QT – Prototyping Image Processing System

  • Bruce G. BatchelorEmail author
  • Simon J. Caton
Reference work entry


QT is the latest in a series of interactive image processing tool-kits for prototype development of Machine Vision systems. Experience gained over more than 35 years has shown that a command-line system with short mnemonic function names is an effective tool for developing vision algorithms. Parameter lists are also required for some functions. When the user types a command, such as neg (negate), thr(89,127) (threshold) or lpf (low-pass filter, blur), the operation is performed immediately and the result is displayed, together with the image before processing. When running on a modern computer, typical response times for processing an image of good resolution are rarely more that one second. Measurements are obtained in an obvious way. For example, to count the number of distinct objects (blobs) in a binary image, the user types x = cbl, while the command y = avg computes the average intensity in a grey-scale image. Binary, grey-scale and colour images are all processed by QT, which is built on top of MATLAB™. The advantage of QT over MATLAB lies in its greater ease of use. The command names are short but most importantly, the user does not have to keep track of source and destination images. QT always processes the image displayed on the left of the computer screen. It then replaces this with the result of the operation selected by the user and displays the input image for the last processing step on the right. In addition, to image processing commands, QT provides facilities for a range of other operations, including image capture from local or remote cameras, controlling external devices, speech synthesis, on-line documentation, consulting the Catalogue of Lighting-Viewing methods ( Chap. 40). Any MATLAB function can be invoked from QT and programming new functions is straightforward. Many of the illustrations in the book were prepared using QT and several chapters exploit its ability to express image processing algorithms succinctly. Since it is a prototyping tool, QT is not fast enough for any but the least demanding factory applications. However, it is a powerful exploratory tool in the hands of an experienced user. On numerous occasions, QT or one of its predecessors, has been used to discover/invent algorithms very quickly in response to new visual inspection and control applications. QT has been interfaced to Prolog to produce an even more powerful language (PQT) that facilitates intelligent reasoning about images ( Chap. 23).


Binary Image Current Image Serial Port Alternate Image Machine Vision System 
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.

Further Reading

  1. 1.
    Batchelor BG (1979) Interactive image analysis as a prototyping tool for industrial inspection. IEE PROC-E 2(2):61–69MathSciNetGoogle Scholar
  2. 2.
    Batchelor BG (1986) Merging the autoview image processing language with prolog. Image Vision Comput 4(4):189–196CrossRefGoogle Scholar
  3. 3.
    Batchelor BG (1991) Intelligent image processing in prolog. Springer, Berlin. ISBN 0-540-19647-1zbMATHCrossRefGoogle Scholar
  4. 4.
    Batchelor BG, Hack R (1995) Robot vision system programmed in Prolog. In: Proceedings of the Conference on Machine Vision Applications, Architectures and Systems IV, Philadelphia, October 1995, SPIE, Bellingham, vol 2597, ISBN 0-8194-1961-3, pp 239–252Google Scholar
  5. 5.
    Batchelor BG, Waltz FM (1993) Interactive image processing. Springer, New York. ISBN 3-540-19814-8CrossRefGoogle Scholar
  6. 6.
    Batchelor BG, Waltz FM (2001) Intelligent machine vision. Springer, New York. ISBN 3-540-76224-8zbMATHCrossRefGoogle Scholar
  7. 7.
    Batchelor BG, Whelan PF (1997) Intelligentvision systems for industry. Springer, London. ISBN 3-540-19969 1CrossRefGoogle Scholar
  8. 8.
    Batchelor BG, Mott DH, Page GJ, Upcott DN (1982) The Autoview interactive image processing facility. In: Jones NB (ed) Digital signal processing. Peter Peregrinus, London, pp 319–351. ISBN 0 906048 91 5Google Scholar
  9. 9.
    Batchelor BG, Daley MW, Griffiths EC (1994) Hardware and software for prototyping industrial vision systems. In: Proceedings of the Conference on Machine Vision Applications, Architectures and Systems Integration III, Boston, October 1994, pub. SPIE, Bellingham, vol 2347, ISBN 0-8194-1682-7, pp 189–197Google Scholar
  10. 10.
    Batchelor BG, Griffiths EC, Hack R, Jones AC (1996) Multi-media extensions to prototyping software for machine vision. In: Proceedings of the Conference on Machine Vision Applications, Architectures and Systems VI, Boston, November 1996, SPIE, Bellingham, vol 2908, pp 259–273Google Scholar
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    Batchelor BG, Hack R, Jones AC (1996) Prolog-based prototyping software for machine vision. In: Proceedings of the Conference on Machine Vision Applications, Architectures and Systems VI, Boston, November 1996, SPIE, Bellingham, vol 2908, pp 234–248Google Scholar
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    Batchelor BG, Daley MW, Hitchell RJ, Hunter GJ, Jones GE, Karantalis G (1999) Remotely operated prototyping environment for automated visual inspection. In: Proceedings of the IMVIP99 – Irish Machine Vision and Image Processing Conference 1999, Dublin City University, Dublin, September 1999, pub Irish Pattern Recognition and Classification Society, ISBN 1 872 327 22 2, pp 300–320Google Scholar
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    Caton SJ, Rana OF, Batchelor BG (2009) Distributed image processing over an adaptive campus grid. Concurrency Comput-Prac Ex 21(3):321–336CrossRefGoogle Scholar
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    Chatburn LT, Daley MW, Batchelor BG (2004) Myriad: a framework for distributed and networked vision systems, two- and three-dimensional vision systems for inspection, control, and metrology. In: Batchelor BG, Hügli H (eds) Proceedings of the SPIE, vol 5265, pp 98–109Google Scholar
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    Jones AC, Hack R, Batchelor BG (1996) Software organisation for a Prolog-based prototyping system for machine vision. In: Proceedings of the Conferecne on Application of Digital Image Processing XIX, Denver, August 1996, pub. SPIE, Bellingham, vol 2847, ISBN 0-8194-2235-5, pp 16–27Google Scholar
  16. 16.
    Karantalis G, Batchelor BG (1998) Prototyping machine vision software on the World Wide Web. In: Proceedings of the SPIE Conference on Machine Vision Systems for Inspection and Metrology VII, Boston, November 1998, vol 3521, pp 3014–31, ISBN 0-8194-2982-1Google Scholar
  17. 17.
    Suau P, Pujol M, Rizo R, Caton SJ, Rana OF, Batchelor BG, Pujol F (2005) Agent-based recognition of facial expressions. In: Proceedings of the 4th International Joint Conference on Autonomous Agents and Multi-agent Systems, Utrecht, 2005, ISBN: 1-59593-093-0, pp 161–162Google Scholar
  18. 18.
    Whelan PF, Batchelor BG, Lewis MRF, Hack R (1998) Machine vision design and training aids on the World Wide Web. Vision 14(2):1–6Google Scholar

Copyright information

© Springer-Verlag London Ltd. 2012

Authors and Affiliations

  1. 1.School of Computer Science and InformaticsCardiff UniversityCardiffUK
  2. 2.Karlsruher Institut für Technologie (KIT)Karlsruher Institut für Technologie (KIT)KarlsruheGermany

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