Weighted geometric grammars for object detection in context
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2010
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| Online Access: | http://hdl.handle.net/1721.1/60155 |
| _version_ | 1826196214292414464 |
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| author | Lippow, Margaret Aycinena |
| author2 | Leslie Pack Kaelbling and Tomáis Lozano-Pérez. |
| author_facet | Leslie Pack Kaelbling and Tomáis Lozano-Pérez. Lippow, Margaret Aycinena |
| author_sort | Lippow, Margaret Aycinena |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. |
| first_indexed | 2024-09-23T10:23:19Z |
| format | Thesis |
| id | mit-1721.1/60155 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T10:23:19Z |
| publishDate | 2010 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/601552019-04-10T08:53:52Z Weighted geometric grammars for object detection in context WGGs for object detection in context Lippow, Margaret Aycinena Leslie Pack Kaelbling and Tomáis Lozano-Pérez. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. Cataloged from PDF version of thesis. Includes bibliographical references (p. 155-161). This thesis addresses the problem of detecting objects in images of complex scenes. Strong patterns exist in the types and spatial arrangements of objects that occur in scenes, and we seek to exploit these patterns to improve detection performance. We introduce a novel formalism-weighted geometric grammars (WGGs)-for flexibly representing and recognizing combinations of objects and their spatial relationships in scenes. We adapt the structured perceptron algorithm to parameter learning in WGG models, and develop a set of original clustering-based algorithms for structure learning. We then demonstrate empirically that WGG models, with parameters and structure learned automatically from data, can outperform a standard object detector. This thesis also contributes three new fully-labeled datasets, in two domains, to the scene understanding community. by Margaret Aycinena Lippow. Ph.D. 2010-12-06T17:30:52Z 2010-12-06T17:30:52Z 2010 2010 Thesis http://hdl.handle.net/1721.1/60155 681624242 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 161 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Lippow, Margaret Aycinena Weighted geometric grammars for object detection in context |
| title | Weighted geometric grammars for object detection in context |
| title_full | Weighted geometric grammars for object detection in context |
| title_fullStr | Weighted geometric grammars for object detection in context |
| title_full_unstemmed | Weighted geometric grammars for object detection in context |
| title_short | Weighted geometric grammars for object detection in context |
| title_sort | weighted geometric grammars for object detection in context |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/60155 |
| work_keys_str_mv | AT lippowmargaretaycinena weightedgeometricgrammarsforobjectdetectionincontext AT lippowmargaretaycinena wggsforobjectdetectionincontext |