Learning and applying model-based visual context
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Patrick Henry Winston.
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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.
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 53).
DepartmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.