What, where and how many? Combining object detectors and CRFs
Computer vision algorithms for individual tasks such as object recognition, detection and segmentation have shown impressive results in the recent past. The next challenge is to integrate all these algorithms and address the problem of scene understanding. This paper is a step towards this goal. We...
| मुख्य लेखकों: | , , , , |
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| स्वरूप: | Conference item |
| भाषा: | English |
| प्रकाशित: |
Springer
2010
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| _version_ | 1826315019979063296 |
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| author | Ladický, L Sturgess, P Alahari, K Russell, C Torr, PHS |
| author_facet | Ladický, L Sturgess, P Alahari, K Russell, C Torr, PHS |
| author_sort | Ladický, L |
| collection | OXFORD |
| description | Computer vision algorithms for individual tasks such as object recognition, detection and segmentation have shown impressive results in the recent past. The next challenge is to integrate all these algorithms and address the problem of scene understanding. This paper is a step towards this goal. We present a probabilistic framework for reasoning about regions, objects, and their attributes such as object class, location, and spatial extent. Our model is a Conditional Random Field defined on pixels, segments and objects. We define a global energy function for the model, which combines results from sliding window detectors, and low-level pixel-based unary and pairwise relations. One of our primary contributions is to show that this energy function can be solved efficiently. Experimental results show that our model achieves significant improvement over the baseline methods on CamVid and PASCAL VOC datasets. |
| first_indexed | 2024-12-09T03:16:42Z |
| format | Conference item |
| id | oxford-uuid:5f4c5ce3-16c7-471f-b7db-a8ad45777de8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-12-09T03:16:42Z |
| publishDate | 2010 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:5f4c5ce3-16c7-471f-b7db-a8ad45777de82024-10-24T14:39:52ZWhat, where and how many? Combining object detectors and CRFsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:5f4c5ce3-16c7-471f-b7db-a8ad45777de8EnglishSymplectic ElementsSpringer2010Ladický, LSturgess, PAlahari, KRussell, CTorr, PHSComputer vision algorithms for individual tasks such as object recognition, detection and segmentation have shown impressive results in the recent past. The next challenge is to integrate all these algorithms and address the problem of scene understanding. This paper is a step towards this goal. We present a probabilistic framework for reasoning about regions, objects, and their attributes such as object class, location, and spatial extent. Our model is a Conditional Random Field defined on pixels, segments and objects. We define a global energy function for the model, which combines results from sliding window detectors, and low-level pixel-based unary and pairwise relations. One of our primary contributions is to show that this energy function can be solved efficiently. Experimental results show that our model achieves significant improvement over the baseline methods on CamVid and PASCAL VOC datasets. |
| spellingShingle | Ladický, L Sturgess, P Alahari, K Russell, C Torr, PHS What, where and how many? Combining object detectors and CRFs |
| title | What, where and how many? Combining object detectors and CRFs |
| title_full | What, where and how many? Combining object detectors and CRFs |
| title_fullStr | What, where and how many? Combining object detectors and CRFs |
| title_full_unstemmed | What, where and how many? Combining object detectors and CRFs |
| title_short | What, where and how many? Combining object detectors and CRFs |
| title_sort | what where and how many combining object detectors and crfs |
| work_keys_str_mv | AT ladickyl whatwhereandhowmanycombiningobjectdetectorsandcrfs AT sturgessp whatwhereandhowmanycombiningobjectdetectorsandcrfs AT alaharik whatwhereandhowmanycombiningobjectdetectorsandcrfs AT russellc whatwhereandhowmanycombiningobjectdetectorsandcrfs AT torrphs whatwhereandhowmanycombiningobjectdetectorsandcrfs |