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Scanpath visualization and comparison using visual aggregation techniques

We demonstrate the use of different visual aggregation techniques to obtain non-cluttered visual representations of scanpaths. First, fixation points are clustered using the mean-shift algorithm. Second, saccades are aggregated using the Attribute-Driven Edge Bundling (ADEB) algorithm that handles a...

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Detalles Bibliográficos
Autores principales: Peysakhovich, Vsevolod, Hurter, Christophe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Bern Open Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7140773/
https://www.ncbi.nlm.nih.gov/pubmed/33828667
http://dx.doi.org/10.16910/jemr.10.5.9
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author Peysakhovich, Vsevolod
Hurter, Christophe
author_facet Peysakhovich, Vsevolod
Hurter, Christophe
author_sort Peysakhovich, Vsevolod
collection PubMed
description We demonstrate the use of different visual aggregation techniques to obtain non-cluttered visual representations of scanpaths. First, fixation points are clustered using the mean-shift algorithm. Second, saccades are aggregated using the Attribute-Driven Edge Bundling (ADEB) algorithm that handles a saccades direction, onset timestamp, magnitude or their combination for the edge compatibility criterion. Flow direction maps, computed during bundling, can be visualized separately (vertical or horizontal components) or as a single image using the Oriented Line Integral Convolution (OLIC) algorithm. Furthermore, cosine similarity between two flow direction maps provides a similarity map to compare two scanpaths. Last, we provide examples of basic patterns, visual search task, and art perception. Used together, these techniques provide valuable insights about scanpath exploration and informative illustrations of the eye movement data.
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spelling pubmed-71407732021-04-06 Scanpath visualization and comparison using visual aggregation techniques Peysakhovich, Vsevolod Hurter, Christophe J Eye Mov Res Research Article We demonstrate the use of different visual aggregation techniques to obtain non-cluttered visual representations of scanpaths. First, fixation points are clustered using the mean-shift algorithm. Second, saccades are aggregated using the Attribute-Driven Edge Bundling (ADEB) algorithm that handles a saccades direction, onset timestamp, magnitude or their combination for the edge compatibility criterion. Flow direction maps, computed during bundling, can be visualized separately (vertical or horizontal components) or as a single image using the Oriented Line Integral Convolution (OLIC) algorithm. Furthermore, cosine similarity between two flow direction maps provides a similarity map to compare two scanpaths. Last, we provide examples of basic patterns, visual search task, and art perception. Used together, these techniques provide valuable insights about scanpath exploration and informative illustrations of the eye movement data. Bern Open Publishing 2018-01-08 /pmc/articles/PMC7140773/ /pubmed/33828667 http://dx.doi.org/10.16910/jemr.10.5.9 Text en This work is licensed under a Creative Commons Attribution 4.0 International License, ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Research Article
Peysakhovich, Vsevolod
Hurter, Christophe
Scanpath visualization and comparison using visual aggregation techniques
title Scanpath visualization and comparison using visual aggregation techniques
title_full Scanpath visualization and comparison using visual aggregation techniques
title_fullStr Scanpath visualization and comparison using visual aggregation techniques
title_full_unstemmed Scanpath visualization and comparison using visual aggregation techniques
title_short Scanpath visualization and comparison using visual aggregation techniques
title_sort scanpath visualization and comparison using visual aggregation techniques
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7140773/
https://www.ncbi.nlm.nih.gov/pubmed/33828667
http://dx.doi.org/10.16910/jemr.10.5.9
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