2026-09-21T07:06:37Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/660082022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Frédo Durand and Antonio Torralba.
Judd, Tilke (Tilke M.)
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-09-27T18:31:47Z
2011-09-27T18:31:47Z
2011
2011
http://hdl.handle.net/1721.1/66008
751924500
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 115-126).
For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene. This is a challenging task given that no one fully understands how the human visual system works. This thesis explores the way people look at different types of images and provides methods of predicting where they look in new scenes. We describe a new way to model where people look from ground truth eye tracking data using techniques of machine learning that outperforms all existing models, and provide a benchmark data set to quantitatively compare existing and future models. In addition we explore how image resolution affects where people look. Our experiments, models, and large eye tracking data sets should help future researchers better understand and predict where people look in order to create more powerful computational vision systems.
by Tilke Judd.
Ph.D.
126 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.
Understanding and predicting where people look in images
Thesis
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