Weighted geometric grammars for object detection in context
Name
681624242-MIT.pdf
Description
Full printable version
Size
26.78 MB
Format
Adobe PDF
Checksum (MD5)
57e9c9688ed75dc23dad0d6562fee9ce
Author(s)
Lippow, Margaret Aycinena
Advisor(s)
Leslie Pack Kaelbling and Tomáis Lozano-Pérez.
Alternative Title
WGGs for object detection in context
Date Issued
2010
Publisher
Massachusetts Institute of Technology
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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