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A neural network for 3D gaze recording with binocular eye trackers.

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Title: A neural network for 3D gaze recording with binocular eye trackers.
Authors: Essig, Kai1 kessig@techfak.uni-bielefeld.de, Pomplun, Marc2, Ritter, Helge2
Source: International Journal of Parallel, Emergent & Distributed Systems. Apr2006, Vol. 21 Issue 2, p79-95. 17p. 1 Black and White Photograph, 4 Diagrams, 3 Charts, 5 Graphs.
Subjects: Artificial satellite tracking, Binoculars, Three-dimensional display systems, Geometry, Artificial neural networks, Artificial intelligence, Calibration
Abstract: Using eye tracking for the investigation of visual attention has become increasingly popular during the last few decades. Nevertheless, only a small number of eye tracking studies have employed 3D displays, although such displays would closely resemble our natural visual environment. Besides higher cost and effort for the experimental setup, the main reason for the avoidance of 3D displays is the problem of computing a subject's current 3D gaze position based on the measured binocular gaze angles. The geometrical approaches to this problem that have been studied so far involved substantial error in the measurement of 3D gaze trajectories. In order to tackle this problem, we developed an anaglyph-based 3D calibration procedure and used a well-suited type of artificial neural network—a parametrized self-organizing map (PSOM)—to estimate the 3D gaze point from a subject's binocular eye-position data. We report an experiment in which the accuracy of the PSOM gaze-point estimation is compared to a geometrical solution. The results show that the neural network approach produces more accurate results than the geometrical method, especially for the depth axis and for distant stimuli. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Parallel, Emergent & Distributed Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 19352264
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A neural network for 3D gaze recording with binocular eye trackers.
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  Data: <searchLink fieldCode="AR" term="%22Essig%2C+Kai%22">Essig, Kai</searchLink><relatesTo>1</relatesTo><i> kessig@techfak.uni-bielefeld.de</i><br /><searchLink fieldCode="AR" term="%22Pomplun%2C+Marc%22">Pomplun, Marc</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Ritter%2C+Helge%22">Ritter, Helge</searchLink><relatesTo>2</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Parallel%2C+Emergent+%26+Distributed+Systems%22">International Journal of Parallel, Emergent & Distributed Systems</searchLink>. Apr2006, Vol. 21 Issue 2, p79-95. 17p. 1 Black and White Photograph, 4 Diagrams, 3 Charts, 5 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+satellite+tracking%22">Artificial satellite tracking</searchLink><br /><searchLink fieldCode="DE" term="%22Binoculars%22">Binoculars</searchLink><br /><searchLink fieldCode="DE" term="%22Three-dimensional+display+systems%22">Three-dimensional display systems</searchLink><br /><searchLink fieldCode="DE" term="%22Geometry%22">Geometry</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Using eye tracking for the investigation of visual attention has become increasingly popular during the last few decades. Nevertheless, only a small number of eye tracking studies have employed 3D displays, although such displays would closely resemble our natural visual environment. Besides higher cost and effort for the experimental setup, the main reason for the avoidance of 3D displays is the problem of computing a subject's current 3D gaze position based on the measured binocular gaze angles. The geometrical approaches to this problem that have been studied so far involved substantial error in the measurement of 3D gaze trajectories. In order to tackle this problem, we developed an anaglyph-based 3D calibration procedure and used a well-suited type of artificial neural network—a parametrized self-organizing map (PSOM)—to estimate the 3D gaze point from a subject's binocular eye-position data. We report an experiment in which the accuracy of the PSOM gaze-point estimation is compared to a geometrical solution. The results show that the neural network approach produces more accurate results than the geometrical method, especially for the depth axis and for distant stimuli. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Parallel, Emergent & Distributed Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/17445760500354440
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      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 79
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      – SubjectFull: Artificial satellite tracking
        Type: general
      – SubjectFull: Binoculars
        Type: general
      – SubjectFull: Three-dimensional display systems
        Type: general
      – SubjectFull: Geometry
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Calibration
        Type: general
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      – TitleFull: A neural network for 3D gaze recording with binocular eye trackers.
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            NameFull: Essig, Kai
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            NameFull: Pomplun, Marc
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            – D: 01
              M: 04
              Text: Apr2006
              Type: published
              Y: 2006
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            – TitleFull: International Journal of Parallel, Emergent & Distributed Systems
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