A Biologically Motivated Multiresolution Approach to Contour Detection
<p/> <p>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 edg...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2007-01-01
|
| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | http://asp.eurasipjournals.com/content/2007/071828 |
| _version_ | 1819139336758624256 |
|---|---|
| author | Campisi Patrizio Neri Alessandro Papari Giuseppe Petkov Nicolai |
| author_facet | Campisi Patrizio Neri Alessandro Papari Giuseppe Petkov Nicolai |
| author_sort | Campisi Patrizio |
| collection | DOAJ |
| description | <p/> <p>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.</p> |
| first_indexed | 2024-12-22T11:21:03Z |
| format | Article |
| id | doaj.art-b8472d6844964d718d0a383fcd49eeae |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-22T11:21:03Z |
| publishDate | 2007-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-b8472d6844964d718d0a383fcd49eeae2022-12-21T18:27:53ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802007-01-0120071071828A Biologically Motivated Multiresolution Approach to Contour DetectionCampisi PatrizioNeri AlessandroPapari GiuseppePetkov Nicolai<p/> <p>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.</p>http://asp.eurasipjournals.com/content/2007/071828 |
| spellingShingle | Campisi Patrizio Neri Alessandro Papari Giuseppe Petkov Nicolai 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://asp.eurasipjournals.com/content/2007/071828 |
| work_keys_str_mv | AT campisipatrizio abiologicallymotivatedmultiresolutionapproachtocontourdetection AT nerialessandro abiologicallymotivatedmultiresolutionapproachtocontourdetection AT paparigiuseppe abiologicallymotivatedmultiresolutionapproachtocontourdetection AT petkovnicolai abiologicallymotivatedmultiresolutionapproachtocontourdetection AT campisipatrizio biologicallymotivatedmultiresolutionapproachtocontourdetection AT nerialessandro biologicallymotivatedmultiresolutionapproachtocontourdetection AT paparigiuseppe biologicallymotivatedmultiresolutionapproachtocontourdetection AT petkovnicolai biologicallymotivatedmultiresolutionapproachtocontourdetection |