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Boundary Depth Estimation Using Hough Transform and Focus Measure

허프 변환과 초점정보를 이용한 경계면 깊이 추정

  • Kwon, Dae-Sun (College of Electrical and Computer Engineering, Chungbuk National University) ;
  • Lee, Dae-Jong (College of Electrical and Computer Engineering, Chungbuk National University) ;
  • Chun, Myung-Geun (College of Electrical and Computer Engineering, Chungbuk National University)
  • 권대순 (충북대학교 전자정보대학 전자공학부 컴퓨터정보통신연구소) ;
  • 이대종 (충북대학교 전자정보대학 전자공학부 컴퓨터정보통신연구소) ;
  • 전명근 (충북대학교 전자정보대학 전자공학부 컴퓨터정보통신연구소)
  • Received : 2014.09.23
  • Accepted : 2015.02.12
  • Published : 2015.02.25

Abstract

Depth estimation is often required for robot vision, 3D modeling, and motion control. Previous method is based on the focus measures which are calculated for a series of image by a single camera at different distance between and object. This method, however, has disadvantage of taking a long time for calculating the focus measure since the mask operation is performed for every pixel in the image. In this paper, we estimates the depth by using the focus measure of the boundary pixels located between the objects in order to minimize the depth estimate time. To detect the boundary of an object consisting of a straight line and a circle, we use the Hough transform and estimate the depth by using the focus measure. We performed various experiments for PCB images and obtained more effective depth estimation results than previous ones.

로봇 비전, 3차원 형상 모델링 그리고 모션 제어를 하기 위해 3차원 깊이 추정이 필요하다. 기존에 제안 되었던 깊이 추정 방법은 렌즈와 물체사이의 거리를 변화시켜 가면서 취득된, 일련의 전체영상에 대해서 초점값을 계산하는 방법에 기초하고 있다. 그러나 이러한 방법은 전체 영상에 대해서 초점값 계산을 위한 마스크 연산을 하기에 수행 시간이 오래 걸리는 단점이 있다. 이에 반해서 본 논문에서 제안하는 방법은 물체간의 깊이를 추정하는 시간을 개선하기 위하여 전체 영상을 고려하지 않고, 물체간의 경계면과 경계면 부근의 영상 정보만을 이용하여 깊이를 추정한다. 특히 직선과 원으로 구성된 물체의 경계면을 검출하기 위해서 허프 변환을 이용하였으며, 깊이 추정은 초점 정보를 이용하였다. PCB 영상을 이용하여 실험을 수행한 결과, 이전에 비해서 더욱 효과적인 깊이 추정이 가능함을 알 수 있었다.

Keywords

References

  1. Joon Jae Lee, "3-D Solder Paste Inspection Based on B-spline Surface Approximation," KSIAM IT series, Vol.10, No.1, pp 31-45, 2006.
  2. Jong Hwan Yoon, "Development of an Autofocusing System for LCD Defect Detection," Master thesis, Chungbuk National University, 2011.
  3. Ji Seok Jeong, "A Relative Depth Estimation Algorithm Using Focus Measure," Journal of Korean Institute of Intelligent Systems, Vol. 23, No. 6, pp 527-532, 2013. https://doi.org/10.5391/JKIIS.2013.23.6.527
  4. Muhammad Tariq Mahmood, Tae-Sun Choi, "3D shape recovery from image focus using kernel regression in eigenspace," Image and Vision Computing, Vol. 28, No 4, pp 634-643, 2010. https://doi.org/10.1016/j.imavis.2009.10.005
  5. Aamir Saeed Malik, Humaira Nisar, Tae-Sun Choi, "A Fuzzy-Neural approach for estimation of depth map using focus," Applied Soft Computing, Vol. 11, No. 2, pp 1837-1850, 2011 https://doi.org/10.1016/j.asoc.2010.05.030
  6. Muhammad Tariq Mahmood, Abdul Majid, Tae-Sun Choi, "Optimal depth estimation by combining focus measures using genetic programming," Information Science, Vol. 181, No. 7, pp 1249-1263, 2011. https://doi.org/10.1016/j.ins.2010.11.039
  7. Rashid Minhas, Abdul Adeel Mohammed, Q.M. Jonathan Wu, "Shape from focus using fast discrete curvelet transform," Pattern Recognition, Vol. 44, No. 4, pp 839-853, 2011. https://doi.org/10.1016/j.patcog.2010.10.015
  8. Ikhyun Lee, Muhammad Tariq Mahmood, Tae-Sun Choi, "Adaptive window selection for 3D shape recovery from image focus," Optics&Laser Technology, Vol. 45, pp 21-31, 2013.
  9. J. Illingworth, J. Kittler, "A survey of the Hough Transform," Computer Vision, Graphics and Image Processing, Vol 44, pp 87-116, 1988. https://doi.org/10.1016/S0734-189X(88)80033-1
  10. A. Rosenfeld, Picture Processing by Computer, Academic Press, New York, 1969.
  11. R.O. Duda, P.E. Hart, "Use of the Hough Transformation to detect lines and curves in pictures, Communications of the Association of Computing Machinery," Vol 15, pp 11-15, 1972. https://doi.org/10.1145/361237.361242
  12. E.R. Davies, "A modified Hough scheme for general circle location," Pattern Recognition Letters, Vol 7, pp 37-43, 1987.
  13. R.K.K. Yip, P.K.S. Tam, D.N.K. Leung, "Modification of Hough transform for circles and ellipses detection using a 2-dimensional array," Pattern Recognition, Vol 25, pp 1007-1022, 1992. https://doi.org/10.1016/0031-3203(92)90064-P
  14. Chester F. Carlson, Hough circle transform, Rochester Institute of Technology: Lecture notes, 2004.
  15. Junheong Park, Seung-Min Park, Kwee-Bo Sim, "Occluded Object Motion Tracking Method based on Combination of 3D Reconstruction and Optical Flow Estimation," Journal of The Korean Institute of Intelligent System, Vol. 21, pp 537-542, 2011. https://doi.org/10.5391/JKIIS.2011.21.5.537
  16. Gyeongyong Heo, Young Woon Woo, Kwang-Baek Kim, "Optimal Parameter Selection in Edge Strength Hough Transform," Journal of The Korean Institute of Intelligent System, Vol. 17, pp 575-581, 2007. https://doi.org/10.5391/JKIIS.2007.17.5.575

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