TY - GEN
T1 - Effects of depth cues on the recognition of the spatial position of a 3D object in transparent stereoscopic visualization
AU - Kitaura, Yurina
AU - Hasegawa, Kyoko
AU - Sakano, Yuichi
AU - Lopez-Gulliver, Roberto
AU - Li, Liang
AU - Ando, Hiroshi
AU - Tanaka, Satoshi
N1 - Funding Information:
This work was supported in part by JSPS KAKENHI Grant Number 16H02826 and MEXT-Supported Program for the Strategic Research Foundation at Private Universities (2013–2017).
Publisher Copyright:
© Springer International Publishing AG 2018.
PY - 2018
Y1 - 2018
N2 - Medical applications, as well as many other scientific fields, frequently utilize transparent viewing to investigate the inner 3D structures of complex objects. On the other hand, it is known that stereoscopic vision is effective in allowing us to intuitively understand 3D shapes and to realize natural depth feel of visualized scenes. It is expected that the combination of these two visualization techniques, that is, transparent viewing and the stereoscopic vision, namely transparent stereoscopic visualization, should be effective for our easier and intuitive understanding of inner structures of 3D objects. However, the cognitive effects that arise when combining these two techniques have not been fully understood for us until now. In this paper, we investigate the cognitive effects that arise when combining these two techniques of computer visualization. We specially focus on medical volume visualization to investigate influences of the luminance gradient, which is inherent in the stochastic point-based rendering (SPBR) that we proposed recently. We conducted psychophysical experiments in which observers analysed the perceived 3D structure based on transparent stereoscopic visualization. The experiments are executed under the conditions of monocular, binocular viewing and motion parallax. We found that the luminance gradient is effective in the perceived depth magnitude in the transparent stereoscopic viewing of medical volume data.
AB - Medical applications, as well as many other scientific fields, frequently utilize transparent viewing to investigate the inner 3D structures of complex objects. On the other hand, it is known that stereoscopic vision is effective in allowing us to intuitively understand 3D shapes and to realize natural depth feel of visualized scenes. It is expected that the combination of these two visualization techniques, that is, transparent viewing and the stereoscopic vision, namely transparent stereoscopic visualization, should be effective for our easier and intuitive understanding of inner structures of 3D objects. However, the cognitive effects that arise when combining these two techniques have not been fully understood for us until now. In this paper, we investigate the cognitive effects that arise when combining these two techniques of computer visualization. We specially focus on medical volume visualization to investigate influences of the luminance gradient, which is inherent in the stochastic point-based rendering (SPBR) that we proposed recently. We conducted psychophysical experiments in which observers analysed the perceived 3D structure based on transparent stereoscopic visualization. The experiments are executed under the conditions of monocular, binocular viewing and motion parallax. We found that the luminance gradient is effective in the perceived depth magnitude in the transparent stereoscopic viewing of medical volume data.
KW - Automultiscopic 3D image
KW - Depth perception
KW - Transparent visualization
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U2 - 10.1007/978-3-319-59397-5_30
DO - 10.1007/978-3-319-59397-5_30
M3 - Conference contribution
AN - SCOPUS:85019726472
SN - 9783319593968
T3 - Smart Innovation, Systems and Technologies
SP - 277
EP - 282
BT - Innovation in Medicine and Healthcare 2017 - Proceedings of the 5th KES International Conference on Innovation in Medicine and Healthcare, KES-InMed 2017
A2 - Howlett, Robert J.
A2 - Jain, Lakhmi C.
A2 - Chen, Yen-Wei
A2 - Tanaka, Satoshi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th KES International Conference on Innovation in Medicine and Healthcare, InMed-17 2017
Y2 - 21 June 2017 through 23 June 2017
ER -