Machine Vision and Applications

, Volume 18, Issue 2, pp 85–93 | Cite as

Using distance transform to solve real-time machine vision inspection problems

  • Dah-Jye Lee
  • James Archibald
  • Xiaoqian Xu
  • Pengcheng Zhan
Original Paper


This paper describes novel solutions to two challenging real-time inspection tasks in machine vision. The first is fast surface approximation for volume and surface area measurements of irregularly shaped objects; the second is fast intensity gradient correction for surface inspection and evaluation of spherical objects. Both solutions apply a distance transform (DT) based on the distance of each image pixel from the object boundary. We describe both real-time machine vision inspection tasks and discuss their complexity. We show that the new solutions result in significant improvements in both accuracy and efficiency—despite the relative simplicity of the DT approach.


Distance transform Laser triangulation Surface approximation Volumetric measurement Intensity gradient 


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  1. 1.
    Almansa, A., Cao, F., Gousseau, Y., Rouge, B.: Interpolation of digital elevation models using AMLE and related methods. IEEE Trans. Geosci. Remote Sensing 40-2, 314–325 (2002)Google Scholar
  2. 2.
    Anzalone, A., Machi, A.: Real-time visual inspection of moulded plastic drippers. In: Proceedings of the Conference on Computer Architectures for Machine Perception, pp. 402–409 (1995)Google Scholar
  3. 3.
    Arlandis, J., Perez-Cortes, J., Llobet, J.R.: Handwritten character recognition using the continuous distance transform. In: Proceedings of IEEE 15th International Conference on Pattern Recognition, vol. 1, pp. 940–943 (2000)Google Scholar
  4. 4.
    Au A.T.S., Tsang P.W.M.: Affine invariant recognition of 2D occluded objects using geometric hashing and distance transformation. In: Proceedings of IEEE TENCON Digital Signal Processing Applications, vol. 1, pp. 64–67 (1996)Google Scholar
  5. 5.
    Cong, G., Parvin, B.: Surface reconstruction from sparse fringe contours. In: Proceedings of the 4th IEEE Workshop on Applications of Computer Vision, pp. 140–145 (1998)Google Scholar
  6. 6.
    Cong, G., Parvin, B.: Surface recovery from planar sectional contours. In: Proceedings of the 15th International Conference on Pattern Recognition, vol. 4, pp. 106–109 (2000)Google Scholar
  7. 7.
    Gavrila, D.M., Giebel, J.: Virtual sample generation for template-based shape matching. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 1, 676–681 (2001)Google Scholar
  8. 8.
    Golland, P., Eric, W., Grimson, L.: Fixed topology skeletons. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 1, pp. 10–17 (2000)Google Scholar
  9. 9.
    Grant P. (1990) Local path planning: a brute force approach. Proc. IEEE Int. Symp. Intell. Control, 2, 689–693CrossRefGoogle Scholar
  10. 10.
    Kovacs V., Zs M. (1995) Massively-parallel handwritten character recognition based on the distance transform. Pattern Recognit. 28-3: 293–301CrossRefGoogle Scholar
  11. 11.
    Kwon J.S., Choi J.H., Choi J.S. (1995) Two-dimensional object recognition using chamfer distance transform on morphological skeleton. Proc. SPIE Conf. Vis. Commun. Image Process. 2501: 1750–1761Google Scholar
  12. 12.
    Lee D.J., Westover B., Eifert J.D. (2002) Three-dimensional surface approximation from incomplete data using distance SPIE Image Reconstr. Incomplete Data II 4792-15: 125–134Google Scholar
  13. 13.
    Lee D.J., Eifert J.D., Zhan P., Westover B.P. (2003) Fast surface approximation for volume and surface area measurements using distance transform. Opt. Eng. 42: 2947–2955CrossRefGoogle Scholar
  14. 14.
    Lee J., Fukue K., Shimoda H., Sakata T. (1993) Grid data generation from contour images by using Euclid distance transformation. Proc. IEEE Int. Symp. Geosci. Remote Sensing 4: 1727–1729Google Scholar
  15. 15.
    Liu, B., Fang, X., Wang, W., Zheng, Z.: Automatic separation of overlapping objects. In: Proceedings of the 4th IEEE World Congress on Intelligent Control and Automation, vol. 4, pp. 2901–2905 (2002)Google Scholar
  16. 16.
    Lotufo RA, Zampirolli FA (2001) Fast multidimensional parallel Euclidean distance transform based on mathematical morphology. In: Proceedings of XIV Brazilian Symposium on Computer Graphics and Image Processing, pp. 100–105Google Scholar
