Visual Task Inference Using Hidden Markov Models
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1678-1682.
Abstract
It has been known for a long time that visual task, such as reading, counting and searching, greatly influences eye movement patterns. Perhaps the best known demonstration of this is the celebrated study of Yarbus showing that different eye movement trajectories emerge depending on the visual task that the viewers are given. The objective of this paper is to develop an inverse Yarbus process whereby we can infer the visual task by observing the measurements of a viewer’s eye movements while executing the visual task. The method we are proposing is to use Hidden Markov Models (HMMs) to create a probabilistic framework to infer the viewer’s task from eye movements.
@inproceedings{ijcai2011p282,
title = {Visual Task Inference Using Hidden Markov Models},
author = {Abolhassani, Amin Haji and Clark, James J.},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1678--1682},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-282},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-282},
}
TY - CPAPER TI - Visual Task Inference Using Hidden Markov Models AU - Abolhassani, Amin Haji AU - Clark, James J. T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1678 EP - 1682 DO - 10.5591/978-1-57735-516-8/IJCAI11-282 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-282 ER -Copied
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