Learning and applying model-based visual context
Name
62242093-MIT.pdf
Description
Full printable version
Size
6.36 MB
Format
Adobe PDF
Checksum (MD5)
1235561cc80bb49f9a80f4c7f62e3e3f
Author(s)
Gilja, Vikash
Advisor(s)
Patrick Henry Winston.
Date Issued
2004
Publisher
Massachusetts Institute of Technology
Abstract
I believe that context's ability to reduce the ambiguity of an input signal makes it a vital constraint for understanding the real world. I specifically examine the role of context in vision and how a model-based approach can aid visual search and recognition. Through the implementation of a system capable of learning visual context models from an image database, I demonstrate the utility of the model-based approach. The system is capable of learning models for "water-horizon scenes" and "suburban street scenes" from a database of 745 images.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 53).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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