Skip to main content
SpringerLink
Log in

Modified adaptive support weight and disparity search range estimation schemes for stereo matching processors

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Recently, to obtain three-dimensional depth information from a set of stereo images, stereo matching processors are widely used in intelligent robots, autonomous vehicles, and the Internet of things environment, all of which require real-time processing capability with minimal hardware resources. In this paper, we propose a modified adaptive support weight scheme with rectangular ring-type window configurations that minimize hardware resources while maintaining matching accuracy. In addition, to reduce the computational overhead of window-based local stereo matching algorithms, we present a robust disparity search range estimation scheme based on stretched depth histograms. To evaluate the performance of the proposed schemes, we implemented them using C language and performed experiments. In addition, to show the feasibility of the hardware implementation of the proposed schemes, we also describe them using Verilog hardware description language and implemented them using a field-programmable gate array-based platform. Experimental results show that compared to conventional method, the proposed schemes reduced up to 57% of hardware resources and 33% of computational overhead, respectively.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

References

  1. DeSouza GN, Kak AC (2002) Vision for mobile robot navigation: a survey. IEEE Trans Pattern Anal Mach Intell 24(2):237–267

    Article  Google Scholar 

  2. Bruch MH, Lum J, Yee S, Tran N (2005) Advances in autonomy for small UGVs. Unmanned Ground Veh Technol VII Proc SPIE 5804:532–541

    Article  Google Scholar 

  3. Howard A (2008) Real-time stereo visual odometry for autonomous ground vehicles. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 3946–3952

  4. Bostanci E, Kanwal N, Clark AF (2015) Augmented reality applications for cultural heritage using Kinect. Hum Centric Comput Inf Sci 5(1):1–20

    Article  Google Scholar 

  5. Ho YS (2013) Challenging technical issues of 3D video processing. JoC 4(1):1–6

    Google Scholar 

  6. Liu Z, Yan T (2013) Study on multi-view video based on IOT and its application in intelligent security system. In: Proceedings of the 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), pp 1437–1440

  7. Scharstein D, Szeliski R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int J Comput Vis 47(1):7–42

    Article  Google Scholar 

  8. Nalpantidis L, Georgios CS, Antonios G (2008) Review of stereo vision algorithms: from software to hardware. Int J Optomechatroni 2(4):435–462

    Article  Google Scholar 

  9. Nie DH, Han KP, Lee HS (2009) GPU-based stereo matching algorithm with the strategy of population-based incremental learning. J Inf Process Syst 5(2):105–116

    Article  Google Scholar 

  10. Tombari F, Mattoccia S, Stefano LD, Addimanda E (2008) Classification and evaluation of cost aggregation methods for stereo correspondence. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp 1–8

  11. Ok SH, Bae KR, Lim SK, Moon B (2013) Design and analysis of 3D IC-based low power stereo matching processors. In: International Symposium on Low Power Electronics and Design, pp 15–20

  12. Um GM, Kim SM, Hur N, Lee KH, Lee SI (2006) Depth map-based disparity estimation technique using multiview and depth camera. In: Stereoscopic Displays and Virtual Reality Systems XIII, Proceedings of SPIE 6055, pp 60551E–60551E-11

  13. Shin H, Sohn K (2012) Real-time depth range estimation and its application to mobile stereo camera. In: IEEE Consumer Communications and Networking Conference (CCNC), pp 5–9

  14. Park CO, Heo JH, Lee DH, Cho JD (2012) Dynamic search range using sparse disparity map for fast stereo matching. In: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, pp 1–4

  15. Yoon KJ, Kweon IS (2006) Adaptive support-weight approach for correspondence search. IEEE Trans Pattern Anal Mach Intell 28(4):650–656

    Article  Google Scholar 

  16. Chang NYC, Tsai TH, Hsu BH, Chen YC, Chang TS (2010) Algorithm and architecture of disparity estimation with mini-census adaptive support weight. IEEE Trans Circuits Syst Video Technol 20(6):792–805

    Article  Google Scholar 

  17. Ding J, Liu J, Zhou W, Yu H, Wang Y, Gong X (2011) Real-time stereo vision system using adaptive weight cost aggregation approach. EURASIP J Image Video 1:1–20

    Google Scholar 

  18. Perri S, Corsonello P, Cocorullo G (2013) Adaptive census transform: a novel hardware-oriented stereovision algorithm. Comput Vis Image Underst 117(1):29–41

    Article  Google Scholar 

  19. Zabih R, Woodfill J (1994) Non-parametric local transforms for computing visual correspondence. In: Proceedings of the Third European Conference on Computer Vision, pp 151–158

  20. Heo YS, Lee KM, Lee SU (2011) Robust stereo matching using adaptive normalized cross-correlation. IEEE Trans Pattern Anal Mach Intell 33(4):807–822

    Article  Google Scholar 

  21. Banks J, Corke PI (2001) Quantitative evaluation of matching methods and validity measures for stereo vision. Int J Robot Res 20(7):512–532

    Article  Google Scholar 

  22. Hirschmuller H, Scharstein D (2007) Evaluation of cost functions for stereo matching. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 1–8

  23. Hu X, Mordohai P (2012) A quantitative evaluation of confidence measures for stereo vision. IEEE Trans Pattern Anal Mach Intell 34(11):2121–2133

    Article  Google Scholar 

  24. Zinner C, Humenberger M, Ambrosch K, Kubinger W (2008) An optimized software-based implementation of a census-based stereo matching algorithm. In: Proceedings of the 4th International Symposium on Advances in Visual Computing, pp 216–227

    Chapter  Google Scholar 

  25. Humenberger M, Zinner C, Weber M, Kubinger W, Vincze M (2010) A fast stereo matching algorithm suitable for embedded real-time systems. Comput Vis Image Underst 114(11):1180–1202

    Article  Google Scholar 

  26. Bae KR, Son HS, Hyun J, Moon B (2015) A census-based stereo matching algorithm with multiple sparse windows. In: Seventh International Conference on Ubiquitous and Future Networks, pp 240–245

  27. Wang L, Liao M, Gong M, Yang R, Nister D (2006) High-quality real-time stereo using adaptive cost aggregation and dynamic programming. In: Third International Symposium on 3D Data Processing, Visualization, and Transmission, pp 798–805

  28. Lee Z, Khoshabeh R, Juang J, Nguyen TQ (2012) Local stereo matching using motion cue and modified census in video disparity estimation. In: Proceedings of the 20th European Signal Processing Conference (EUSIPCO), pp 1114–1118

  29. Fife WS, Archibald JK (2013) Improved census transforms for resource-optimized stereo vision. IEEE Trans Circuits Syst Video Technol 23(1):60–73

    Article  Google Scholar 

  30. Banz C, Hesselbarth S, Flatt H, Blume H, Pirsch P (2010) Real-time stereo vision system using semi-global matching disparity estimation: architecture and FPGA-implementation. In: 2010 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, pp 93–101

  31. Richardt C, Orr D, Davies I, Criminisi A, Dodgson NA (2010) Real-time spatiotemporal stereo matching using the dual-cross-bilateral grid. In: Proceedings the 11th European Conference on Computer Vision, pp 510–523

    Chapter  Google Scholar 

Download references

Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A3B01015379).

Author information

Authors and Affiliations

  1. Samsung Electronics, Hwaseong-si, Gyeonggi-do, 18448, Korea

    Seung-Ho Ok

  2. School of Electronics Engineering, Kyungpook National University, Daegu, 41566, Korea

    Jae Hoon Shim & Byungin Moon

Authors
  1. Seung-Ho Ok
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jae Hoon Shim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Byungin Moon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Byungin Moon.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ok, SH., Shim, J.H. & Moon, B. Modified adaptive support weight and disparity search range estimation schemes for stereo matching processors. J Supercomput 74, 6665–6690 (2018). https://doi.org/10.1007/s11227-017-2058-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-017-2058-y

Keywords

Search

Navigation