Skip to main content
SpringerLink
Log in

Introduction

  • Chapter
Image Processing, Analysis and Machine Vision

  • 2029 Accesses

Abstract

Vision allows humans to perceive and understand the world surrounding them. Computer vision aims to duplicate the effect of human vision by electronically perceiving and understanding an image. Giving computers the ability to see is not an easy task — we live in a three-dimensional (3D) world, and when computers try to analyse objects in 3D space, available visual sensors (e.g. TV cameras) usually give two-dimensional (2D) images, and this projection to a lower number of dimensions incurs an enormous loss of information. Dynamic scenes such as those to which we are accustomed, with moving objects or a moving camera, make computer vision even more complicated.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. J K Aggarwal, and W Martin. Motion Understanding. Kluwer Academic Publishers, Boston, Ma, 1988.

    Google Scholar 

  2. Y Aloimonos, and D Shulman Integration of Visual Modules - An Extension of the Marr Paradigm. Academic Press, New York, 1989.

    MATH  Google Scholar 

  3. T Appenzeller; Ozone loss hits us where we live. Science, 254: 645, November 1991.

    Article  Google Scholar 

  4. D H Ballard, and C M Brown; Computer Vision. Prentice-Hall, Englewood Cliffs, NJ, 1982.

    Google Scholar 

  5. R H T Bates, and M J McDonnell; Image Restoration and Reconstruction. Clarendon Press, Oxford, England, 1986.

    Google Scholar 

  6. R D Boyle, and R C Thomas; Computer Vision: A First Course. Blackwell Scientific, 1988.

    Google Scholar 

  7. K R Castleman; Digital Image Processing. Prentice-Hall, Englewood Cliffs, NJ, 1979.

    Google Scholar 

  8. E R Dougherty, and C R Giardina; Image Processing - Continuous to Discrete, Vol.1. Prentice-Hall, Englewood Cliffs, NJ, 1987.

    Google Scholar 

  9. E R Dougherty, and C R Giardina: Structured Image Processing. Prentice-Hall, Englewood Cliffs, NJ, 1987.

    Google Scholar 

  10. R O Duda, and P E Hart: Pattern Classification and Scene Analysis. John Wiley and Sons, New York, 1973.

    MATH  Google Scholar 

  11. M B J Duff, and S Levialdi, editors. Languages and Architectures for Image Procesing. Academic Press, New York, 1981.

    Google Scholar 

  12. M P Ekstrom: Digital Image Processing Techniques. Academic Press, New York, 1984.

    Google Scholar 

  13. M C Fairhurst: Computer Vision for Robotic Systems: An Introduction. Prentice-Hall, Englewood Cliffs, NJ, 1988.

    Google Scholar 

  14. K S Fu: Syntactic Methods in Pattern Recognition. Academic Press, New York, 1974.

    MATH  Google Scholar 

  15. K S Fu: Syntactic Pattern Recognition — Applications. Springer Verlag, Berlin, 1977.

    Book  MATH  Google Scholar 

  16. K S Fu: Syntactic Pattern Recognition and Applications. Prentice-Hall, Englewood Cliffs, NJ, 1982.

    MATH  Google Scholar 

  17. C R Giardina, and E R Dougherty: Morphological Methods in Image and Signal Processing. Prentice-Hall, Englewood Cliffs, NJ, 1988.

