Novel methods for analysis and visualization of saccade trajectories

Thomas Kübler, Wolfgang Fuhl, Raphael Rosenberg, Wolfgang Rosenstiel, Enkelejda Kasneci

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Visualization of eye-tracking data is mainly based on fixations. However, saccade trajectories and their characteristics might contain more information than sole fixation positions. Artists, for example, can influence the way our eyes traverse a picture by employing composition methods. Repetitive saccade trajectories and the sequence of eye movements seem to correlate with this composition. In this work, we propose two novel methods to visualize saccade patterns during static stimulus viewing. The first approach, so-called saccade heatmap, utilizes a modified Gaussian density distribution to highlight frequent gaze paths. The second approach is based on clustering and assigns identical labels to similar saccades to thus filter for the most relevant gaze paths. We demonstrate and discuss the strengths and weaknesses of both approaches by examples of free-viewing paintings and compare them to other state-of-the-art visualization techniques.

Original languageEnglish
Title of host publicationComputer Vision - ECCV 2016 Workshops, Proceedings
EditorsGang Hua, Hervé Jégou
PublisherSpringer Verlag
Pages783-797
Number of pages15
ISBN (Print)9783319466033
DOIs
StatePublished - 2016
Externally publishedYes
EventComputer Vision - ECCV 2016 Workshops, Proceedings - Amsterdam, Netherlands
Duration: 8 Oct 201616 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9913 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceComputer Vision - ECCV 2016 Workshops, Proceedings
Country/TerritoryNetherlands
CityAmsterdam
Period8/10/1616/10/16

Keywords

  • Eye-tracking
  • Image viewing
  • Perception of art
  • Saccade clustering
  • Scanpath

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