skip to main content
10.1145/3205929.3205937acmconferencesArticle/Chapter ViewAbstractPublication PagesetraConference Proceedingsconference-collections
research-article

Region of interest generation algorithms for eye tracking data

Published:15 June 2018Publication History

ABSTRACT

Using human fixation behavior, we can interfere regions that require to be processed at high resolution and where stronger compression can be favored. Analyzing the visual scan path solely based on a predefined set of regions of interest (ROIs) limits the exploration room of the analysis. Insights can only be gained for those regions that the data analyst considered worthy of labeling. Furthermore, visual exploration is naturally time-dependent: A short initial overview phase may be followed by an in-depth analysis of regions that attracted the most attention. Therefore, the shape and size of regions of interest may change over time. Automatic ROI generation can help in automatically reshaping the ROIs to the data of a time slice. We developed three novel methods for automatic ROI generation and show their applicability to different eye tracking data sets. The methods are publicly available as part of the EyeTrace software at http://www.ti.uni-tuebingen.de/Eyetrace.175L0.html

References

  1. Bonnie Auyeung, Michael V Lombardo, Markus Heinrichs, Bhisma Chakrabarti, A Sule, Julia Brynja Deakin, RAI Bethlehem, L Dickens, Natasha Mooney, JAN Sipple, et al. 2015. Oxytocin increases eye contact during a real-time, naturalistic social interaction in males with and without autism. Translational psychiatry 5, 2 (2015), e507.Google ScholarGoogle Scholar
  2. T Blascheck, K Kurzhals, M Raschke, M Burch, D Weiskopf, and T Ertl. 2014. State-of-the-art of visualization for eye tracking data. In Proceedings of EuroVis, Vol. 2014.Google ScholarGoogle Scholar
  3. Tanja Blascheck, Michael Raschke, and Thomas Ertl. 2013. Circular heat map transition diagram. In Proceedings of the 2013 Conference on Eye Tracking South Africa. ACM, 58--61. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. L. Commare and H. Brinkmann. 2016. Aesthetic Echoes in the Beholder's Eye? Empirical evidence for the divergence of theory and practice in the perception of abstract art.. In M. Zimmermann (Ed.): Vision in Motion. Streams of Sensation and Configurations of Time. Diaphanes.Google ScholarGoogle Scholar
  5. Andrew T Duchowski. 2002. A breadth-first survey of eye-tracking applications. Behavior Research Methods, Instruments, & Computers 34, 4 (2002), 455--470.Google ScholarGoogle ScholarCross RefCross Ref
  6. Joseph H Goldberg, Mark J Stimson, Marion Lewenstein, Neil Scott, and Anna M Wichansky. 2002. Eye tracking in web search tasks: design implications. In Proceedings of the 2002 symposium on Eye tracking research & applications. ACM, 51--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Thomas E Hutchinson, K Preston White, Worthy N Martin, Kelly C Reichert, and Lisa A Frey. 1989. Human-computer interaction using eye-gaze input. IEEE Transactions on systems, man, and cybernetics 19, 6 (1989), 1527--1534.Google ScholarGoogle ScholarCross RefCross Ref
  8. Thomas C Kübler, Katrin Sippel, Wolfgang Fuhl, Guilherme Schievelbein, Johanna Aufreiter, Raphael Rosenberg, Wolfgang Rosenstiel, and Enkelejda Kasneci. 2015. Analysis of eye movements with Eyetrace. In Biomedical Engineering Systems and Technologies. Springer, 458--471.Google ScholarGoogle Scholar
  9. Radoslaw Mantiuk, Bartosz Bazyluk, and Rafal K. Mantiuk. 2013. Gaze-driven Object Tracking for Real Time Rendering. Computer Graphics Forum 32, 2 (2013), 163--173.Google ScholarGoogle ScholarCross RefCross Ref
  10. Franco Mawad, Marcela Trías, Ana Giménez, Alejandro Maiche, and Gastón Ares. 2015. Influence of cognitive style on information processing and selection of yogurt labels: Insights from an eye-tracking study. Food Research International 74 (2015), 1--9.Google ScholarGoogle ScholarCross RefCross Ref
