Image Processing by Variational Methods, Stochastic Programming and Deep Learning Techniques
This thesis is to investigate effective approaches to tackle different problems in computer vision: variational methods are first studied for image processing, illusory contour reconstruction and segmentation as well as their efficiency improvement. Next, we develop variational segmentation methods...
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| Format: | Thesis |
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Curtin University
2020
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| Online Access: | http://hdl.handle.net/20.500.11937/82126 |
| _version_ | 1848764478463672320 |
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| author | Tan, Lu |
| author_facet | Tan, Lu |
| author_sort | Tan, Lu |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This thesis is to investigate effective approaches to tackle different problems in computer vision: variational methods are first studied for image processing, illusory contour reconstruction and segmentation as well as their efficiency improvement. Next, we develop variational segmentation methods by stochastic programming, tackling diverse problems with random noises. Third, the fusion approaches integrating varaitional models and deep neural networks are explored for challenging image tasks. These innovative ideas are validated by significant performance gains. |
| first_indexed | 2025-11-14T11:20:00Z |
| format | Thesis |
| id | curtin-20.500.11937-82126 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:20:00Z |
| publishDate | 2020 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-821262023-01-12T08:18:18Z Image Processing by Variational Methods, Stochastic Programming and Deep Learning Techniques Tan, Lu This thesis is to investigate effective approaches to tackle different problems in computer vision: variational methods are first studied for image processing, illusory contour reconstruction and segmentation as well as their efficiency improvement. Next, we develop variational segmentation methods by stochastic programming, tackling diverse problems with random noises. Third, the fusion approaches integrating varaitional models and deep neural networks are explored for challenging image tasks. These innovative ideas are validated by significant performance gains. 2020 Thesis http://hdl.handle.net/20.500.11937/82126 Curtin University fulltext |
| spellingShingle | Tan, Lu Image Processing by Variational Methods, Stochastic Programming and Deep Learning Techniques |
| title | Image Processing by Variational Methods, Stochastic Programming and Deep Learning Techniques |
| title_full | Image Processing by Variational Methods, Stochastic Programming and Deep Learning Techniques |
| title_fullStr | Image Processing by Variational Methods, Stochastic Programming and Deep Learning Techniques |
| title_full_unstemmed | Image Processing by Variational Methods, Stochastic Programming and Deep Learning Techniques |
| title_short | Image Processing by Variational Methods, Stochastic Programming and Deep Learning Techniques |
| title_sort | image processing by variational methods, stochastic programming and deep learning techniques |
| url | http://hdl.handle.net/20.500.11937/82126 |