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Computational Models of Human Visual Attention and Their Implementations: A Survey
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We humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation.
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- IEICE TRANSACTIONS on Information Vol.E96-D No.3 pp.562-578
- Publication Date
- 2013/03/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.562
- Type of Manuscript
- INVITED SURVEY PAPER
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Akisato KIMURA, Ryo YONETANI, Takatsugu HIRAYAMA, "Computational Models of Human Visual Attention and Their Implementations: A Survey" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 3, pp. 562-578, March 2013, doi: 10.1587/transinf.E96.D.562.
Abstract: We humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.562/_p
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@ARTICLE{e96-d_3_562,
author={Akisato KIMURA, Ryo YONETANI, Takatsugu HIRAYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Computational Models of Human Visual Attention and Their Implementations: A Survey},
year={2013},
volume={E96-D},
number={3},
pages={562-578},
abstract={We humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation.},
keywords={},
doi={10.1587/transinf.E96.D.562},
ISSN={1745-1361},
month={March},}
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TY - JOUR
TI - Computational Models of Human Visual Attention and Their Implementations: A Survey
T2 - IEICE TRANSACTIONS on Information
SP - 562
EP - 578
AU - Akisato KIMURA
AU - Ryo YONETANI
AU - Takatsugu HIRAYAMA
PY - 2013
DO - 10.1587/transinf.E96.D.562
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2013
AB - We humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation.
ER -
English
