A depth cue method based on blurring effect in augmented reality

ACM International Conference Proceeding Series

Volumn , Issue , 2013, Pages 81-88

  Lin, Xueting ^a   Ogawa, Takefumi ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

Augmented reality; Blur; Depth cue; Point spread function; Thin lens model

Indexed keywords

BLUR; DEPTH CUE; ESTIMATION ALGORITHM; INTRINSIC PARAMETERS; MEASUREMENT PROCEDURES; POINT SPREAD FUNCTION PARAMETERS; SPATIAL INFORMATIONS; THIN LENS;

ALGORITHMS; CAMERAS; ESTIMATION; OPTICAL TRANSFER FUNCTION;

AUGMENTED REALITY;

EID: 84875990889     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2459236.2459251     Document Type: Conference Paper

References (15)

1. Kruijff, E., Swan, J. E. and Feiner S. Perceptual issues in Augmented Reality revisited. Proc. ISMAR 2010.
2. Dey, A., Cunningham, A. and Sandor, C. Evaluating depth perception of photorealistic mixed reality visualizations for occluded objects in outdoor environments. Proc. VRST 2010, ACM Press (2010), 211-218.
3. Mikkola, M., Boev, A. and Gotchev, A. Relative importance of depth cues on portable autostereoscopic display. Proc. MoViD 2010, ACM Press (2010), 63-68.
4. Surdick, R., Davis, T. and Elizabeth, T. Perception of distance in simulated visual displays: a comparison of the effectiveness and accuracy of multiple depth cues across viewing distances. J., Teleoperators & Virtual Environments. 6 (1997), 513-532.
5. Fischer, J., Cunningham, D., Bartz, D., Wallraven, C., Bülthoff, H. and Straßer, W. Measuring the discernability of virtual objects in conventional and stylized Augmented Reality. Proc. EGVE 2006.
6. Haller, M., Landerl, F., and Billinghurst, M. A loose and sketchy approach in a mediated reality environment. Proc. GRAPHITE 2005, ACM Press (2005), 371-379.
7. Okumura, B., Kanbara, M., and Yokoya, N. Augmented Reality based on estimation of defocusing and motion blurring from captured images. Proc. ISMAR 2006, 219-225.
8. Okumura, B., Kanbara, M., and Yokoya, N. Image composition based on blur estimation from captured image for Augmented Reality. Proc. VR 2006, 18.
9. Shan, Q., Jia, J., and Agarwala, A. High-quality motion deblurring from a single image. Proc. SIGGRAPH 2008, ACM Press (2008), 73-83.
10. Cho, T.S., Paris, S., Horn, B. K. P., and Freeman, W. T. Blur kernel estimation using the radon transform. Proc. CVPR 2011, 241-248.
11. Joshi, N., Szeliski, R., and Kriegman, D.J. PSF estimation using sharp edge prediction. Proc. CVPR 2008, 1-8.
12. Pentland, A. P. 1985. A new sense for depth of field. Proc. IJCAI 85, Aravind Joshi (Ed.), Vol. 2, 988-994.
13. Mühlich, M., Bildverarbeitung, L., and Aach, T. High accuracy feature detection for camera calibration: a multi-steerable approach. Proc. The 29th DAGM, 284-293.
14. Sun, W., Yang, X., Xiao, S., and Hu, W. Robust checkerboard recognition for efficient nonplanar geometry registration in projector-camera systems. Proc. PROCAMS 2008.
15. Kim, S. K., Park, S. R., and Paik, J. K. Simultaneous out-of-focus blur estimation and restoration for digital auto-focusing system. IEEE Trans. on Consum. Electron. 44, 3 (1998), 1071-1075.