2026-09-22T20:00:40Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/601552022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Leslie Pack Kaelbling and Tomáis Lozano-Pérez.
Lippow, Margaret Aycinena
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-12-06T17:30:52Z
2010-12-06T17:30:52Z
2010
2010
http://hdl.handle.net/1721.1/60155
681624242
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.
161 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Weighted geometric grammars for object detection in context
WGGs for object detection in context
Thesis
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<Abstract>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.</Abstract>
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