A Biologically Motivated Multiresolution Approach to Contour Detection
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 con...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2007-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | http://dx.doi.org/10.1155/2007/71828 |
| _version_ | 1818148431218081792 |
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| author | Alessandro Neri Nicolai Petkov Patrizio Campisi Giuseppe Papari |
| author_facet | Alessandro Neri Nicolai Petkov Patrizio Campisi Giuseppe Papari |
| author_sort | Alessandro Neri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T12:51:02Z |
| format | Article |
| id | doaj.art-2ffa07c4c1884187a31c676e0056f181 |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-11T12:51:02Z |
| publishDate | 2007-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-2ffa07c4c1884187a31c676e0056f1812022-12-22T01:06:41ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802007-01-01200710.1155/2007/71828A Biologically Motivated Multiresolution Approach to Contour DetectionAlessandro NeriNicolai PetkovPatrizio CampisiGiuseppe PapariStandard 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.http://dx.doi.org/10.1155/2007/71828 |
| spellingShingle | Alessandro Neri Nicolai Petkov Patrizio Campisi Giuseppe Papari A Biologically Motivated Multiresolution Approach to Contour Detection EURASIP Journal on Advances in Signal Processing |
| title | A Biologically Motivated Multiresolution Approach to Contour Detection |
| title_full | A Biologically Motivated Multiresolution Approach to Contour Detection |
| title_fullStr | A Biologically Motivated Multiresolution Approach to Contour Detection |
| title_full_unstemmed | A Biologically Motivated Multiresolution Approach to Contour Detection |
| title_short | A Biologically Motivated Multiresolution Approach to Contour Detection |
| title_sort | biologically motivated multiresolution approach to contour detection |
| url | http://dx.doi.org/10.1155/2007/71828 |
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