Skip to main content
SpringerLink
Log in

Image Understanding: Towards Universal Capability

  • Chapter
Applied Image Processing

Part of the book series: Macmillan New Electronics Series

  • 85 Accesses

Abstract

A general-purpose machine vision system must be flexible in the sense that it should be able to operate in virtually unconstrained environments containing ill-defined objects which partially occlude one another. Thus the image analysis descriptions of two-dimensional (2-D) relationships must be enriched and extended to include the three-dimensional (3-D) relationships between objects within a real-world scene.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. R. T. Chin and C. R. Dyer, ‘Model based recognition in robot vision’, ACM Computing Surveys, 18(1), pp. 67–108, 1986.

    Article  Google Scholar 

  2. H. G. Barrow and J. M. Tenenbaum, ‘Recovering intrinsic scene characteristics from images’, in A. R. Hanson and E. M. Riseman (Eds.), Computer Vision Systems, pp. 3–26, Academic Press, New York, 1978.

    Google Scholar 

  3. D. Marr, Vision, Freeman, Oxford, 1982.

    Google Scholar 

  4. B. K. P. Horn, ‘Artificial intelligence and the science of image understanding’, in G. G. Dodds and L. Rossol (Eds), Computer Vision and Sensor Based Robots, Plenum Press, New York, 1979.

    Google Scholar 

  5. J. Beck, Organisation and Representation in Perception, Lawrence Erlbaum Associates, London, 1982.

    Google Scholar 

  6. I. Biederman, ‘Recognition-by-components: a theory of human image understanding’, Psychological Review, 94(2), pp. 115–147, 1987.

    Article  Google Scholar 

  7. D. G. Lowe, ‘Three dimensional object recognition from two-dimensional images’, Artificial Intelligence, 31, pp. 355–395, 1987.

    Article  Google Scholar 

  8. D. Marr and H. K. Nishihara, ‘Visual information processing: artificial intelligence and the sensorium of sight’, Technology Review, 81(1), pp. 2–23, MIT Alumni Assoc., 1978.

    Google Scholar 

  9. M. Brady, ‘Preface — the changing shape of computer vision’, Artificial Intelligence, 17(1–3), pp. 1–15, August 1981.

    Article  Google Scholar 

  10. D. Marr and T. Poggio, ‘A computational theory of human stereo vision’, Proc. Royal Society, B204, pp. 301–328, 1979.

    Article  Google Scholar 

  11. S. Ullman, The Interpretation of Visual Motion, MIT Press, Cam-bridge, Massachusetts, 1979.

    Google Scholar 

  12. A. P. Witkin, ‘Recovering surface shape and orientation from texture’, Artificial Intelligence, 17, pp. 17–45, August 1981.

    Article  Google Scholar 

  13. A. P. Pentland, ‘Local shading analysis’, IEEE Trans. Pattern Analysis and Machine Intelligence, 6(2), pp. 179–187, 1984.

    Google Scholar 

  14. T. O. Binford, ‘Visual perception by a computer’, IEEE Conf. Systems and Control, Miami, 1971.

    Google Scholar 

  15. R. A. Brooks, ‘Model-based three-dimensional interpretations of two-dimensional images’, IEEE Trans. Pattern Analysis and Machine Intelligence, 5(2), pp. 140–149, 1983.

    Article  Google Scholar 

  16. D. T. Morris and P. Quarendon, ‘An algorithm for direct display of CSG objects by spatial subdivision’, in R. A. Earnshaw (Ed.), NATO ASI F-17: Fundamental Algorithms for Computer Graphics, Springer-Verlag, Berlin, 1985.

    Google Scholar 

  17. D. H. Ballard and C. M. Brown, Computer Vision, Chapter 9, Prentice-Hall, Englewood Cliffs, New Jersey, 1982.

    Google Scholar 

  18. T. M. Silberberg, ‘Multiresolution aerial image interpretation’, Proc. DARPA Image Understanding Workshop, Vol. 2, pp. 505–511, Materials Research Society, Pittsburgh, Pennsylvania, 1988.

    Google Scholar 

  19. S. J. Cosby and R. Thomas, ‘IRS: a hierarchical knowledge based system for aerial image interpretation’, Proc. 3rd Int. Conf. IEA/AIE, pp. 207–215, 1990.

    Google Scholar 

  20. W. E. L. Grimson and T. Lozano-Perez, ‘Model based recognition and localization from sparse range or tactile data’, Int. Journal of Robotics Research, No. 3, pp. 3–35, 1984.

    Google Scholar 

  21. N. Ayache and O. D. Faugeras, ‘HYPER: a new approach for the recognition and positioning of two-dimensional objects’, IEEE Trans. Pattern Analysis and Machine Intelligence, 8, 1986.

    Google Scholar 

  22. J. Porril, S. B. Pollard et al., ‘TINA: a 3-D vision system for pick and place’, Proc. 3rd AVC, Cambridge, UK, pp. 65–72, 1987.

    Google Scholar 

  23. S. B. Pollard, J. E. W. Mayhew and J. P. Frisby, ‘PMF: a stereo correspondence algorithm using a disparity gradient’, Perception, 14, pp. 449–470, 1985.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Brighton, UK

    G. J. Awcock & R. Thomas

Authors
  1. G. J. Awcock
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

Copyright information

© 1995 G.J. Awcock and R. Thomas

About this chapter

Cite this chapter

Awcock, G.J., Thomas, R. (1995). Image Understanding: Towards Universal Capability. In: Applied Image Processing. Macmillan New Electronics Series. Palgrave, London. https://doi.org/10.1007/978-1-349-13049-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-349-13049-8_8

  • Publisher Name: Palgrave, London

  • Print ISBN: 978-0-333-58242-8

  • Online ISBN: 978-1-349-13049-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation