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Volume composition and evaluation using eye-tracking data

Authors:

Ross Maciejewski,

David S. EbertAuthors Info & Claims

ACM Transactions on Applied Perception (TAP), Volume 7, Issue 1

Article No.: 4, Pages 1 - 20

https://doi.org/10.1145/1658349.1658353

Published: 18 January 2010 Publication History

Abstract

This article presents a method for automating rendering parameter selection to simplify tedious user interaction and improve the usability of visualization systems. Our approach acquires the important/interesting regions of a dataset through simple user interaction with an eye tracker. Based on this importance information, we automatically compute reasonable rendering parameters using a set of heuristic rules, which are adapted from visualization experience and psychophysical experiments. A user study has been conducted to evaluate these rendering parameters, and while the parameter selections for a specific visualization result are subjective, our approach provides good preliminary results for general users while allowing additional control adjustment. Furthermore, our system improves the interactivity of a visualization system by significantly reducing the required amount of parameter selections and providing good initial rendering parameters for newly acquired datasets of similar types.

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Cited By

Rae IZhou FBilsing MBunge P(2022)Understanding Visual Investigation Patterns Through Digital “Field” ObservationsProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517445(1-16)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517445
Ma BJain EEntezari A(2017)3D Saliency from Eye Tracking with TomographyEye Tracking and Visualization10.1007/978-3-319-47024-5_11(185-198)Online publication date: 4-Feb-2017
https://doi.org/10.1007/978-3-319-47024-5_11
Preim BBaer ACunningham DIsenberg TRopinski T(2016)A Survey of Perceptually Motivated 3D Visualization of Medical Image DataComputer Graphics Forum10.5555/3071534.307158735:3(501-525)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.5555/3071534.3071587
Show More Cited By

Index Terms

Volume composition and evaluation using eye-tracking data
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
    2. Image manipulation
      1. Texturing
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Graphics input devices

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Published In

cover image ACM Transactions on Applied Perception

ACM Transactions on Applied Perception Volume 7, Issue 1

January 2010

154 pages

ISSN:1544-3558

EISSN:1544-3965

DOI:10.1145/1658349

Issue’s Table of Contents

Copyright © 2010 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]

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Publication History

Published: 18 January 2010

Accepted: 01 November 2008

Received: 01 August 2008

Revised: 01 June 2008

Published in TAP Volume 7, Issue 1

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Cited By

Rae IZhou FBilsing MBunge P(2022)Understanding Visual Investigation Patterns Through Digital “Field” ObservationsProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517445(1-16)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517445
Ma BJain EEntezari A(2017)3D Saliency from Eye Tracking with TomographyEye Tracking and Visualization10.1007/978-3-319-47024-5_11(185-198)Online publication date: 4-Feb-2017
https://doi.org/10.1007/978-3-319-47024-5_11
Preim BBaer ACunningham DIsenberg TRopinski T(2016)A Survey of Perceptually Motivated 3D Visualization of Medical Image DataComputer Graphics Forum10.5555/3071534.307158735:3(501-525)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.5555/3071534.3071587
Mahfoud ELu AHancock MMarquardt NSchöning JTory M(2016)Gaze-directed Immersive Visualization of Scientific EnsemblesProceedings of the 2016 ACM Companion on Interactive Surfaces and Spaces10.1145/3009939.3009952(77-82)Online publication date: 6-Nov-2016
https://dl.acm.org/doi/10.1145/3009939.3009952
Preim BBaer ACunningham DIsenberg TRopinski T(2016)A Survey of Perceptually Motivated 3D Visualization of Medical Image DataComputer Graphics Forum10.1111/cgf.1292735:3(501-525)Online publication date: 4-Jul-2016
https://doi.org/10.1111/cgf.12927
Brown EOttley AZhao HLin QSouvenir REndert AChang R(2014)Finding Waldo: Learning about Users from their InteractionsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2014.234657520:12(1663-1672)Online publication date: 31-Dec-2014
https://doi.org/10.1109/TVCG.2014.2346575
Gallo LPlacitelli A(2013)High-Fidelity Visualization of Large Medical Datasets on Commodity HardwareISRN Biomedical Engineering10.1155/2013/8929672013(1-9)Online publication date: 2013
https://doi.org/10.1155/2013/892967

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