Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Farne, M.: Brightness as an indicator to distance: relative brightness per se or contrast with the background? Perception 6(3), 287–293 (1977)
Egusa, H.: Effect of brightness on perceived distance as a figure-ground phenomenon. Perception 11(6), 671–676 (1982)
O’Shea, R.P., Blackburn, S.G., Ono, H.: Contrast as a depth cue. Vision Res. 34(12), 1595–1604 (1994)
Tanaka, S., Hasegawa, K., Shimokubo, Y., Kaneko, T., Kawamura, T., Nakata, S., Ojima, S., Sakamoto, N., Tanaka, H.T., Koyamada, K.: Particle-based transparent rendering of implicit surfaces and its application to fused visualization. In: Euro Vis 2012, pp. 25–29 (2012). (short paper)
Hasegawa, K., Ojima, S., Shimokubo, Y., Nakata, S., Hachimura, K., Tanaka, S.: Particle-based transparent fused visualization applied to medical volume data. Int. J. Model. Simul. Sci. Comput. 4, 11 (2013). 1341003
Tanaka, S., Hasegawa, K., Okamoto, N., Umegaki, R., Wang, S., Uemura, M., Okamoto, A., Koyamada, K.: See-through imaging of laser-scanned 3D cultural heritage objects based on stochastic rendering of large-scale point clouds. In: ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. III-5, pp. 73–80, July 2016
Sakano, Y., Kitaura, Y., Hasegawa, K., Lopez-Gulliver, R., Ando, H., Tanaka, S.: Evaluation of perceived 3D structure of multi-view 3D medical image based on transparent visualization: a psychophysical study. In: Proceedings of the International Display Workshops, vol. 23, pp. 898–901 (2016)
Howard, I.P., Rogers, B.J.: Binocular Vision and Stereopsis. Oxford University Press, New York (1995)
Julesz, B.: Binocular depth Perception of computer-generated patterns. Bell Syst. Tech. J. 39, 1125–1162 (1960)
Rogers, B., Graham, M.: Motion parallax as an independent cue for depth perception. Perception 8, 125–134 (1979)
Acknowledgement
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Kitaura, Y. et al. (2018). Effects of Depth Cues on the Recognition of the Spatial Position of a 3D Object in Transparent Stereoscopic Visualization. In: Chen, YW., Tanaka, S., Howlett, R., Jain, L. (eds) Innovation in Medicine and Healthcare 2017. KES-InMed 2018 2017. Smart Innovation, Systems and Technologies, vol 71. Springer, Cham. https://doi.org/10.1007/978-3-319-59397-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-59397-5_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59396-8
Online ISBN: 978-3-319-59397-5
eBook Packages: EngineeringEngineering (R0)