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On creating a 2D & 3D visual saliency dataset

Published: 22 August 2013 Publication History

Abstract

Visual saliency (VS), which refers to the study of the behaviour or perception of the human vision system, is usually captured using eye tracking technologies on a (statistically) representative set of participants watching visual media on a screen. Using eye tracking technologies to capture the visual behaviour of a set of candidates is a long, expensive and tedious experiment to set. Artificial intelligence can be used to replicate this human behaviour and several 2D and 3D visual saliency algorithms (VSAs) have been proposed [Wang et al. 2013; Zdziarski and Dahyot 2013]. These algorithms attempt to reproduce human visual perception behaviour and have assisted in applications such as video content creation, retargetting and summarisation. Automatically replicating viewing behaviour is easier, less cumbersome and cheaper than the manual option presented by eye trackers.

References

[1]
Hakkinen, J., Kawai, T., Takatalo, J., Mitsuya, R., and Nyman, G. 2010. What do people look at when they watch stereoscopic movies? SPIE Conf. Stereoscopic Displays and Applications 7524.
[2]
Huynh-Thu, Q., and Schiatti, L. 2011. Examination of 3d visual attention in stereoscopic video content. IS&T/SPIE Electronic Imaging 7865.
[3]
Wang, J., Silva, M. P. D., Callet, P. L., and Ricordel, V. 2013. A computational model of stereoscopic 3d visual saliency. IEEE Trans. on Image Processing 22, 6, 2151--2161.
[4]
Zdziarski, Z., and Dahyot, R. 2013. Depth information in visual saliency algorithms. submitted.
  1. On creating a 2D & 3D visual saliency dataset

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    cover image ACM Conferences
    SAP '13: Proceedings of the ACM Symposium on Applied Perception
    August 2013
    150 pages
    ISBN:9781450322621
    DOI:10.1145/2492494
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    Published: 22 August 2013

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    SAP' 13: ACM Symposium on Applied Perception 2013
    August 22 - 23, 2013
    Dublin, Ireland

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    SAP '13 Paper Acceptance Rate 22 of 54 submissions, 41%;
    Overall Acceptance Rate 22 of 54 submissions, 41%

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