Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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A hardware architecture based on the NCC algorithm for fast disparity estimation in 3D shape measurement systems

고밀도 3D 형상 계측 시스템에서의 고속 시차 추정을 위한 NCC 알고리즘 기반 하드웨어 구조

  • Bae, Kyeong-Ryeol (School of Electrical Engineering & Computer Science, Kyungpook National University) ;
  • Kwon, Soon (Daegu Gyeongbuk Institute of Science & Technology) ;
  • Lee, Yong-Hwan (School of Electronic Engineering, Kumoh National Institute of Technology) ;
  • Lee, Jong-Hun (Daegu Gyeongbuk Institute of Science & Technology) ;
  • Moon, Byung-In (School of Electrical Engineering & Computer Science, Kyungpook National University)
  • 배경렬 (경북대학교 전자전기컴퓨터학부) ;
  • 권순 (대구경북과학기술원) ;
  • 이용환 (금오공과대학교 전자공학부) ;
  • 이종훈 (대구경북과학기술원) ;
  • 문병인 (경북대학교 전자전기컴퓨터학부)
  • Received : 2009.10.27
  • Accepted : 2010.03.08
  • Published : 2010.03.31
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Abstract

This paper proposes an efficient hardware architecture to estimate disparities between 2D images for generating 3D depth images in a stereo vision system. Stereo matching methods are classified into global and local methods. The local matching method uses the cost functions based on pixel windows such as SAD(sum of absolute difference), SSD(sum of squared difference) and NCC(normalized cross correlation). The NCC-based cost function is less susceptible to differences in noise and lighting condition between left and right images than the subtraction-based functions such as SAD and SSD, and for this reason, the NCC is preferred to the other functions. However, software-based implementations are not adequate for the NCC-based real-time stereo matching, due to its numerous complex operations. Therefore, we propose a fast pipelined hardware architecture suitable for real-time operations of the NCC function. By adopting a block-based box-filtering scheme to perform NCC operations in parallel, the proposed architecture improves processing speed compared with the previous researches. In this architecture, it takes almost the same number of cycles to process all the pixels, irrespective of the window size. Also, the simulation results show that its disparity estimation has low error rate.

Keywords

References

  1. 김종만, 도용태 “이동 물체의 3차원 계측을 위한 PSD 센서 배열 설계”, 센서학회지, 제17권, 제2호, pp. 106-113, 2008. https://doi.org/10.5369/JSST.2008.17.2.106
  2. S. Perri, D. Colonna, P. Zicari, and P. Corsonello, "SAD-based stereo matching circuit for FPGAs", Proc. of the 13th IEEE International Conference on Electronics, Circuits, and Systems, pp. 846-849, Nice, France, 2006.
  3. L. Di Stefano, M. Marchionni, and S. Mattoccia "A fast area-based stereo matchng algorithm", Image and Visioin Computing, vol. 22, no. 12, pp. 983-1005, 2004. https://doi.org/10.1016/j.imavis.2004.03.009
  4. D. Scharstein and R. Szeliski, "A taxonomy and evaluation of dense two-frame stereo correspondence algorithms", International Journal of Computer Vision, vol. 47 (April-June), pp. 7-42, 2002. https://doi.org/10.1023/A:1014573219977
  5. C. Geogoulas, L. Kotoulas, G. Ch. Sirakoulis, I. Andreadis, and A. Gasteratos, "Real-time disparity map computation module", Microprocessors and Microsystems, vol. 32, no. 3, pp. 159-170, 2008. https://doi.org/10.1016/j.micpro.2007.10.002
  6. M. Hariyama, N. Yokoyama, and M. Kameyama, "FPGA implementation of a stereo matching processor based on window-parallel-and-pixel-parallel architecture", IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E88-A, no. 12 pp. 3516-3522, 2005. https://doi.org/10.1093/ietfec/e88-a.12.3516
  7. M. Hariyama, T. Takeuchi, and M. Kameyama, "VLSI processor for reliable stereo matching based on adaptive window-size selection", Proc. of the 2001 IEEE International Conference on Robotics & Automation, pp. 1168-1173, Seoul, Korea, 2001.
  8. L. Di Stefano and S. Mattoccia, "Fast template matching using bounded partial correlation", Machine Vision and Applications, vol. 13, no. 4, pp. 213-221, 2003. https://doi.org/10.1007/s00138-002-0070-5
  9. L. Di Stefano, S. Mattoccia, and M. Mola, "An efficient algorithm for exhaustive template matching based on normalized cross correlation", Proc. of the 12th International Conference on Image Analysis and Processing, pp. 322-327, Mantova, Italy, 2003.
  10. S. Mattoccia, F. Tombari, and L. Di Stefano, "Fast full-search equivalent template matching by enhanced bounded correlation", IEEE Transactions on Image Processing, vol. 17, no. 4, pp. 528-538, 2008. https://doi.org/10.1109/TIP.2008.919362
  11. Shou-Der Wei and Shang-Hong Lai, "Fast template matching based on normalized cross correlation with adaptive multilevel winner update", IEEE Transactions on Image Processing, vol. 17, no. 11, pp. 2227-2235, 2008. https://doi.org/10.1109/TIP.2008.2004615
  12. D. M. Tsai and C. T. Lin, "Fast normalized cross correlation for defect detection", Pattern Recognition Letters, vol. 24, no. 15, pp. 2625-2631, 2003. https://doi.org/10.1016/S0167-8655(03)00106-5
  13. 도용태, 이대식, 류석환, "3차원 위치측정을 위한 스테레오 카메라 시스템의 인공 신경망을 이용한 보정", 센서학회지, 제7권, 제6호, pp. 418-425, 1998.
  14. L. Di Stefano and S. Mattoccia, "A sufficient condition based on the Cauchy-Schwarz inequality for efficient template matching", Proc. of the 2003 International Conference on Image Processing, pp. I - 269-272, Barcelona, Spain, 2003.
  15. L. Di Stefano, Ss Mattoccia, and F. Tombari, "ZNCC-based template matching using bounded partial correlation", Pattern Recognition Letters, vol. 26, no. 14, 2129-2134, 2005. https://doi.org/10.1016/j.patrec.2005.03.022
  16. M. J. McDonnell, "Box-filtering techniques", Computer Graphics and Image Processing, vol. 17, pp. 65-70, 1981. https://doi.org/10.1016/S0146-664X(81)80009-3
  17. http://www.middlebury.edu/stereo