Novel Methods for Analysis and Visualization of Saccade Trajectories

详细信息    查看全文

• 关键词：Eye ; tracking ; Image viewing ; Perception of art ; Scanpath ; Saccade clustering
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9913
• 期：1
• 页码：783-797
• 全文大小：4,843 KB
• 参考文献：1.Arnheim, R.: Art and Visual Perception: A Psychology of the Creative Eye. University of California Press, Berkeley (1954)
  2.Badt, K., Dittmann, L., van Delft, J.V.: Modell und Maler von Jan Vermeer: Probleme der Interpretation: eine Streitschrift gegen Hans Sedlmayr. DuMont (1997)
  3.Blascheck, T., Kurzhals, K., Raschke, M., Burch, M., Weiskopf, D., Ertl, T.: State-of-the-art of visualization for eye tracking data. In: EuroVis, vol. 2014 (2014)
  4.Bojko, A.A.: Informative or misleading? heatmaps deconstructed. In: Jacko, J.A. (ed.) HCI International 2009, Part I. LNCS, vol. 5610, pp. 30–39. Springer, Heidelberg (2009)
  5.Burch, M., Schmauder, H., Raschke, M., Weiskopf, D.: Saccade plots. In: ETRA, pp. 307–310. ACM (2014)
  6.Condorovici, R.G., Vrânceanu, R., Vertan, C.: Saliency map retrieval for artistic paintings inspired from human understanding. In: SPAMEC (2011)
  7.Dong, W., Liao, H., Roth, R.E., Wang, S.: Eye tracking to explore the potential of enhanced imagery basemaps in web mapping. Cartographic J. 51(4), 313–329 (2014)CrossRef
  8.Ellis, S.R., Smith, J.D.: Patterns of Statistical Dependency in Visual Scanning, pp. 221–238. Elsevier Science Publishers BV, Amsterdam (1985)
  9.Engelbrecht, M., Betz, J., Klein, C., Rosenberg, R.: Dem auge auf der spur: Eine historische und empirische studie zur blickbewegung beim betrachten von gemälden. IMAGE-Zeitschrift für interdisziplinäre Bildwissenschaft 11 (2010)
  10.Härdle, W.K., Simar, L.: Applied Multivariate Statistical Analysis, vol. 22007. Springer, Heidelberg (2007)MATH
  11.Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., Van de Weijer, J.: Eye Tracking: A Comprehensive Guide to Methods and Measures. Oxford University Press, Oxford (2011)
  12.Icoglu, O., Gunsel, B., Sariel, S.: Classification and indexing of paintings based on art movements. In: 2004 12th European Signal Processing Conference, pp. 749–752. IEEE (2004)
  13.Kasneci, E., Kasneci, G., Kübler, T.C., Rosenstiel, W.: The applicability of probabilistic methods to the online recognition of fixations and saccades in dynamic scenes. In: ETRA, pp. 323–326. ACM (2014)
  14.Kübler, T.C., Kasneci, E., Rosenstiel, W.: Subsmatch: scanpath similarity in dynamic scenes based on subsequence frequencies. In: ETRA, ACM (2014)
  15.Kübler, T.C., Sippel, K., Fuhl, W., Schievelbein, G., Aufreiter, J., Rosenberg, R., Rosenstiel, W., Kasneci, E.: Analysis of eye movements with eyetrace. In: Fred, A., Gamboa, H., Elias, D. (eds.) BIOSTEC 2015. CCIS, vol. 574, pp. 458–471. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-27707-3_​28 CrossRef
  16.Leonards, U., Baddeley, R., Gilchrist, I.D., Troscianko, T., Ledda, P., Williamson, B.: Mediaeval artists: masters in directing the observers’ gaze. Curr. Biol. 17(1), R8–R9 (2007)CrossRef
  17.Ouerhani, N., Bracamonte, J., Hugli, H., Ansorge, M., Pellandini, F.: Adaptive color image compression based on visual attention. In: ICIAP, IEEE (2001)
  18.Peysakhovich, V., Hurter, C., Telea, A.: Attribute-driven edge bundling for general graphs with applications in trail analysis. In: PacificVis, pp. 39–46. IEEE (2015)
  19.Popelka, S., Voženílek, V.: Specifying of requirements for spatio-temporal data in map by eye-tracking and space-time-cube. In: ICGIP, p. 87684N. International Society for Optics and Photonics (2013)
  20.Privitera, C.M., Stark, L.W.: Algorithms for defining visual regions-of-interest: comparison with eye fixations. PAMI 22(9), 970–982 (2000)CrossRef
  21.Rosenberg, R.: Blicke messen: Vorschläge für eine empirische bildwissenschaft. Jahrbuch der Bayerischen Akademie der Schönen Künste 27, 71–86 (2014)
  22.Rosenberg, R., Klein, C.: The moving eye of the beholder: Eye tracking and the perception of paintings (2015)
  23.Tafaj, E., Kasneci, G., Rosenstiel, W., Bogdan, M.: Bayesian online clustering of eye movement data. In: ETRA 2012, pp. 285–288. ACM (2012)
  24.Tafaj, E., Kübler, T.C., Kasneci, G., Rosenstiel, W., Bogdan, M.: Online classification of eye tracking data for automated analysis of traffic hazard perception. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds.) ICANN 2013. LNCS, vol. 8131, pp. 442–450. Springer, Heidelberg (2013)CrossRef
  25.Tatler, B.W., Wade, N.J., Kwan, H., Findlay, J.M., Velichkovsky, B.M.: Yarbus, eye movements, and vision. i-Perception 1(1), 7–27 (2010)CrossRef
  26.van der Zwan, M., Codreanu, V., Telea, A.: CUBu: universal real-time bundling for large graphs. IEEE Trans. Vis. Comput. Graph. 38(1), 30–45 (2016)
  
• 作者单位：Thomas Kübler (15)
  Wolfgang Fuhl (15)
  Raphael Rosenberg (17)
  Wolfgang Rosenstiel (16)
  Enkelejda Kasneci (15)
  
  15. Perception Engineering, University of Tübingen, Tübingen, Germany
  17. Lab for Cognitive Research in Art History, University of Vienna, Vienna, Austria
  16. Computer Engineering, University of Tübingen, Tübingen, Germany
  
• 丛书名：Computer Vision – ECCV 2016 Workshops
• ISBN：978-3-319-46604-0
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9913

文摘

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.