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Visual attention using 2D & 3D displays |
Zdziarski, Zbigniew
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In the past three decades, robotists and computer vision scientists, inspired by psychological
and neurophysiological studies, have developed many computational models
of attentions (CMAs) that mimic the behaviour of the human visual system in order
to predict where humans will focus their attention. Most of CMA research has
been focussing on the visual perception of images and videos displayed on 2D screens.
There has recently, however, been a surge in devices that can display media in 3D and
CMAs in this domain are becoming increasingly important. Research in this context
is minimal, however. This thesis attempts to alleviate this problem. We explore the
Graph-Based Visual Saliency algorithm [68] and extend it into 3D by developing a new
depth incorporation method. We also propose a new online eye tracker calibration
procedure that is more accurate and faster than standard processes and is also able
to give confidence values associated with each eye position reading. Eye tracking data
is used to evaluate CMAs. We use our novel eye tracking method to create a 2D/3D
video eye tracking dataset obtained from 50 people. A statistical analysis is performed
to locate where perception differs in 2D and 3D in videos. Taking advantage of the
uncertainties associated with our eye tracking data, we also propose a novel Gaussian
mixture model for computing eye tracking heat maps.
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Keyword(s):
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Statistics, Ph.D.; Ph.D. Trinity College Dublin |
Publication Date:
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2015 |
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Type:
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Doctoral thesis |
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Peer-Reviewed:
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Unknown |
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Language(s):
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English |
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Institution:
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Trinity College Dublin |
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Citation(s):
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Zbigniew Zdziarski, 'Visual attention using 2D & 3D displays', [thesis], Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2015, pp 132 |
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Publisher(s):
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Trinity College (Dublin, Ireland). School of Computer Science & Statistics |
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Supervisor(s):
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Dahyot, Rozenn |
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First Indexed:
2016-11-08 06:34:19 Last Updated:
2017-04-26 10:35:20 |
