A data-driven learning approach to image registration
Handling large displacement optical flow is a remarkably arduous task. For instance, standard coarse-to-fine techniques often struggle to adequately deal with moving objects whose motion exceeds their size. Here we propose a learning approach to the estimation of large displacement between two non-c...
| Main Author: | |
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
2016
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| Online Access: | https://eprints.nottingham.ac.uk/33723/ |
| _version_ | 1848794689892777984 |
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| author | Mustafa, Mohammad A.R. |
| author_facet | Mustafa, Mohammad A.R. |
| author_sort | Mustafa, Mohammad A.R. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Handling large displacement optical flow is a remarkably arduous task. For instance, standard coarse-to-fine techniques often struggle to adequately deal with moving objects whose motion exceeds their size. Here we propose a learning approach to the estimation of large displacement between two non-consecutive images in a sequence on the basis of a learning set of optical flows estimated a priori between different consecutive images in the same sequence. Our method refines an initial estimate of the flow field by replacing each displacement vector by a linear combination of displacement vectors at the center of similar patches taken from a code-book built from the learning set. The key idea is to use the accurate flows estimated a priori between consecutive images to help improve the potentially less accurate flows estimated online between images further apart. Experimental results suggest the ability of a purely data-driven learning approach to handle fine scale structures with large displacements. |
| first_indexed | 2025-11-14T19:20:11Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-33723 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T19:20:11Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-337232025-02-28T13:29:15Z https://eprints.nottingham.ac.uk/33723/ A data-driven learning approach to image registration Mustafa, Mohammad A.R. Handling large displacement optical flow is a remarkably arduous task. For instance, standard coarse-to-fine techniques often struggle to adequately deal with moving objects whose motion exceeds their size. Here we propose a learning approach to the estimation of large displacement between two non-consecutive images in a sequence on the basis of a learning set of optical flows estimated a priori between different consecutive images in the same sequence. Our method refines an initial estimate of the flow field by replacing each displacement vector by a linear combination of displacement vectors at the center of similar patches taken from a code-book built from the learning set. The key idea is to use the accurate flows estimated a priori between consecutive images to help improve the potentially less accurate flows estimated online between images further apart. Experimental results suggest the ability of a purely data-driven learning approach to handle fine scale structures with large displacements. 2016-07-19 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/33723/1/Thesis%20Mohammad%20A%20R%20Mustafa%20ID%204151693.pdf Mustafa, Mohammad A.R. (2016) A data-driven learning approach to image registration. PhD thesis, University of Nottingham. |
| spellingShingle | Mustafa, Mohammad A.R. A data-driven learning approach to image registration |
| title | A data-driven learning approach to image registration |
| title_full | A data-driven learning approach to image registration |
| title_fullStr | A data-driven learning approach to image registration |
| title_full_unstemmed | A data-driven learning approach to image registration |
| title_short | A data-driven learning approach to image registration |
| title_sort | data-driven learning approach to image registration |
| url | https://eprints.nottingham.ac.uk/33723/ |