Vision: Human and Machine

  • Arthur Browne
  • Leonard Norton-Wayne


The principal objective of this chapter is to present the principles of computer vision which are applicable in intelligent automation. However, vision by machine is essentially a replacement for human vision, hopefully with improvement. Thus, we start by explaining how the human eye-brain system works, so far as this is known. Machine vision is such a vast subject that we can mention only a few topics that are particularly useful in intelligent automation. Even for these, reference to selected textbooks(1–3) will be necessary for in-depth information. However, we do provide a general survey of machine vision, specifying the various subdivisions and indicating their relevance to automation, before concentrating on the selected practical topics.


Spatial Frequency Convex Hull Machine Vision Modulation Transfer Function Quantization Noise 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    D.H. Ballard and C.M. Brown, Computer Vision, Prentice-Hall, New Jersey (1982).Google Scholar
  2. 2.
    A. Rosenfeld and A.C. Kak, Digital Picture Processing, Academic Press, New York (1976).CrossRefGoogle Scholar
  3. 3.
    R.C. Gonzalez and P. Wintz, Digital Image Processing, Addison-Wesley, Reading, MA (1977).zbMATHGoogle Scholar
  4. 4.
    W.D. Wright, The Measurement of Colour, A. Hilger, London (1969).Google Scholar
  5. 5.
    O.J. Braddick and A.C. Sleigh (eds.), Physical and Biological Processing of Images, Springer-Verlag, Berlin (1983).Google Scholar
  6. 6.
    R.L. Gregory, Eye and Brain, World University Library, London (1974).Google Scholar
  7. 7.
    T.N. Cornsweet, Visual Perception, Academic Press, New York (1970).Google Scholar
  8. 8.
    P.A. Kolers, Reading Pictures—Some Cognitive Aspects of Perception, in: Picture Bandwidth Compression, T.S. Huang and O.J. Tretiak (eds.), Gordon and Breach, New York (1972).Google Scholar
  9. 9.
    D. Lines, Real-time histogram specification for ultrasound images, IEE Conf. Pub. 214, Electronic Image Processing, 11-15 (1982).Google Scholar
  10. 10.
    P.G. Roetling, Binary approximation of continuous tone images, Proc. SPSE Toronto Conf, 323-330 (1976).Google Scholar
  11. 11.
    A. Rose, Vision: Human and Electronic, Plenum Press, New York (1973).Google Scholar
  12. 12.
    B.R. Frieden, Image enhancement and restoration, in: Picture Processing and Digital Filtering, T. S. Huang (ed.), Springer-Verlag, Berlin (1976).Google Scholar
  13. 13.
    W.K. Pratt, Digital Image Processing, Chap. 12, Wiley, New York (1978).Google Scholar
  14. 14.
    Y. Yasuda, Y. Yamazaki, T. Kamae, and K. Kobayashi, Advances in FAX, Proc. IEE 73(4), 706–730 (April, 1985).CrossRefGoogle Scholar
  15. 15.
    W.K. Pratt, Digital Image Processing, Chap. 23, Wiley, New York (1978).Google Scholar
  16. 16.
    D. Lavie and W.K. Taylor, Effect of border variations due to spatial quantisation on binary image template matching, Electron. Lett. 18(10), 418–420 (May 1982).CrossRefGoogle Scholar
  17. 17.
    T. Pavlidis, Algorithms for shape analysis of contours and waveforms, IEEE Trans. Pattern Analysis and Machine Intelligence PAMI 1–2, 301–312 (1980).CrossRefGoogle Scholar
  18. 18.
    J-D. Dessimoz, Sampling and smoothing curves in digitised pictures, Proc. lst EUSIPCO, Lausanne (Sept. 1980).Google Scholar
  19. 19.
    J. Wilder, Application of a flexible pattern recognition system in industrial inspection, SPIE 182, 94–101 (1979).CrossRefGoogle Scholar
  20. 20.
    C.T. Zahn and R.Z. Roskies, Fourier descriptors for planar closed curves, IEEE Trans. Comps. 21, 269–281 (Mar. 1972).MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    B.G. Batchelor, Using concavity trees for shape description, Computers and Digital Techniques 2(4), 157–168 (1979).CrossRefGoogle Scholar
  22. 22.
    K. Nakata, Y. Nakano, and Y. Uchikura, Recognition of Chinese characters, in: Machine Perception of Patterns and Pictures, Inst. of Phys. Conf. Pub. 13, 45–52 (1972).Google Scholar
  23. 23.
    L. Norton-Wayne, W.J. Hill, and L. Finkelstein, Image enhancement and pre-processing, SPIE 130, 29–35 (1977).CrossRefGoogle Scholar
  24. 24.
    M.R. Teague, Image analysis via the general theory of moments, J. Opt. Soc. Am. 70(8), 920–930 (1980).MathSciNetCrossRefGoogle Scholar
  25. 25.
    G.A.W. West, Ph.D. Thesis, The City University, London (1983).Google Scholar
  26. 26.
    G.S. Robinson, Detection and coding of edges using spatial masks, SPIE 87, 117–125 (1976).CrossRefGoogle Scholar
  27. 27.
    S.W. Zucker, Region growing—Childhood and adolescence, CGIP 5, 382 (1976).Google Scholar
  28. 28.
    T. Peli and D. Malah, A study of edge detection algorithms, CGIP 20, 1 (1982).zbMATHGoogle Scholar
  29. 29.
    L. Kitchen and A. Rosenfeld, Edge evaluation using local edge coherence, SPIE 281, 284–298 (1981).CrossRefGoogle Scholar
  30. 30.
    R.O. Duda and P.E. Hart, Use of the Hough Transformation to detect lines and curves in pictures, Comm. of the ACM 15, 11–15 (Jan. 1972).CrossRefGoogle Scholar
  31. 31.
    I.G. Logan, and J.E.S. Macleod, An application of pattern recognition algorithms to the automatic inspection of steel strip surfaces, 2nd IJCPR Copenhagen, 286-290 (1974).Google Scholar
  32. 32.
    L. Norton-Wayne, W.J. Hill, and R.A. Brook, Automated visual inspection of moving steel surfaces, Brit. Jnl. of NDT 19(5), 242–248 (1977).Google Scholar
  33. 33.
    K.J. Stout, CD. Obray, and J. Jungles, Specification and control of surface finish— empiricism versus dogmatism, Opt. Eng. 24(3), 414–418 (1985).CrossRefGoogle Scholar
  34. 34.
    R.M. Haralick, K. Shunugam, and I. Dinstein, Texture features for image classification, IEEE Trans. SMC 3, 610–621 (1973).Google Scholar
  35. 35.
    J. Weszka and A. Rosenfeld, An application of texture analysis to materials inspection, Patt. Rec. 8, 195–199 (1976).CrossRefGoogle Scholar
  36. 36.
    O.H. Schade, Image Quality, RCA Corporation, Princeton (1975).Google Scholar
  37. 37.
    C.A. Rosen and G.L. Gleason, Evaluation of performance of machine vision systems, Robotics International, Paper MS 80-700 (1980).Google Scholar
  38. 38.
    L. Norton-Wayne and P. Saraga, A set of shapes for the benchmark testing of silhouette recognition systems, Proc. 4th Intl. Conf. Robot Vison and Sensory Controls, 65-74 (1984).Google Scholar

Copyright information

© Springer Science+Business Media New York 1986

Authors and Affiliations

  • Arthur Browne
    • 1
  • Leonard Norton-Wayne
    • 2
  1. 1.Philips Research LaboratoriesRedhill, SurreyEngland
  2. 2.Leicester PolytechnicLeicesterEngland

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