The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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A Hardware Architecture of Hough Transform Using an Improved Voting Scheme

개선된 보팅 정책을 적용한 허프 변환 하드웨어 구조

  • 이정록 (경북대학교 IT대학 전자공학부) ;
  • 배경렬 (경북대학교 IT대학 전자공학부) ;
  • 문병인 (경북대학교 IT대학 전자공학부)
  • Received : 2013.04.05
  • Accepted : 2013.09.09
  • Published : 2013.09.30
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Abstract

The Hough transform for line detection is widely used in many machine vision applications due to its robustness against data loss and distortion. However, it is not appropriate for real-time embedded vision systems, because it has inefficient computation structure and demands a large number of memory accesses. Thus, this paper proposes an improved voting scheme of the Hough transform, and then applies this scheme to a Hough transform hardware architecture so that it can provide real-time performance with less hardware resource. The proposed voting scheme reduces computation overhead of the voting procedure using correlation between adjacent pixels, and improves computational efficiency by increasing reusability of vote values. The proposed hardware architecture, which adopts this improved scheme, maximizes its throughput by computing and storing vote values for many adjacent pixels in parallel. This parallelization for throughput improvement is accomplished with little hardware overhead compared with sequential computation.

허프 변환은 데이터 손실 및 왜곡이 포함된 영상에서도 직선 정보 추출에 용이한 장점이 있어 컴퓨터 비전 분야의 응용분야에 널리 사용되어 왔다. 그러나 허프 변환의 보팅 과정은 비효율적인 연산구조와 많은 메모리 접근횟수로 인해 실시간 처리 임베디드 비전 시스템에 적용하는데 한계가 있다. 이에 본 논문에서는 허프 변환의 개선된 보팅 정책을 제시하고, 이를 적용하여 적은 하드웨어 자원 사용량으로 실시간 성능을 만족하는 허프 변환의 하드웨어 구조를 제안한다. 제안된 보팅 정책은 인접한 픽셀 간의 관계를 이용하여 보팅 연산 과정의 오버헤드를 줄였으며, 하드웨어 재사용성을 높임으로서 효율적인 연산구조를 가진다. 이러한 개선된 보팅 정책을 적용한 제안된 하드웨어 구조는 인접한 픽셀들의 보트 값을 병렬적으로 연산하고 저장하여 시간당 처리량을 높인다. 제안 구조의 장점은 순차적 연산 방식 대비 매우 적은 추가 하드웨어 자원만으로 이러한 성능 향상을 위한 병렬화를 달성한다는 것이다.

Keywords

References

  1. C. T. Ho and L. H. Chen, "A high speed algorithm for line detection," Pattern Recognition Lett., vol. 17, no. 5, pp. 467-473, May 1996. https://doi.org/10.1016/0167-8655(96)00009-8
  2. Y. S. Lee, H. S. Koo, and C. S. Jeong, "A straight line detection using principal component analysis," Pattern Recognition Lett., vol. 27, no. 14, pp. 1744-1754, Oct. 2006. https://doi.org/10.1016/j.patrec.2006.04.016
  3. M. H. Chang and J. A. Park, "Information extraction of the moving objects based on edge detection and optical flow," J. Korea Inform. Commun. Soc. (KICS), vol. 27, no. 8A, pp. 822-828, Aug. 2002.
  4. P. V. C. Hough, "Methods and means for recognizing complex patterns," U.S. Patent 3.069.654, Dec. 1962.
  5. R. O. Duda and P. E. Hart, "Use of the hough transformation to detect lines and curves in pictures," Commun. ACM, vol. 15, no. 1, pp. 11-15, Jan. 1972. https://doi.org/10.1145/361237.361242
  6. F. O'Gorman and M. B. Clowes, "Finding picture edges through collinearity of feature points," IEEE Trans. Comput., vol. C-25, no. 4, pp. 449-456, Apr. 1976.
  7. W Niblack and D Petkovic, "On improving the accuracy of the hough transform," Machine Vision Applicat., vol. 3, no. 2, pp. 87-106, Mar. 1990. https://doi.org/10.1007/BF01212193
  8. L. A. F. Fernandes and M. M. Oliveira, "Real-time line detection through an improved hough transform voting scheme," Pattern Recognition, vol. 41, no. 1, pp. 299-314, Jan. 2008. https://doi.org/10.1016/j.patcog.2007.04.003
  9. J. Princen, J. Illingworth, and J. Kittler, "A hierarchical approach to line extraction based on the hough transform," Comput. Vision, Graphics, Image Process., vol. 52, no. 1, pp. 57-77, Oct. 1990. https://doi.org/10.1016/0734-189X(90)90123-D
  10. S. B. Yacoub and J. Jolion, "Hierarchical line extraction," IEE Proc. Vision, Image, Signal Process., vol. 142, no. 1, pp. 7-14, Feb. 1995. https://doi.org/10.1049/ip-vis:19951434
  11. K. Hanahara, T. Maruyama and T. Uchiyama, "A real-time processor for the hough transform," IEEE Trans. Pattern Anal. Mach. Intell., vol. 10, no. 1, pp. 121-125, Jan. 1988. https://doi.org/10.1109/34.3876
  12. F. Zhou and P. Kornerup, "A high speed hough transform using CORDIC," in Proc. Int. Conf. Digital Signal Process. (DSP 1995), pp. 27-39, Limassol, Cyprus, June 1995.
  13. K. Mayasandra, S. Salehi, W. Wang, and H. M. Ladak, "A distributed arithmetic hardware architecture for real-time hough-transform-based segmentation," Canadian J. Elect. Comput. Eng., vol. 30, no. 4, pp. 201-205, May 2005. https://doi.org/10.1109/CJECE.2005.1541752
  14. M. Y. Chern and Y. H. Lu, "Design and integration of parallel hough transform chips for high-speed line detection," in Proc. 11th Int. Conf. Parallel Distributed Syst. Workshops, vol. 2, pp. 42-46, Fukuoka, Japan, July 2005.
  15. Z. H. Chen, A. W. Y. Su, and M. T. Sun, "Resource-efficient FPGA architecture and implementation of hough transform," IEEE Trans. VLSI Syst., vol. 20, no. 8, pp. 1419-1428, Aug. 2012. https://doi.org/10.1109/TVLSI.2011.2160002

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