On Symmetry, Perspectivity, and Level-Set-Based Segmentation
We introduce a novel variational method for the extraction of objects with either bilateral or rotational symmetry in the presence of perspective distortion. Information on the symmetry axis of the object and the distorting transformation is obtained as a by-product of the segmentation process. The...
| Main Authors: | , , |
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2010
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| Online Access: | http://hdl.handle.net/1721.1/60292 |
| _version_ | 1811077005518569472 |
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| author | Riklin-Raviv, Tammy Sochen, Nir Kiryati, Nahum |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Riklin-Raviv, Tammy Sochen, Nir Kiryati, Nahum |
| author_sort | Riklin-Raviv, Tammy |
| collection | MIT |
| description | We introduce a novel variational method for the extraction of objects with either bilateral or rotational symmetry in the presence of perspective distortion. Information on the symmetry axis of the object and the distorting transformation is obtained as a by-product of the segmentation process. The key idea is the use of a flip or a rotation of the image to segment as if it were another view of the object. We call this generated image the symmetrical counterpart image. We show that the symmetrical counterpart image and the source image are related by planar projective homography. This homography is determined by the unknown planar projective transformation that distorts the object symmetry. The proposed segmentation method uses a level-set-based curve evolution technique. The extraction of the object boundaries is based on the symmetry constraint and the image data. The symmetrical counterpart of the evolving level-set function provides a dynamic shape prior. It supports the segmentation by resolving possible ambiguities due to noise, clutter, occlusions, and assimilation with the background. The homography that aligns the symmetrical counterpart to the source level-set is recovered via a registration process carried out concurrently with the segmentation. Promising segmentation results of various images of approximately symmetrical objects are shown. |
| first_indexed | 2024-09-23T10:34:58Z |
| format | Article |
| id | mit-1721.1/60292 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:34:58Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/602922022-09-27T10:00:12Z On Symmetry, Perspectivity, and Level-Set-Based Segmentation Riklin-Raviv, Tammy Sochen, Nir Kiryati, Nahum Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Riklin-Raviv, Tammy Riklin-Raviv, Tammy We introduce a novel variational method for the extraction of objects with either bilateral or rotational symmetry in the presence of perspective distortion. Information on the symmetry axis of the object and the distorting transformation is obtained as a by-product of the segmentation process. The key idea is the use of a flip or a rotation of the image to segment as if it were another view of the object. We call this generated image the symmetrical counterpart image. We show that the symmetrical counterpart image and the source image are related by planar projective homography. This homography is determined by the unknown planar projective transformation that distorts the object symmetry. The proposed segmentation method uses a level-set-based curve evolution technique. The extraction of the object boundaries is based on the symmetry constraint and the image data. The symmetrical counterpart of the evolving level-set function provides a dynamic shape prior. It supports the segmentation by resolving possible ambiguities due to noise, clutter, occlusions, and assimilation with the background. The homography that aligns the symmetrical counterpart to the source level-set is recovered via a registration process carried out concurrently with the segmentation. Promising segmentation results of various images of approximately symmetrical objects are shown. MUSCLE Network of Excellence Yizhak and Chaya Weinstein Research Institute for Signal Processing Universiṭat Tel-Aviv. A.M.N. Foundation 2010-12-14T19:59:03Z 2010-12-14T19:59:03Z 2009-08 2009-05 Article http://purl.org/eprint/type/JournalArticle 0162-8828 INSPEC Accession Number: 10721314 http://hdl.handle.net/1721.1/60292 Riklin-Raviv, T., N. Sochen, and N. Kiryati. “On Symmetry, Perspectivity, and Level-Set-Based Segmentation.” Pattern Analysis and Machine Intelligence, IEEE Transactions on 31.8 (2009): 1458-1471. © 2009 IEEE. en_US http://dx.doi.org/10.1109/tpami.2008.160 IEEE Transactions on Pattern Analysis and Machine Intelligence Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Riklin-Raviv, Tammy Sochen, Nir Kiryati, Nahum On Symmetry, Perspectivity, and Level-Set-Based Segmentation |
| title | On Symmetry, Perspectivity, and Level-Set-Based Segmentation |
| title_full | On Symmetry, Perspectivity, and Level-Set-Based Segmentation |
| title_fullStr | On Symmetry, Perspectivity, and Level-Set-Based Segmentation |
| title_full_unstemmed | On Symmetry, Perspectivity, and Level-Set-Based Segmentation |
| title_short | On Symmetry, Perspectivity, and Level-Set-Based Segmentation |
| title_sort | on symmetry perspectivity and level set based segmentation |
| url | http://hdl.handle.net/1721.1/60292 |
| work_keys_str_mv | AT riklinravivtammy onsymmetryperspectivityandlevelsetbasedsegmentation AT sochennir onsymmetryperspectivityandlevelsetbasedsegmentation AT kiryatinahum onsymmetryperspectivityandlevelsetbasedsegmentation |