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.
Similar content being viewed by others
References
DeSouza GN, Kak AC (2002) Vision for mobile robot navigation: a survey. IEEE Trans Pattern Anal Mach Intell 24(2):237–267
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
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
Bostanci E, Kanwal N, Clark AF (2015) Augmented reality applications for cultural heritage using Kinect. Hum Centric Comput Inf Sci 5(1):1–20
Ho YS (2013) Challenging technical issues of 3D video processing. JoC 4(1):1–6
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
Scharstein D, Szeliski R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int J Comput Vis 47(1):7–42
Nalpantidis L, Georgios CS, Antonios G (2008) Review of stereo vision algorithms: from software to hardware. Int J Optomechatroni 2(4):435–462
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
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
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
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
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
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
Yoon KJ, Kweon IS (2006) Adaptive support-weight approach for correspondence search. IEEE Trans Pattern Anal Mach Intell 28(4):650–656
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
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
Perri S, Corsonello P, Cocorullo G (2013) Adaptive census transform: a novel hardware-oriented stereovision algorithm. Comput Vis Image Underst 117(1):29–41
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
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
Banks J, Corke PI (2001) Quantitative evaluation of matching methods and validity measures for stereo vision. Int J Robot Res 20(7):512–532
Hirschmuller H, Scharstein D (2007) Evaluation of cost functions for stereo matching. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 1–8
Hu X, Mordohai P (2012) A quantitative evaluation of confidence measures for stereo vision. IEEE Trans Pattern Anal Mach Intell 34(11):2121–2133
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
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
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
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
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
Fife WS, Archibald JK (2013) Improved census transforms for resource-optimized stereo vision. IEEE Trans Circuits Syst Video Technol 23(1):60–73
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
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
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
Corresponding author
Rights and permissions
About this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-017-2058-y