DOI QR코드

DOI QR Code

적응적 영역 가중치를 이용한 실시간 스테레오 비전 시스템 설계

Design of a Realtime Stereo Vision System using Adaptive Support-weight

  • 류동훈 (가톨릭대학교 정보통신전자공학부) ;
  • 박태근 (가톨릭대학교 정보통신전자공학부)
  • Ryu, Donghoon (Department of Information, Communication, and Electronic Engineering, The Catholic University of Korea) ;
  • Park, Taegeun (Department of Information, Communication, and Electronic Engineering, The Catholic University of Korea)
  • 투고 : 2013.07.30
  • 발행 : 2013.11.25

초록

지역적 정합방법을 이용한 스테레오 시스템은 알고리즘의 특성상 하드웨어 설계가 용이하여 많이 사용되나 낮은 정합률로 인해 정확한 깊이 영상을 얻기 힘들기 때문에 많은 응용 분야에 사용하기에 제한이 있다. 본 논문에서 제안한 스테레오 시스템은 픽셀의 변화도(gradient)를 기반으로 한 적응적인 가중치 알고리즘을 이용하여 높은 정합 성능을 보이며 하드웨어로 설계하였을 때 실시간처리가 가능하다. 일반적으로 적응적인 가중치 윈도우를 적용할 경우 중간 결과를 재사용하기 불가능하지만 행, 열을 분리하여 처리함으로써 데이터를 재사용할 수 있고 따라서 처리성능이 개선되었다. 알고리즘에 필요한 지수 및 아크탄젠트 함수를 구현하기 위해 선형(PWL, piecewise linear) 및 계단(step) 함수 등으로 근사화한 뒤 에러를 분석하여 최선의 파라미터를 선택하였다. 제안한 구조는 실시간처리를 위하여 9개의 프로세서를 사용하여 병렬처리를 하였으며, 동부하이텍 0.18um 라이브러리로 합성하였을 경우 최대 동작주파수 350MHz(33 fps)와 424K 게이트의 하드웨어 복잡도를 나타내었다.

The stereo system based on local matching is very popular due to its algorithmic simplicity, however it is limited to apply to various applications because it shows poor quality with low matching rates. In this paper, we propose and design a realtime stereo system based on an adaptive support-weight and the system shows low error rates and realtime performance. Generally, in the adaptive support-weight algorithm the intermediate computing results can not be reused to reduce the number of computations. In this research we modify the scheduling to reuse the intermediate results for the better performance by processing rows and columns separately. The nonlinear functions such as exponential or arc tangent have been designed with piecewise linear and step functions by empirical simulations and error analysis. The proposed architecture is composed of 9 processing elements for realtime performance. The proposed stereo system has been designed and synthesized using Donbu Hitek 0.18um standard cell library and can run up to 350Mhz operation frequency (33 frames per second) with 424K gates.

키워드

참고문헌

  1. K. Y. Lee, J. W. Lee, and N. Houshangi, "A stereo matching algorithm based on top-view transformation and dynamic programming for road-vehicle detection," Int. J. of Control, Automation, and Systems, vol. 7, no. 2, pp. 221-231, 2009. https://doi.org/10.1007/s12555-009-0208-6
  2. D. S. Kim. "A real-time stereo depth extraction hardware for intelligent home assistant robot," IEEE Trans. on Consumer Electronics, vol. 56, pp. 1782-1788, 2010. https://doi.org/10.1109/TCE.2010.5606326
  3. D. Scharstein and R. Szeliski, "A taxonomy and evaluation of dense two-frame stereo correspondence algorithms," Int. J. of Computer Vision, vol. 47, pp. 79-88, 2002. https://doi.org/10.1023/A:1014581421794
  4. M. Z. Brown, D. Burschka, and G. D. Hager, "Advances in computational stereo," Trans. Pattern Analysis and Machine Intelligence, vol. 25, pp. 993-1008, 2003. https://doi.org/10.1109/TPAMI.2003.1217603
  5. K. J. Yoon, and I. S. Kweon, "Adaptive support-weight approach for correspondence search," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, pp. 650-656, 2006. https://doi.org/10.1109/TPAMI.2006.70
  6. A. Hosni, M. Bleyer, M. Gelautz, and C. Rhemann. "Local stereo matching using geodesic support weights," 16th IEEE Int. Conf. on Image Processing on, pp. 2093-2096, 2009.
  7. L. De-Maeztu, A. Villanueva, and R. Cabeza, "Stereo matching using gradient similarity and locally adaptive support-weight," Pattern Recognition Letters, vol.32, pp. 1643-1651, 2011.
  8. N. Chang, T. M. Lin, T. H. Tsai, Y. C. Tseng, T. S. Chang, "Real-time DSP implementation on local stereo matching," IEEE Int. Conf. on Multimedia and Expo, pp. 2090-2093, 2007.
  9. K. Ambrosch, W. Kubinger, "Accurate hardware-based stereo vision," Computer Vision and Image Understanding, vol. 114. pp. 1303-1316, 2010. https://doi.org/10.1016/j.cviu.2010.07.008
  10. S. Jin, J. Cho, X. D. Pham, K. M. Lee, S. K Park, M. Kim, and J. W. Jeon, "FPGA design and implementation of a real-time stereo vision system," IEEE Trans. on Circuits and Systems for Video Technology, vol. 20, no. 1, pp. 15-26, 2010. https://doi.org/10.1109/TCSVT.2009.2026831
  11. A. Darabiha, J. Rose, and W. J. Maclean, "Video-rate stereo depth measurement on programmable hardware," 2003 IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 203-210, 2003.
  12. S. Park, H. Jeong, "Real-time stereo vision FPGA chip with low error rate," 2007 Int. Conf. on Multimedia and Ubiquitous Engineering, pp. 751-765, 2007.
  13. H. Hirschmuller, P. R. Innocent, and J. Garibaldi, "Real-time correlation based stereo vision with reduced border errors," Int. J. of Computer Vision, vol. 47, pp. 229-246, 2004.
  14. N. Chang, T. Tsai, B. Hsu, Y. Chen, and T. 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. https://doi.org/10.1109/TCSVT.2010.2045814
  15. K. Hwang, "Computer arithmetic: principles, architecture, and design," John Wiley & Sons, 1979.