Understanding and predicting where people look in images
Name
751924500-MIT.pdf
Description
Full printable version
Size
23.6 MB
Format
Adobe PDF
Checksum (MD5)
b76b18ffa3a4030b6265baf49ed14ee5
Author(s)
Judd, Tilke (Tilke M.)
Advisor(s)
Frédo Durand and Antonio Torralba.
Date Issued
2011
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
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