irrespective of whether they are due to the contours of the objects represented in a scene or due to natural textures like grass, foliage, water, and so forth. Moreover, edges due to texture are often stronger than edges due to object contours. This implies that further processing is needed to discriminate object contours from texture edges. In this paper, we propose a biologically motivated multiresolution contour detection method using Bayesian denoising and a surround inhibition technique. Specifically, the proposed approach deploys computation of the gradient at different resolutions, followed by Bayesian denoising of the edge image. Then, a biologically motivated surround inhibition step is applied in order to suppress edges that are due to texture. We propose an improvement of the surround suppression used in previous works. Finally, a contour-oriented binarization algorithm is used, relying on the observation that object contours lead to long connected components rather than to short rods obtained from textures. Experimental results show that our contour detection method outperforms standard edge detectors as well as other methods that deploy inhibition.

Keywords

Information Technology Detection Method Quantum Information Edge Detector Edge Image 
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.

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Copyright information

© Giuseppe Papari et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  • Giuseppe Papari
    • 1
  • Patrizio Campisi
    • 2
  • Nicolai Petkov
    • 1
  • Alessandro Neri
    • 2
  1. 1.Institute of Mathematics and Computing ScienceUniversity of GroningenGroningenThe Netherlands
  2. 2.Dipartimento di Elettronica ApplicataUniversità degli Studi di Roma "Roma Tre"RomaItaly

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