    Google Scholar 

  18. R C Gonzalez, and M G Thomason: Syntactic Pattern Recognition: An Introduction. Addison-Wesley, Reading, Ma, 1978.

    Google Scholar 

  19. R C Gonzalez, and P Wintz: Digital Image Processing. Addison-Wesley, Reading, Ma, 1977.

    Google Scholar 

  20. R C Gonzalez, and P Wintz. Digital Image Processing. Addison-Wesley, Reading, Ma, 2nd edition, 1987.

    Google Scholar 

  21. R C Gonzalez, and R E Woods: Digital Image Processing. Addison-Wesley, Reading, Ma, 1992.

    Google Scholar 

  22. E L Hall: Computer Image Processing and Recognition. Academic Press, San Diego-New York, 1979.

    MATH  Google Scholar 

  23. A R Hanson, and E M Riseman, editors. Computer Vision Systems. Academic Press, New York, 1978.

    Google Scholar 

  24. R M Haralick, and L G Shapiro: Computer and Robot Vision, Volume I. Addison-Wesley, Reading, Ma, 1992.

    Google Scholar 

  25. R M Haralick, and L G Shapiro: Computer and Robot Vision, Volume II. Addison-Wesley, Reading, Ma, 1993.

    Google Scholar 

  26. B K P Horn: Robot Vision. MIT Press, Cambridge, Ma, 1986.

    Google Scholar 

  27. B K P Horn, and M J Brooks, editors. Shape from Shading. MIT Press, Cambridge, Ma, 1989.

    Google Scholar 

  28. A K Jain: Fundamentals of Digital Image Processing. Prentice-Hall, Englewood Cliffs, NJ, 1989.

    MATH  Google Scholar 

  29. R Kasturi, and R C Jain, editors. Computer Vision. IEEE Computer Press, Los Alamitos, Ca, 1991.

    Google Scholar 

  30. J S Lim: Two-Dimensional Signal and Image Processing. Prentice-Hall, Englewood Cliffs, NJ, 1990.

    Google Scholar 

  31. A Low: Introductory Computer Vision and Image Processing. McGraw Hill, 1991.

    Google Scholar 

  32. D Marr: Vision — A Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman and Co., San Francisco, 1982.

    Google Scholar 

  33. J L Mundy, and A Zisserman: Geometric Invariance in Computer Vision. MIT Press, Cambridge, Ma; London, 1992.

    Google Scholar 

  34. D W Murray, and B F Buxton: Experiments in the Machine Interpretation of Visual Motion. MIT Press, Cambridge, Ma, 1990.

    Google Scholar 

  35. M Nagao, and T Matsuyama: A Structural Analysis of Complex Aerial Photographs. Plenum Press, New York, 1980.

    Google Scholar 

  36. A N Netravali: Digital Pictures: Representation and Compression. Plenum Press, New York, 1988.

    Book  Google Scholar 

  37. H Niemann: Pattern Analysis and Understanding. Springer Verlag, Berlin-New York-Tokyo, 2nd edition, 1990.

    Google Scholar 

  38. N J Nilsson: Principles of Artificial Intelligence. Springer Verlag, Berlin, 1982.

    Book  MATH  Google Scholar 

  39. T Pavlidis: Structural Pattern Recognition. Springer Verlag, Berlin, 1977.

    MATH  Google Scholar 

  40. T Pavlidis: Algorithms for Graphics and Image Processing. Computer Science Press, New York, 1982.

    Book  Google Scholar 

  41. A P Pentland, editor. From Pixels to Predicates. Ablex Publishing Corporation, Norwood, NJ, 1986.

    Google Scholar 

  42. V K Prasanna Kumar: Parallel Architectures and Algorithms for Image Understanding. Academic Press, Boston, Ma, 1991.

    Google Scholar 

  43. W K Pratt: Digital Image Processing. John Wiley and Sons, New York, 1978.

    Google Scholar 

  44. W K Pratt: Digital Image Processing. John Wiley and Sons, New York, 2nd edition, 1991.

    Google Scholar 

  45. M Rabbani: Digital Image Compression. SPIE Optical Engineering Press, Bellingham, Wa, 1991.

    Google Scholar 

  46. A Rosenfeld: Picture Languages — Formal Models for Picture Recognition. Academic Press, New York, 1979.

    MATH  Google Scholar 

  47. A Rosenfeld, editor. Multiresolution Image Processing and Analysis. Springer Verlag, Berlin, 1984.

    MATH  Google Scholar 

  48. A Rosenfeld, and A C Kak: Digital Picture Processing. Academic Press, New York, 1st edition, 1976.

    Google Scholar 

  49. A Rosenfeld, and A C Kak: Digital Picture Processing. Academic Press, New York, 2nd edition, 1982.

    Google Scholar 

  50. J Serra: Image Analysis and Mathematical Morphology. Academic Press, London, 1982.

    MATH  Google Scholar 

  51. J C Simon, editor. From Pixels to Features: Proceedings of a Workshop held at Bonas, France, 22–27 August, 1988. Elsevier, 1989.

    Google Scholar 

  52. F Tomita, and S Tsuji: Computer Analysis of Visual Textures. Kluwer Academic Publishers, Norwell, Ma, 1990.

    Google Scholar 

  53. H Wechsler: Computational Vision. Academic Press, London — San Diego, 1990.

    Google Scholar 

  54. Y T Zhou: Artificial Neural Networks for Computer Vision. Springer Verlag, New York, 1992.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Iowa, Iowa City, Iowa, USA

    Milan Sonka PhD

  2. Czech Technical University, Prague, Czech Republic

    Vaclav Hlavac PhD

  3. University of Leeds, Leeds, England

    Roger Boyle DPhil, MBCS, CEng

Authors
  1. Milan Sonka PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vaclav Hlavac PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roger Boyle DPhil, MBCS, CEng
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 1993 Milan Sonka, Vaclav Hlavac and Roger Boyle

About this chapter

Cite this chapter

Sonka, M., Hlavac, V., Boyle, R. (1993). Introduction. In: Image Processing, Analysis and Machine Vision. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-3216-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4899-3216-7_1

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-412-45570-4

  • Online ISBN: 978-1-4899-3216-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation