Skip to main content
Log in

An optimized phase-shifting algorithm for depth image acquisition

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

As developing science and technology, traditional two-dimensional computer vision cannot meet the people’s needs for the three-dimensional recognition, and the depth information of objects are required by more and more applications. Recently, structured light has become one of the core techniques of depth acquisition. The main idea of the approach is first projecting pre-designed pattern onto objects, then capturing an image and processing further. In a structured light system, the phase-shifting algorithm, which is a depth acquiring algorithm for sinusoidal pattern, is discussed in this paper, and is argued that its performance weakness for applying in the real time environments. Then we propose the performance optimization on its phase wrapping step and phase unwrapping step, respectively. Finally, we compare acquiring results and advantages as well as disadvantages of them by experiments results. Experiments show that the optimized phase-shifting algorithm is 4.6× faster than the original one with the ignorable errors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Bhandari A, Raskar R (2016) Signal processing for time-of-flight imaging sensors: an introduction to inverse problems in computational 3-D imaging. IEEE Signal Process Mag 33(5):45–58

    Article  Google Scholar 

  2. Charles J, Everingham M (2011) Learning shape models for monocular human pose estimation from the Microsoft Xbox Kinect. In: Computer vision workshops (ICCV Workshops), 2011 I.E. international conference on (pp 1202–1208). IEEE

  3. Cong P, Xiong Z, Zhang Y, Zhao S, Wu F (2015) Accurate dynamic 3D sensing with Fourier-assisted phase shifting. IEEE J Se Top Sign Process 9(3):396–408

    Article  Google Scholar 

  4. Huang PS, Zhang S (2006) Fast three-step phase shifting algorithm. Appl Opt 45(21):5086–5091

    Article  Google Scholar 

  5. Huang PS, Zhang S, Chiang FP (2004) Trapezoidal phase-shifting method for 3D shape measurement. In: Proc. of SPIE (Vol. 5606, p 143)

  6. Koninckx TP, Peers P, Dutré P, van Gool L (2005) Scene-adapted structured light. In: Computer vision and pattern recognition, 2005. CVPR 2005. IEEE computer society conference on (Vol. 2, pp 611–618). IEEE

  7. Susanto W, Rohrbach M, Schiele B (2012) 3D object detection with multiple kinects. In: Computer vision–ECCV 2012. Workshops and demonstrations (pp 93–102). Springer Berlin/Heidelberg

    Chapter  Google Scholar 

  8. Wang S (2014) An improved quality guided phase unwrapping method and its applications to MRI. Prog Electromagn Res-PIER 145:273–286

    Article  Google Scholar 

  9. Xia L, Chen CC, Aggarwal JK (2011) Human detection using depth information by kinect. In: Computer vision and pattern recognition workshops (CVPRW), 2011 I.E. computer society conference on (pp 15–22). IEEE

  10. Yang Z, Xiong Z, Zhang Y, Wang J., Wu F (2013) Depth acquisition from density modulated binary patterns. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp 25–32)

  11. Yang L, Li F, Xiong Z, Shi G, Niu Y, Li R (2017) Single-shot dense depth sensing with frequency-division multiplexing fringe projection. J Vis Commun Image Represent 46:139–149

    Article  Google Scholar 

  12. Zhang S (2010) High-resolution, high-speed 3-d dynamically deformable shape measurement using digital fringe projection techniques. In: Advances in measurement systems, Milind Kr Sharma (Ed.), InTech. http://www.intechopen.com/books/advances-in-measurementsystems/high-resolution-high-speed-3-d-dynamically-deformable-shape-measurement-using-digital-fringeproject

    Google Scholar 

  13. Zhang Z (2012) Microsoft kinect sensor and its effect. IEEE Multimed 19(2):4–10

    Article  Google Scholar 

  14. Zhang Y, Xiong Z, Yang Z, Wu F (2014) Real-time scalable depth sensing with hybrid structured light illumination. IEEE Trans Image Process 23(1):97–109

    Article  MathSciNet  Google Scholar 

  15. Zhang Y, Wang S, Ji G, Dong Z (2014) An improved quality guided phase unwrapping method and its applications to MRI. Prog Electromagn Res 145:273–286

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by the National Natural Science Foundation of China (61662057, 61672143, U1435216), the Fundamental Research Funds for the Central Universities (N162504007, N161602003, N151704004), the Doctor Research Starting Foundation of Liaoning (20141011).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jie Song.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, H., Wang, B., Yan, F. et al. An optimized phase-shifting algorithm for depth image acquisition. Multimed Tools Appl 78, 5367–5380 (2019). https://doi.org/10.1007/s11042-018-6007-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6007-4

Keywords

Navigation