Abstract

Structured-light illumination is a process of three-dimensional imaging where a series of time-multiplexed, striped patterns are projected onto a target scene with the corresponding captured images used to determine surface shape according to the warping of the projected patterns around the target. In a real-time system, a high-speed projector/camera pair is used such that any surface motion is small over the projected pattern sequence, but regardless of acquisition speed, there are always those pixels near the edge of a moving surface that capture the projected patterns on both fore- and background surfaces. These edge pixels then create unpredictable results that typically require expensive processing steps to remove, but in this Letter, we introduce a filtering process that identifies motion artifacts based upon the discrete Fourier transform applied to the time axis of the captured pattern sequence. The process is of very low computational complexity, and in this Letter, we demonstrate that in a real-time structured-light illumination (SLI) system, the process comes at a cost of 15 frames per second (fps), where our SLI system drops from 180 to 165fps after deleting those edge pixels where motion was detected.

© 2010 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. M. Takeda, Q. Gu, M. Kinoshita, H. Takai, and Y. Takahashi, Appl. Opt. 36, 5347 (1997).
    [CrossRef] [PubMed]
  2. S. Y. Chen, Y. F. Li, and J. Zhang, IEEE Trans. Image Process. 17, 167 (2008).
    [CrossRef]
  3. S. Zhang and S.-T. Yau, Opt. Eng. 46, 113603 (2007).
    [CrossRef]
  4. K. Liu, Y. Wang, D. L. Lau, Q. Hao, and L. G. Hassebrook, Opt. Express 18, 5229 (2010).
    [CrossRef] [PubMed]
  5. S. B. Gokturk, H. Yalcin, and C. Bamji, in CVPRW ’04: Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (2004), Vol. 3, pp. 35–43.
  6. X. Su, G. von Bally, and D. Vukicevic, Opt. Commun. 98, 141 (1993).
    [CrossRef]

2010

2008

S. Y. Chen, Y. F. Li, and J. Zhang, IEEE Trans. Image Process. 17, 167 (2008).
[CrossRef]

2007

S. Zhang and S.-T. Yau, Opt. Eng. 46, 113603 (2007).
[CrossRef]

1997

1993

X. Su, G. von Bally, and D. Vukicevic, Opt. Commun. 98, 141 (1993).
[CrossRef]

Bamji, C.

S. B. Gokturk, H. Yalcin, and C. Bamji, in CVPRW ’04: Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (2004), Vol. 3, pp. 35–43.

Chen, S. Y.

S. Y. Chen, Y. F. Li, and J. Zhang, IEEE Trans. Image Process. 17, 167 (2008).
[CrossRef]

Gokturk, S. B.

S. B. Gokturk, H. Yalcin, and C. Bamji, in CVPRW ’04: Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (2004), Vol. 3, pp. 35–43.

Gu, Q.

Hao, Q.

Hassebrook, L. G.

Kinoshita, M.

Lau, D. L.

Li, Y. F.

S. Y. Chen, Y. F. Li, and J. Zhang, IEEE Trans. Image Process. 17, 167 (2008).
[CrossRef]

Liu, K.

Su, X.

X. Su, G. von Bally, and D. Vukicevic, Opt. Commun. 98, 141 (1993).
[CrossRef]

Takahashi, Y.

Takai, H.

Takeda, M.

von Bally, G.

X. Su, G. von Bally, and D. Vukicevic, Opt. Commun. 98, 141 (1993).
[CrossRef]

Vukicevic, D.

X. Su, G. von Bally, and D. Vukicevic, Opt. Commun. 98, 141 (1993).
[CrossRef]

Wang, Y.

Yalcin, H.

S. B. Gokturk, H. Yalcin, and C. Bamji, in CVPRW ’04: Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (2004), Vol. 3, pp. 35–43.

Yau, S.-T.

S. Zhang and S.-T. Yau, Opt. Eng. 46, 113603 (2007).
[CrossRef]

Zhang, J.

S. Y. Chen, Y. F. Li, and J. Zhang, IEEE Trans. Image Process. 17, 167 (2008).
[CrossRef]

Zhang, S.

S. Zhang and S.-T. Yau, Opt. Eng. 46, 113603 (2007).
[CrossRef]

Appl. Opt.

IEEE Trans. Image Process.

S. Y. Chen, Y. F. Li, and J. Zhang, IEEE Trans. Image Process. 17, 167 (2008).
[CrossRef]

Opt. Commun.

X. Su, G. von Bally, and D. Vukicevic, Opt. Commun. 98, 141 (1993).
[CrossRef]

Opt. Eng.

S. Zhang and S.-T. Yau, Opt. Eng. 46, 113603 (2007).
[CrossRef]

Opt. Express

Other

S. B. Gokturk, H. Yalcin, and C. Bamji, in CVPRW ’04: Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (2004), Vol. 3, pp. 35–43.

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (2)

Fig. 1
Fig. 1

Calibration target (left) and corresponding first harmonic magnitude image (right).

Fig. 2
Fig. 2

3-D point cloud reconstruction without (left) and with (right) motion detection.

Equations (10)

Equations on this page are rendered with MathJax. Learn more.

I n p ( x p , y p ) = 1 2 + 1 2 cos ( 2 π ( n N y p ) ) ,
I n c ( x c , y c ) = A c + B c cos ( 2 π n N θ ) ,
A c = 1 N n = 0 N 1 I n c ( x c , y c ) .
B R c = n = 0 N 1 I n c ( x c , y c ) cos ( 2 π n N )
B I c = n = 0 N 1 I n c ( x c , y c ) sin ( 2 π n N )
B c = B R c + j B I c = { B R c 2 + B I c 2 } 1 2 ,
θ = ( B R c + j B I c ) = arctan { B I c B R c } ,
A c = 1 N X [ 0 ] ,
B c = 2 N X [ 1 ] = 2 N X [ N 1 ]
θ = X [ 1 ] = X [ N 1 ] .

Metrics