A Biologically Motivated Multiresolution Approach to Contour Detection. (22nd January 2007)

• Record Type: Journal Article
• Authors: Papari Papari, Giuseppe Giuseppe; Campisi Campisi, Patrizio Patrizio; Petkov Petkov, Nicolai Nicolai; Neri Neri, Alessandro Alessandro
• Other Names: Morrone Morrone Maria Concetta Maria Concetta Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2007(2007)
• Publication Date: 2007-01-22
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2007/71828
• Languages: English
• ISSNs: 1687-6172

Abstract

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.