Regularizing variational methods for robust object boundary detection
In this thesis, robust object boundary detection by the variational methods is studied. The variational methods fall into two categories: boundary-based and region based. This thesis focuses on the boundary-based variational methods. However, as the boundary-based variational methods depend only on...
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| Format: | Thesis |
| Language: | English |
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2009
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| Online Access: | https://hdl.handle.net/10356/14582 |
| _version_ | 1824455297276575744 |
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| author | Fang, Wen |
| author2 | Chan Kap Luk |
| author_facet | Chan Kap Luk Fang, Wen |
| author_sort | Fang, Wen |
| collection | NTU |
| description | In this thesis, robust object boundary detection by the variational methods is studied. The variational methods fall into two categories: boundary-based and region based. This thesis focuses on the boundary-based variational methods. However, as the boundary-based variational methods depend only on the boundary information to locate the object, they are very sensitive to the disruptions to the object boundary. If the object is partially occluded by other objects or is interfered by cluttered background, the evolving curve of the variational methods may be pulled away from the real boundary and converge to the wrong place. This is regarded as the “missing boundary” problem in this thesis. In order to achieve a robust object detection, additional boundary constraints need to be incorporated into the variational methods to regularize the curve evolution. In this thesis, two new boundary constraints are proposed. The first method incorporates the temporal information into the variational methods, while the second incorporates the shape prior information. |
| first_indexed | 2025-02-19T03:35:58Z |
| format | Thesis |
| id | ntu-10356/14582 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:35:58Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | ntu-10356/145822023-07-04T17:26:13Z Regularizing variational methods for robust object boundary detection Fang, Wen Chan Kap Luk School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision In this thesis, robust object boundary detection by the variational methods is studied. The variational methods fall into two categories: boundary-based and region based. This thesis focuses on the boundary-based variational methods. However, as the boundary-based variational methods depend only on the boundary information to locate the object, they are very sensitive to the disruptions to the object boundary. If the object is partially occluded by other objects or is interfered by cluttered background, the evolving curve of the variational methods may be pulled away from the real boundary and converge to the wrong place. This is regarded as the “missing boundary” problem in this thesis. In order to achieve a robust object detection, additional boundary constraints need to be incorporated into the variational methods to regularize the curve evolution. In this thesis, two new boundary constraints are proposed. The first method incorporates the temporal information into the variational methods, while the second incorporates the shape prior information. DOCTOR OF PHILOSOPHY (EEE) 2009-01-09T06:06:46Z 2009-01-09T06:06:46Z 2008 2008 Thesis Fang, W. (2008). Regularizing variational methods for robust object boundary detection. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/14582 10.32657/10356/14582 en 164 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Fang, Wen Regularizing variational methods for robust object boundary detection |
| title | Regularizing variational methods for robust object boundary detection |
| title_full | Regularizing variational methods for robust object boundary detection |
| title_fullStr | Regularizing variational methods for robust object boundary detection |
| title_full_unstemmed | Regularizing variational methods for robust object boundary detection |
| title_short | Regularizing variational methods for robust object boundary detection |
| title_sort | regularizing variational methods for robust object boundary detection |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision |
| url | https://hdl.handle.net/10356/14582 |
| work_keys_str_mv | AT fangwen regularizingvariationalmethodsforrobustobjectboundarydetection |