Space-Variant Spatio-Temporal Filtering of Video for Gaze Visualization and Perceptual Learning

Michael Dorr; Halszka Jarodzka; Erhardt Barth

doi:10.1145/1743666.1743737

Space-Variant Spatio-Temporal Filtering of Video for Gaze Visualization and Perceptual Learning

Michael Dorr, Halszka Jarodzka, Erhardt Barth

Institute of Neuro- and Bioinformatics

10 Citations (Scopus)

Abstract

We introduce an algorithm for space-variant filtering of video based on a spatio-temporal Laplacian pyramid and use this algorithm to render videos in order to visualize prerecorded eye movements. Spatio-temporal contrast and colour saturation are reduced as a function of distance to the nearest gaze point of regard, i.e. non-fixated, distracting regions are filtered out, whereas fixated image regions remain unchanged. Results of an experiment in which the eye movements of an expert on instructional videos are visualized with this algorithm, so that the gaze of novices is guided to relevant image locations, show that this visualization technique facilitates the novices' perceptual learning.

Original language	English
Title of host publication	ETRA '10 Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Number of pages	8
Place of Publication	New York, NY, USA
Publisher	ACM
Publication date	22.03.2010
Pages	307-314
ISBN (Electronic)	978-1-60558-994-7
DOIs	https://doi.org/10.1145/1743666.1743737
Publication status	Published - 22.03.2010
Event	Eye Tracking Research and Applications - Austin, United States Duration: 22.03.2010 → 24.03.2010 http://www.sis.uta.fi/cs/etra-2010/

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SDG 9 Industry, Innovation, and Infrastructure

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title = "Space-Variant Spatio-Temporal Filtering of Video for Gaze Visualization and Perceptual Learning",

abstract = "We introduce an algorithm for space-variant filtering of video based on a spatio-temporal Laplacian pyramid and use this algorithm to render videos in order to visualize prerecorded eye movements. Spatio-temporal contrast and colour saturation are reduced as a function of distance to the nearest gaze point of regard, i.e. non-fixated, distracting regions are filtered out, whereas fixated image regions remain unchanged. Results of an experiment in which the eye movements of an expert on instructional videos are visualized with this algorithm, so that the gaze of novices is guided to relevant image locations, show that this visualization technique facilitates the novices' perceptual learning.",

author = "Michael Dorr and Halszka Jarodzka and Erhardt Barth",

note = "pyright ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Eye tracking research applications 2010, Austin, Texas, 22-24 March 2010. http://doi.acm.org/10.1145/1743666.1743737; Eye Tracking Research and Applications, ETRA 2010 ; Conference date: 22-03-2010 Through 24-03-2010",

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Dorr, M, Jarodzka, H & Barth, E 2010, Space-Variant Spatio-Temporal Filtering of Video for Gaze Visualization and Perceptual Learning. in ETRA '10 Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications. Eye Tracking Research and Applications, ACM, New York, NY, USA, pp. 307-314, Eye Tracking Research and Applications, Austin, Texas, United States, 22.03.10. https://doi.org/10.1145/1743666.1743737

Space-Variant Spatio-Temporal Filtering of Video for Gaze Visualization and Perceptual Learning. / Dorr, Michael; Jarodzka, Halszka; Barth, Erhardt.
ETRA '10 Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications. New York, NY, USA: ACM, 2010. p. 307-314 (Eye Tracking Research and Applications).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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T1 - Space-Variant Spatio-Temporal Filtering of Video for Gaze Visualization and Perceptual Learning

AU - Dorr, Michael

AU - Jarodzka, Halszka

AU - Barth, Erhardt

N1 - pyright ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Eye tracking research applications 2010, Austin, Texas, 22-24 March 2010. http://doi.acm.org/10.1145/1743666.1743737

PY - 2010/3/22

Y1 - 2010/3/22

N2 - We introduce an algorithm for space-variant filtering of video based on a spatio-temporal Laplacian pyramid and use this algorithm to render videos in order to visualize prerecorded eye movements. Spatio-temporal contrast and colour saturation are reduced as a function of distance to the nearest gaze point of regard, i.e. non-fixated, distracting regions are filtered out, whereas fixated image regions remain unchanged. Results of an experiment in which the eye movements of an expert on instructional videos are visualized with this algorithm, so that the gaze of novices is guided to relevant image locations, show that this visualization technique facilitates the novices' perceptual learning.

AB - We introduce an algorithm for space-variant filtering of video based on a spatio-temporal Laplacian pyramid and use this algorithm to render videos in order to visualize prerecorded eye movements. Spatio-temporal contrast and colour saturation are reduced as a function of distance to the nearest gaze point of regard, i.e. non-fixated, distracting regions are filtered out, whereas fixated image regions remain unchanged. Results of an experiment in which the eye movements of an expert on instructional videos are visualized with this algorithm, so that the gaze of novices is guided to relevant image locations, show that this visualization technique facilitates the novices' perceptual learning.

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DO - 10.1145/1743666.1743737

M3 - Conference contribution

T3 - Eye Tracking Research and Applications

SP - 307

EP - 314

BT - ETRA '10 Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications

PB - ACM

CY - New York, NY, USA

T2 - Eye Tracking Research and Applications

Y2 - 22 March 2010 through 24 March 2010

ER -

Space-Variant Spatio-Temporal Filtering of Video for Gaze Visualization and Perceptual Learning

Abstract

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