A Biologically Motivated Multiresolution Approach to Contour Detection
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Research Article A Biologically Motivated Multiresolution Approach to Contour Detection Giuseppe Papari,1 Patrizio Campisi,2 Nicolai Petkov,1 and Alessandro Neri2 1 Institute
of Mathematics and Computing Science, University of Groningen, P.O. Box 800, 9700 AV Groningen, The Netherlands di Elettronica Applicata, Universit`a degli Studi di Roma “Roma Tre”, Via della Vasca Navale 84, 00146 Roma, Italy
2 Dipartimento
Received 3 January 2006; Revised 3 November 2006; Accepted 3 November 2006 Recommended by Maria Concetta Morrone 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. Copyright © 2007 Giuseppe Papari et al. 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.
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INTRODUCTION
Contour detection is a fundamental operation in image processing and computer vision which, despite of the large number of studies published in the last two decades, is still a fertile field of ongoing research. Many edge detectors have been proposed in the literature. However, they react to all local luminance changes above a given threshold, irrespective of their origin—object contours or textures. Our goal is to isolate objects in a scene; therefore, some further processing is needed beyond general-purpose edge detection. Examples of edge detectors proposed in previous works are operators that incorporate linear filtering [1–5], local orientation analysis [4, 6, 7], fitting of analytical models to the image data [8–11]. In [12], a simple energy model is introduced to simulate perception of perceptually significant elements like lines and edges. Edge detectors using local energy principles have also been proposed in [13–16]. Since these operators do not make any difference between various types of edges,
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