Understanding and predicting where people look in images

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2011

Author dc:creator

• Judd, Tilke (Tilke M.)

Advisor dc:contributor.advisor

• Frédo Durand and Antonio Torralba.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/66008

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/66008