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A Biologically Motivated Multiresolution Approach to Contour Detection

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 071828 (2007) Cite this article

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.

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Authors and Affiliations

  1. Institute of Mathematics and Computing Science, University of Groningen, P.O. Box 800, Groningen, 9700 AV, The Netherlands

    Giuseppe Papari & Nicolai Petkov

  2. Dipartimento di Elettronica Applicata, Università degli Studi di Roma "Roma Tre", Via della Vasca Navale 84, Roma, 00146, Italy

    Patrizio Campisi & Alessandro Neri

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  1. Giuseppe Papari
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  2. Patrizio Campisi
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  3. Nicolai Petkov
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  4. Alessandro Neri
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Correspondence to Giuseppe Papari.

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Papari, G., Campisi, P., Petkov, N. et al. A Biologically Motivated Multiresolution Approach to Contour Detection. EURASIP J. Adv. Signal Process. 2007, 071828 (2007). https://doi.org/10.1155/2007/71828

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