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Digital video moving object segmentation using tensor voting: A non-causal, accurate approach
Motion based video segmentation is important in many video processing applications such as MPEG4. This thesis presents an exhaustive, non-causal method to estimate boundaries between moving objects in a video clip. It make use of tensor voting principles. The tensor voting is adapted to allow image...
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| Format: | Thesis |
| Language: | English |
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Department of Electrical Engineering
2014
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| _version_ | 1867613210808418304 |
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| access_status_str | Open Access |
| author | Guest, Ian |
| author2 | Nicolls, Fred |
| author_browse | Guest, Ian Nicolls, Fred |
| author_facet | Nicolls, Fred Guest, Ian |
| author_sort | Guest, Ian |
| collection | Thesis |
| description | Motion based video segmentation is important in many video processing applications such as MPEG4. This thesis presents an exhaustive, non-causal method to estimate boundaries between moving objects in a video clip. It make use of tensor voting principles. The tensor voting is adapted to allow image structure to manifest in the tangential plane of the saliency map. The technique allows direct estimation of motion vectors from second-order tensor analysis. The tensors make maximal and direct use of the available information by encoding it into the dimensionality of the tensor. The tensor voting methodology introduces a non-symmetrical voting kernel to allow a measure of voting skewness to be inferred. Skewness is found in the third-order tensor in the direction of the tangential first eigenvector. This new concept is introduced as the Tensor Skewness Map or TS map. The TS map gives further information about whether an object is occluding or disoccluding another object. The information can be used to infer the layering order of the moving objects in the video clip. Matched filtering and detection are applied to reduce the TS map into occluding and disoccluding detections. The technique is computationally exhaustive, but may find use in off-line video object segmentation processes. The use of commercial-off-the-shelf Graphic Processor Units is demonstrated to scale well to the tensor voting framework, providing the computational speed improvement required to make the framework realisable on a larger scale and to handle tensor dimensionalities higher than before. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/5209 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:31.718Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/5209 Digital video moving object segmentation using tensor voting: A non-causal, accurate approach Guest, Ian Nicolls, Fred Electrical Engineering Motion based video segmentation is important in many video processing applications such as MPEG4. This thesis presents an exhaustive, non-causal method to estimate boundaries between moving objects in a video clip. It make use of tensor voting principles. The tensor voting is adapted to allow image structure to manifest in the tangential plane of the saliency map. The technique allows direct estimation of motion vectors from second-order tensor analysis. The tensors make maximal and direct use of the available information by encoding it into the dimensionality of the tensor. The tensor voting methodology introduces a non-symmetrical voting kernel to allow a measure of voting skewness to be inferred. Skewness is found in the third-order tensor in the direction of the tangential first eigenvector. This new concept is introduced as the Tensor Skewness Map or TS map. The TS map gives further information about whether an object is occluding or disoccluding another object. The information can be used to infer the layering order of the moving objects in the video clip. Matched filtering and detection are applied to reduce the TS map into occluding and disoccluding detections. The technique is computationally exhaustive, but may find use in off-line video object segmentation processes. The use of commercial-off-the-shelf Graphic Processor Units is demonstrated to scale well to the tensor voting framework, providing the computational speed improvement required to make the framework realisable on a larger scale and to handle tensor dimensionalities higher than before. 2014-07-31T10:57:04Z 2014-07-31T10:57:04Z 2009 Doctoral Thesis Doctoral http://hdl.handle.net/11427/5209 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Electrical Engineering Guest, Ian Digital video moving object segmentation using tensor voting: A non-causal, accurate approach |
| thesis_degree_str | Doctoral |
| title | Digital video moving object segmentation using tensor voting: A non-causal, accurate approach |
| title_full | Digital video moving object segmentation using tensor voting: A non-causal, accurate approach |
| title_fullStr | Digital video moving object segmentation using tensor voting: A non-causal, accurate approach |
| title_full_unstemmed | Digital video moving object segmentation using tensor voting: A non-causal, accurate approach |
| title_short | Digital video moving object segmentation using tensor voting: A non-causal, accurate approach |
| title_sort | digital video moving object segmentation using tensor voting a non causal accurate approach |
| topic | Electrical Engineering |
| url | http://hdl.handle.net/11427/5209 |
| work_keys_str_mv | AT guestian digitalvideomovingobjectsegmentationusingtensorvotinganoncausalaccurateapproach |