Multiscale Detection of Circles, Ellipses and Line Segments, Robust to Noise and Blur
This paper proposes a basic taxonomy of image contours. Our goal is to classify smooth curves into five categories, namely, circles, ellipses, line segments, arcs of circles and arcs of ellipses. These geometrical structures have been chosen as they serve as input of many computer vision tasks. The...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9345684/ |
| _version_ | 1818933389186564096 |
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| author | Onofre Martorell Antoni Buades Jose Luis Lisani |
| author_facet | Onofre Martorell Antoni Buades Jose Luis Lisani |
| author_sort | Onofre Martorell |
| collection | DOAJ |
| description | This paper proposes a basic taxonomy of image contours. Our goal is to classify smooth curves into five categories, namely, circles, ellipses, line segments, arcs of circles and arcs of ellipses. These geometrical structures have been chosen as they serve as input of many computer vision tasks. The proposed strategy is applied on a set of initial disjoint contours, which are grouped together to form the aforementioned structures. These, in turn, are validated using an a contrario approach that guarantees a reduced number of false detections. The use of a multiscale strategy permits the detection at different resolution levels, which makes the method robust to noise and blur. |
| first_indexed | 2024-12-20T04:47:36Z |
| format | Article |
| id | doaj.art-e8d01284287044b6b58e1554553d996f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T04:47:36Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e8d01284287044b6b58e1554553d996f2022-12-21T19:52:57ZengIEEEIEEE Access2169-35362021-01-019255542557810.1109/ACCESS.2021.30567959345684Multiscale Detection of Circles, Ellipses and Line Segments, Robust to Noise and BlurOnofre Martorell0https://orcid.org/0000-0002-9071-778XAntoni Buades1https://orcid.org/0000-0001-9832-3358Jose Luis Lisani2https://orcid.org/0000-0002-7004-2252Department of Mathematics and Computer Science, Institute of Applied Computing and Community Code (IAC3), Universitat de les Illes Balears, Palma, SpainDepartment of Mathematics and Computer Science, Institute of Applied Computing and Community Code (IAC3), Universitat de les Illes Balears, Palma, SpainDepartment of Mathematics and Computer Science, Institute of Applied Computing and Community Code (IAC3), Universitat de les Illes Balears, Palma, SpainThis paper proposes a basic taxonomy of image contours. Our goal is to classify smooth curves into five categories, namely, circles, ellipses, line segments, arcs of circles and arcs of ellipses. These geometrical structures have been chosen as they serve as input of many computer vision tasks. The proposed strategy is applied on a set of initial disjoint contours, which are grouped together to form the aforementioned structures. These, in turn, are validated using an a contrario approach that guarantees a reduced number of false detections. The use of a multiscale strategy permits the detection at different resolution levels, which makes the method robust to noise and blur.https://ieeexplore.ieee.org/document/9345684/Line segment detectioncircle detectionellipse detectiona contrario validation |
| spellingShingle | Onofre Martorell Antoni Buades Jose Luis Lisani Multiscale Detection of Circles, Ellipses and Line Segments, Robust to Noise and Blur IEEE Access Line segment detection circle detection ellipse detection a contrario validation |
| title | Multiscale Detection of Circles, Ellipses and Line Segments, Robust to Noise and Blur |
| title_full | Multiscale Detection of Circles, Ellipses and Line Segments, Robust to Noise and Blur |
| title_fullStr | Multiscale Detection of Circles, Ellipses and Line Segments, Robust to Noise and Blur |
| title_full_unstemmed | Multiscale Detection of Circles, Ellipses and Line Segments, Robust to Noise and Blur |
| title_short | Multiscale Detection of Circles, Ellipses and Line Segments, Robust to Noise and Blur |
| title_sort | multiscale detection of circles ellipses and line segments robust to noise and blur |
| topic | Line segment detection circle detection ellipse detection a contrario validation |
| url | https://ieeexplore.ieee.org/document/9345684/ |
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