Algorithm for Discriminating Aggregate Gaze Points: Comparison with Salient Regions-Of-Interest

  • Thomas J. Grindinger
  • Vidya N. Murali
  • Stephen Tetreault
  • Andrew T. Duchowski
  • Stan T. Birchfield
  • Pilar Orero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)


A novel method for distinguishing classes of viewers from their aggregated eye movements is described. The probabilistic framework accumulates uniformly sampled gaze as Gaussian point spread functions (heatmaps), and measures the distance of unclassified scanpaths to a previously classified set (or sets). A similarity measure is then computed over the scanpath durations. The approach is used to compare human observers’s gaze over video to regions of interest (ROIs) automatically predicted by a computational saliency model. Results show consistent discrimination between human and artificial ROIs, regardless of either of two differing instructions given to human observers (free or tasked viewing).


Video Sequence Video Frame Human Observer Saliency Model Dynamic Medium 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Thomas J. Grindinger
    • 1
  • Vidya N. Murali
    • 1
  • Stephen Tetreault
    • 2
  • Andrew T. Duchowski
    • 1
  • Stan T. Birchfield
    • 1
  • Pilar Orero
    • 3
  1. 1.Clemson UniversityUSA
  2. 2.Rhode Island CollegeUSA
  3. 3.Universitat Autònoma de BarcelonaSpain

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