  11. Marcus Nyström. 2008. Off-line Foveated Compression and Scene Perception: An Eye-Tracking Approach. Ph.D. Dissertation. Lund University.Google ScholarGoogle Scholar
  12. Alice Oh, Harold Fox, Max Van Kleek, Aaron Adler, Krzysztof Gajos, Louis-Philippe Morency, and Trevor Darrell. 2002. Evaluating look-to-talk: a gaze-aware interface in a collaborative environment. In CHI'02 Extended Abstracts on Human Factors in Computing Systems. ACM, 650--651. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Bing Pan, Helene A Hembrooke, Geri K Gay, Laura A Granka, Matthew K Feusner, and Jill K Newman. 2004. The determinants of web page viewing behavior: an eye-tracking study. In Proceedings of the 2004 symposium on Eye tracking research & applications. ACM, 147--154. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Claudio M Privitera and Lawrence W Stark. 1998. Evaluating image processing algorithms that predict regions of interest. Pattern Recognition Letters 19, 11 (1998), 1037--1043. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Claudio M Privitera and Lawrence W Stark. 2000. Algorithms for defining visual regions-of-interest: Comparison with eye fixations. Pattern Analysis and Machine Intelligence, IEEE Transactions on 22, 9 (2000), 970--982. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. public domain. 1910. Wassily Kandinski first abstract watercolor. https://en.wikipedia.org/wiki/File:First_abstract_watercolor_kandinsky_1910.jpg. First abstract water-color, painted by Wassily Kandinsky, 1910.Google ScholarGoogle Scholar
  17. Raphael Rosenberg. 2014. Blicke messen: Vorschläge für eine empirische bildwissenschaft. Jahrbuch der Bayerischen Akademie der Schönen Künste 27 (2014), 71--86.Google ScholarGoogle Scholar
  18. Anthony Santella and Doug DeCarlo. 2004. Robust clustering of eye movement recordings for quantification of visual interest. In Proceedings of the 2004 symposium on Eye tracking research & applications. ACM, 27--34. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Benjamin Strobel, Marlit Annalena Lindner, Steffani Saß, and Olaf Köller. 2018. Task-irrelevant data impair processing of graph reading tasks: An eye tracking study. Learning and Instruction (2018).Google ScholarGoogle Scholar
  20. E. Tafaj, G. Kasneci, W. Rosenstiel, and M. Bogdan. 2012. Bayesian Online Clustering of Eye Movement Data. In Proceedings of the Symposium on ETRA. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Benjamin W Tatler, Nicholas J Wade, Hoi Kwan, John M Findlay, and Boris MGoogle ScholarGoogle Scholar
  22. Velichkovsky. 2010. Yarbus, eye movements, and vision. i-Perception 1, 1 (jan 2010), 7--27.Google ScholarGoogle Scholar
  23. Dereck Toker, Cristina Conati, Ben Steichen, and Giuseppe Carenini. 2013. Individual user characteristics and information visualization: connecting the dots through eye tracking. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 295--304. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. David S Wooding. 2002. Eye movements of large populations: II. Deriving regions of interest, coverage, and similarity using fixation maps. Behavior Research Methods, Instruments, & Computers 34, 4 (2002), 518--528.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Region of interest generation algorithms for eye tracking data

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          ETVIS '18: Proceedings of the 3rd Workshop on Eye Tracking and Visualization
          June 2018
          57 pages
          ISBN:9781450357876
          DOI:10.1145/3205929

          Copyright © 2018 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 15 June 2018

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Upcoming Conference

          ETRA '24
          The 2024 Symposium on Eye Tracking Research and Applications
          June 4 - 7, 2024
          Glasgow , United Kingdom

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader