poster

Proposing a classification model for perceptual target selection on large displays

Authors:

Seungjae Oh,

Heejin Kim,

Hyo-Jeong SoAuthors Info & Claims

SUI '14: Proceedings of the 2nd ACM symposium on Spatial user interaction

Page 151

https://doi.org/10.1145/2659766.2661216

Published: 04 October 2014 Publication History

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Abstract

In this research, we propose a linear SVM classification model for perceptual distal target selection on large displays. The model is based on two simple features of users' finger movements reflecting users' visual perception of targets. The model shows the accuracy of 92.78% for predicting an intended target at end point.

References

[1]

Jota, R., Nacenta, M.A., Jorge, J.A., Carpendale, S., and Greenberg, S. A comparison of ray pointing techniques for very large displays. In Proc. Graphic Interface 2010, ACM Press (2010), 269--276.

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[2]

Vogel, D. and Balakrishnan, R. Distant freehand pointing and clicking on very large, high resolution displays. In Proc. UIST 2005, ACM Press (2005), 33--42.

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Index Terms

Proposing a classification model for perceptual target selection on large displays
1. Human-centered computing
  1. Human computer interaction (HCI)

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Information & Contributors

Information

Published In

SUI '14: Proceedings of the 2nd ACM symposium on Spatial user interaction

October 2014

174 pages

ISBN:9781450328203

DOI:10.1145/2659766

General Chair:
Andy Wilson
Microsoft Research, USA
,
Program Chairs:
Frank Steinicke
University of Hamburg, Germany
,
Evan Suma
University of Southern California, USA
,
Wolfgang Stuerzlinger
York University, Canada

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Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 October 2014

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Poster

Funding Sources

Ministry of Science, ICT and Future Planning

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SUI '14

Sponsor:

SUI '14: Symposium on Spatial User Interaction

October 4 - 5, 2014

Hawaii, Honolulu, USA

Acceptance Rates

SUI '14 Paper Acceptance Rate 18 of 62 submissions, 29%;

Overall Acceptance Rate 86 of 279 submissions, 31%

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Index Terms

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