Kalman Filtering in the Design of Eye-Gaze-Guided Computer Interfaces

  • Oleg V. Komogortsev
  • Javed I. Khan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4552)

Abstract

In this paper, we design an Attention Focus Kalman Filter (AFKF) - a framework that offers interaction capabilities by constructing an eye-movement language, provides real-time perceptual compression through Human Visual System (HVS) modeling, and improves system’s reliability. These goals are achieved by an AFKF through identification of basic eye-movement types in real-time, the prediction of a user’s perceptual attention focus, and the use of the eye’s visual sensitivity function and eye-position data signal de-noising.

Keywords

Human Visual System Modeling Kalman Filter Human Computer Interaction Perceptual Compression 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Jacob, R.J.K.: Eye tracking in advanced interface design, Virtual environments and advanced interface design. Oxford University Press, Inc., New York, NY (1995)Google Scholar
  2. 2.
    Ware, C., Mikaelian, H.T.: An Evaluation of an Eye Tracker as a Device for Computer Input. In: Proc. ACM CHI+GI’87 Human Factors in Computing Systems Conference pp. 183–188 (1987)Google Scholar
  3. 3.
    Komogortsev, O.: World of Warcraft Percept Interface, http://www.cs.kent.edu/~okomogor/wowpercept/wowpercept.htm
  4. 4.
    Duchowski, A.T.: Eye Tracking Methodology: Theory and Practic. Springer, London, UK (2003)Google Scholar
  5. 5.
    Grindinger, T.: Eye Movement Analysis and Prediction with the Kalman Filter, Masters thesis, Computer Science, Clemson University, Clemson, SC, USA (August 2006)Google Scholar
  6. 6.
    Bahill, A.T.: Development, validation and sensitivity analyses of human eye movement models. CRC Critical Reviews in Bioengineering 4, 311–355 (1980)Google Scholar
  7. 7.
    Komogortsev, O., Khan, J.: Perceptual Multimedia Compression based on the Predictive Kalman Filter Eye Movement Modeling. In: Proceedings of the Multimedia Computing and Networking Conference (MMCN 2007), San Jose, pp. 1–12 (January 28 – February 1, 2007)Google Scholar
  8. 8.
    Sauter, D., Martin, B.J., Di Renzo, N., Vomscheid, C.: Analysis of eyetracking movements using innovations generated by a Kalman filter. Med. Biol. Eng. Comput 29, 63–69 (1991)CrossRefGoogle Scholar
  9. 9.
    Norimichi, T., Chizuko, E., Hideaki, H., Yoichi, M.: Image compression and decompression based on gazing area. In: Human Vision and Electronic Imagin, SPIE (April 1996)Google Scholar
  10. 10.
    Stelmach, L.B., Tam, W.J.: Processing image sequences based on eye movements. In: Proc. SPIE 2179, 90–98 (1994)Google Scholar
  11. 11.
    Murphy, H., Duchowski, A.T.: Gaze-Contigent Level Of Detail Rendering. Eurographics (2001)Google Scholar
  12. 12.
    Carpenter, R.H.S.: Movements of the Eyes, pp. 56–57. Pion, London (1977)Google Scholar
  13. 13.
    Robinson, D.A.: Models of the saccadic eye movement control system. Kybernetic 14, 71 (1973)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Oleg V. Komogortsev
    • 1
  • Javed I. Khan
    • 1
  1. 1.Perceptual Engineering Laboratory, Department of Computer Science, Kent State University, Kent, OH, 44242USA

Personalised recommendations