Depth-Reliability-Based Stereo-Matching Algorithm and Its VLSI Architecture Design
Pages 1038 - 1050
Abstract
A low-complexity depth-reliability-based stereomatching algorithm and an efficient scanline memory-merging implementation scheme are proposed in this paper. The developed algorithm analyzes the accuracy of disparity results by using simple local window-based methods and preserves reliable information only. A bidirectional depth propagation flow is then adopted to fill the unreliable segments by using reliable information. Moreover, a set of predefined function-specific reliability variables are extracted to further improve depth quality in the occluded and smooth regions, which can reduce 39% bad pixels obtained by applying the basic 7 × 7 window-based matching. The proposed scanline memory-merging scheme along with data prefetching can lead to 32.7% savings on the scanline memory area and relax the requirements of external frame buffer size and bandwidth. Experimental results show that the implemented stereo-matching hardware has a gate count of 223 k including the scanline memory, and can achieve up to 70 frames/s for 480 × 540 resolution (2 × 2 downsampling of FullHD side-by-side 3-D format) with 56 disparity levels.
References
[1]
T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms. New York, NY, USA: McGraw-Hill, 1990.
[2]
Y. Y. Boykov and M.-P. Jolly, “Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images,” in Proc. 8th IEEE Int. Conf. Comput. Vis. (ICCV), Jul. 2001, pp. 105–112.
[3]
J. Sun, N.-N. Zheng, and H.-Y. Shum, “Stereo matching using belief propagation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 7, pp. 787–800, Jul. 2003.
[4]
D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” Int. J. Comput. Vis., vol. 47, nos. 1–3, pp. 7–42, Apr. 2002.
[5]
O. Veksler, “Stereo correspondence with compact windows via minimum ratio cycle,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 12, pp. 1654–1660, Dec. 2002.
[6]
R. K. Gupta and S. Y. Cho, “Real-time stereo matching using adaptive binary window,” in Proc. Int. Symp. 3D Data Process. Visualizat. Transmiss., vol. 1. 2010, pp. 1–8.
[7]
N. Y.-C. Chang, T.-H. Tsai, B.-H. Hsu, Y.-C. Chen, and T.-S. Chang, “Algorithm and architecture of disparity estimation with mini-census adaptive support weight,” IEEE Trans. Circuits Syst. Video Technol., vol. 20, no. 6, pp. 792–805, Jun. 2010.
[8]
K.-J. Yoon and I. S. Kweon, “Adaptive support-weight approach for correspondence search,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 4, pp. 650–656, Apr. 2006.
[9]
Y. Qu, J. Jiang, X. Deng, and Y. Zheng, “Robust local stereo matching under varying radiometric conditions,” IET Comput. Vis., vol. 8, no. 4, pp. 263–276, Aug. 2014.
[10]
L. De-Maeztu, A. Villanueva, and R. Cabeza, “Near real-time stereo matching using geodesic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 2, pp. 410–416, Feb. 2012.
[11]
M. Baydoun and M. A. Al-Alaoui, “Enhancing stereo matching with classification,” IEEE Access, vol. 2, pp. 485–499, May 2014.
[12]
J. Joglekar, S. S. Gedam, and B. K. Mohan, “Image matching using SIFT features and relaxation labeling technique—A constraint initializing method for dense stereo matching,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 9, pp. 5643–5652, Sep. 2014.
[13]
M. Hariyama, N. Yokoyama, and M. Kameyama, “1000 frame/sec stereo matching VLSI processor with adaptive window-size control,” in Proc. Conf. Asian Solid-State Circuits, Nov. 2006, pp. 123–126.
[14]
M. Hariyama and M. Kameyama, “VLSI processor for reliable stereo matching based on window-parallel logic-in-memory architecture,” in IEEE Conf. Symp. VLSI Circuit Dig. Tech. Paper, Jun. 2004, pp. 166–169.
[15]
S.-K. Han, S. Woo, M.-H. Jeong, and B.-J. You, “Improved-quality real-time stereo vision processor,” in Proc. 22nd Int. Conf. VLSI Design, Jan. 2009, pp. 287–292.
[16]
A. Darabiha, W. J. MacLean, and J. Rose, “Reconfigurable hardware implementation of a phase-correlation stereoalgorithm,” Mach. Vis. Appl., vol. 17, no. 2, pp. 116–132, Apr. 2006.
[17]
C. Ttofis and T. Theocharides, “Towards accurate hardware stereo correspondence: A real-time FPGA implementation of a segmentation-based adaptive support weight algorithm,” in Proc. Design Autom. Test Eur. Conf. Exhibit. (DATE), Mar. 2012, pp. 703–708.
[18]
X. Sun, X. Mei, S. Jiao, M. Zhou, and H. Wang, “Stereo matching with reliable disparity propagation,” in Proc. Int. Conf. 3DIMPVT, May 2011, pp. 132–139.
[19]
N. Jacobson, Y. Freund, and T. Q. Nguyen, “An online learning approach to occlusion boundary detection,” IEEE Trans. Image Process., vol. 21, no. 1, pp. 252–261, Jan. 2012.
[20]
L. Wang, M. Liao, M. Gong, R. Yang, and D. Nister, “High-quality real-time stereo using adaptive cost aggregation and dynamic programming,” in Proc. Int. Symp. 3D Data Process., Visualizat., Transmiss., Jun. 2006, pp. 798–805.
[21]
H. Hubert, B. Stabernack, and F. Zilly, “Architecture of a low latency image rectification engine for stereoscopic 3-D HDTV processing,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 5, pp. 813–822, May 2013.
[22]
Middlebury Stereo Datasets. [Online]. Available: http://vision.middlebury.edu/stereo/data/, accessed May 5, 2013.
[23]
M. Werner, B. Stabernack, and C. Riechert, “Hardware implementation of a full HD real-time disparity estimation algorithm,” IEEE Trans. Consum. Electron., vol. 60, no. 1, pp. 66–73, Feb. 2014.
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Published: 01 June 2015
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