Space-variant spatio-temporal filtering of video for gaze visualization and perceptual learning

Dörr, Michael; Jarodzka, Halszka; Barth, Erhardt

doi:10.1145/1743666.1743737

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…Classical attention maps are visualized as luminance maps [VH96], 3D landscapes [Lat88, Woo02, HRO11], 2D topographic maps with contour lines [GWP07, DMGB10] or with a colour coding [Boj09, DPMO12]. To emphasize attended regions, alternative visualization techniques use filtering approaches to reduce sharpness and colour saturation [DJB10] in unattended regions, by showing only fixations that were recurrent or deterministic [ABL*13], by showing only first fixations [NH08] or by representing reading speed of participants [BDEB12].…”

Section: Point‐based Visualization Techniquesmentioning

confidence: 99%

Visualization of Eye Tracking Data: A Taxonomy and Survey

Blascheck

Kurzhals

Raschke

et al. 2017

Computer Graphics Forum

214

115

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This survey provides an introduction into eye tracking visualization with an overview of existing techniques. Eye tracking is important for evaluating user behaviour. Analysing eye tracking data is typically done quantitatively, applying statistical methods. However, in recent years, researchers have been increasingly using qualitative and exploratory analysis methods based on visualization techniques. For this state‐of‐the‐art report, we investigated about 110 research papers presenting visualization techniques for eye tracking data. We classified these visualization techniques and identified two main categories: point‐based methods and methods based on areas of interest. Additionally, we conducted an expert review asking leading eye tracking experts how they apply visualization techniques in their analysis of eye tracking data. Based on the experts' feedback, we identified challenges that have to be tackled in the future so that visualizations will become even more widely applied in eye tracking research.

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Section: Point‐based Visualization Techniquesmentioning

confidence: 99%

Visualization of Eye Tracking Data: A Taxonomy and Survey

Blascheck

Kurzhals

Raschke

et al. 2017

Computer Graphics Forum

214

115

View full text Add to dashboard Cite

show abstract

“…Beyond EMME, other approaches are somewhat less closely related but incorporate facets of visual search and involve ei- ther highlighting salient regions of interest with a high-resolution foveated window [2], a variant of the moving window paradigm [14,21], or mask out previously foveated regions to force the viewer to inspect previously unfixated regions [20].…”

Section: Introductionmentioning

confidence: 99%

Gaze-augmented think-aloud as an aid to learning

Vitak

Ingram

Duchowski

et al. 2012

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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The use of recorded eye movements, or scanpaths, has been demonstrated as an effective visualization for feed-forward visual search training, instruction, and stimulated retrospective think-aloud usability testing. In this paper we show that creation of a scripted or recorded video of an expert's thinkaloud session augmented by an animation of their scanpaths can result in an effective aid for learners of visual search. Because the creation of such a video is relatively easy, the benefits-to-cost ratio may potentially be substantial, especially in settings where learned visual scanning strategies are indicators of expertise. We suggest that two such examples are examinations of Chest X-Rays and histological slides. Results are presented where straightforward construction of an instruction video provides measurable benefit to novice as well as experienced learners in the latter context.

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“…In contrast, a dot-like representation of eye movements has also been shown to increase skills related to the interpretation of relevant visual features, but without having a positive effect on visual search (Jarodzka et al 2010b). Reducing existing information by blurring non-focused information instead has been shown to guide attention and to foster visual search, whereas no improvements in interpretation performance could be observed (Dorr et al 2010;Jarodzka et al 2010b). Moreover, blurring videos has been shown to guide the observers' attention, without them even noticing it (Nyström 2008).…”

mentioning

confidence: 99%