  17. 17.
    Lotufo, R.A., Falcao, A.A., Zampirolli, F.A.: Fast Euclidean distance transform using a graph-search algorithm. In: Proceedings XIII Brazilian Symposium on Computer Graphics and Image Processing, pp. 269–275 (2000)Google Scholar
  18. 18.
    Luo, B., Hancock, E.R.: Slice interpolation using the distance transform and morphing. In: Proceedings of the 13th International Conference on Digital Signal Processing, vol. 2, pp. 1083–1086 (1997)Google Scholar
  19. 19.
    Moon T.K., Stirling W.C. (1999) Mathematical Methods and Algorithms for Signal Processing. Prentice-Hall, Englewood CliffsGoogle Scholar
  20. 20.
    Ng, G.S., Shi, D., Gunn, S.R., Damper, R.I.: Nonlinear active handwriting models and their applications to handwritten Chinese radical recognition. In: Proceedings of the 7th International Conference on Document Analysis and Recognition, vol. 1, pp. 534–538 (2003)Google Scholar
  21. 21.
    Olson C.F. (2002) Maximum-likelihood image matching. IEEE Trans. Pattern Anal. Mach. Intell. 24-6: 853–857CrossRefMathSciNetGoogle Scholar
  22. 22.
    Paglieroni D.W. (1992) Distance transforms. Properties and machine vision applications. Graph. Models Image Process. 54-1: 56–74Google Scholar
  23. 23.
    Pan Y., Li Y., Li J., Li K., Zheng S.Q. (2002) Efficient parallel algorithms for distance maps of 2D binary images using an optical bus. IEEE Trans. Syst. Man Cybern. A 32-2: 22–236Google Scholar
  24. 24.
    Pudney C. (1996) Distance-based skeletonization of 3D images. Proc. IEEE TENCON Digit. Signal Process. Appl. 1, 209–214Google Scholar
  25. 25.
    Pudney C. (1998) Distance-ordered homotopic thinning: a skeletonization algorithm for 3D digital images. Comput. Vis. Image Underst. 72-3: 404–413CrossRefGoogle Scholar
  26. 26.
    Qian K., Cao S., Bhattacharya P. (1997) Skeletonization of gray-scale images by gray weighted distance transform. Proc. SPIE Conf. Vis. Inf. Process. 3074: 224–228CrossRefGoogle Scholar
  27. 27.
    Saha P.K., Wehrli F.W. (2004) Measurement of trabecular bone thickness in the limited resolution regime of in vivo MRI by fuzzy distance transform. IEEE Trans. Med. Imaging 23-1: 53–62CrossRefGoogle Scholar
  28. 28.
    Sharghi, M., Ricketts, I.W.: A novel method for accelerating the visualisation process used in virtual colonoscopy. In: Proceedings of the Fifth International Conference on Information Visualisation, pp. 167–172 (2001)Google Scholar
  29. 29.
    Shih F.Y., Mitchell O.R. (1988) Industrial parts recognition and inspection by image morphology. Proc. IEEE Int. Conf. Robot. Autom. 3: 1764–1766Google Scholar
  30. 30.
    Sintorn, M., Borgefors, G.: Weighted distance transforms in rectangular grids. In: IEEE Proceedings of 11th International Conference on Image Analysis and Processing, pp. 322–326 (2001)Google Scholar
  31. 31.
    Sramek M., Kaufman A. (2000) Fast ray-tracing of rectilinear volume data using distance transforms. IEEE Trans. Vis. Comput. Graph. 6-3: 236–252CrossRefGoogle Scholar
  32. 32.
    Sudha, N.: An area-efficient pipelined array architecture for Euclidean distance transformation and its FPGA implementation. In: Proceedings of the 17th IEEE International Conference on VLSI Design, pp. 689–692 (2004)Google Scholar
  33. 33.
    Techmer A. (2001) Contour-based motion estimation and object tracking for real-time applications. Proc. IEEE Int. Conf. Image Process. 3, 648–651Google Scholar
  34. 34.
    Tsang P.W.M., Yuen P.C., Lam F.K. (1994) Classification of partially occluded objects using 3-point matching and distance transformation. Pattern Recognit. 27-1: 27–40CrossRefGoogle Scholar
  35. 35.
    Tuduki, Y., Murase, K., Izumida, M., Miki, H., Kikuchi, K., Murakami, K., Ikezoe, J.: Automated seeded region growing algorithm for extraction of cerebral blood vessels from magnetic resonance angiographic data. In: Proceedings of the IEEE 22nd Annual International Conference, vol. 3, pp. 1756–1759 (2000)Google Scholar
  36. 36.
    Zhou Y., Toga A.W. (1999) Efficient skeletonization of volumetric objects. IEEE Trans. Vis. Comput. Graph. 5-3: 196–209CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Dah-Jye Lee
    • 1
  • James Archibald
    • 1
  • Xiaoqian Xu
    • 1
  • Pengcheng Zhan
    • 1
  1. 1.Department of Electrical and Computer EngineeringBrigham Young UniversityProvoUSA

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