Skip to main content


Performance Measure as Feedback Variable in Image Processing


This paper extends the view of image processing performance measure presenting the use of this measure as an actual value in a feedback structure. The idea behind is that the control loop, which is built in that way, drives the actual feedback value to a given set point. Since the performance measure depends explicitly on the application, the inclusion of feedback structures and choice of appropriate feedback variables are presented on example of optical character recognition in industrial application. Metrics for quantification of performance at different image processing levels are discussed. The issues that those metrics should address from both image processing and control point of view are considered. The performance measures of individual processing algorithms that form a character recognition system are determined with respect to the overall system performance.


  1. 1.

    Bailey S, Forshaw M, Hodgetts M: A methodology for goal oriented image processing-performance evaluation. Workshop on Performance Characterisation and Benchmarking of Vision Systems , January 1999, Canary Islands, Spain

  2. 2.

    Wenyin L, Zhai J, Dori D, Long T: A system for performance evaluation of arc segmentation algorithms. Procedings of 3rd Workshop on Empirical Evaluation Methods in Computer Vision (CVPR '01), December 2001, Kauai, Hawaii, USA

  3. 3.

    McCall JC, Trivedi MM: Performance evaluation of a vision based lane tracker designed for driver assistance systems. Computer Vision Robotics Research Laboratory, University of California , San Diego, Calif, USA; December 2004.

  4. 4.

    Palmer PL, Dabis H, Kittler J: A performance measure for boundary detection algorithms. Computer Vision and Image Understanding 1996, 63(3):476–494. 10.1006/cviu.1996.0036

  5. 5.

    Mirmehdi M, Palmer PL, Kittler J: Optimising the complete image feature extraction chain. Proceedings of the 3rd Asian Conference on Computer Vision (ACCV '98), January 1997, Hong Kong 2: 307–314.

  6. 6.

    Ogata K: Modern Control Engineering. Prentice-Hall, London, UK; 2002.

  7. 7.

    Corke P: Visual Control of Robots: High Performance Visual Servoing. Research Studies Press, Taunton, UK; Wiley, New York, NY, USA; 1996.

  8. 8.

    Jähne B, Haußecker H, Geißler P (Eds): Handbook of Computer Vision and Applications-Vol.3. Systems and Applications. Academic Press, London, UK; 1999.

  9. 9.

    Nair D, Wenzel L, Barp A, Siddiqi A: Control strategies and image processing. Proceedings of the 7th International Symposium on Signal Processing and Its Applications (ISSPA '03), July 2003, Paris, France 557–560.

  10. 10.

    Gräser A, Ivlev O, Ristić D: First experiences with feedback structures in image processing. Procedings of 24th Colloquium of Automation, 2002, Salzhausen, Germany 98–109.

  11. 11.

    Kriegman D, Hager GD, Morse AS (Eds): The Confluence of Vision and Control. Springer, New York, NY, USA; 1998.

  12. 12.

    Mirmehdi M, Palmer PL, Kittler J, Dabis H: Feedback control strategies for object recognition. IEEE Transactions on Image Processing 1999, 8(8):1084–1101. 10.1109/83.777089

  13. 13.

    Ristić D, Volosyak I, Gräser A: Feedback Control in Image Processing. atp international-automation technology in practice 2005, (1):61–70.

  14. 14.

    Peng J, Bhanu B: Closed-loop object recognition using reinforcement learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998, 20(2):139–154. 10.1109/34.659932

  15. 15.

    Marchant JA: Testing a measure of image quality for acquisition. Image and Vision Computing 2002, 20(7):449–458. 10.1016/S0262-8856(01)00088-9

  16. 16.

    J. Racky J, Pandit M: Active Illumination for the segmentation of surface deformations. International Conference on Image Processing (ICIP '99), October 1999, Kobe, Japan 1:

  17. 17.

    Jähne B: Practical Handbook on Image Processing for Scientific Applications. CRC Press LLC, Boca Raton, Fla, USA; 1997.

  18. 18.

    Volosyak I, Kouzmitcheva O, Ristić D, Gräser A: Improvement of visual perceptual capabilities by feedback structures for robotic system FRIEND. IEEE Transactions on Systems, Man, and Cybernetics—Part C 2005, 35(1):66–74.

  19. 19.

    Demant C, Streicher-Abel B, Waszkewitz P, Strick M: Industrial Image Processing-Visual Quality Control in Manufacturing. Springer, Berlin, Germany; 1999.

  20. 20.

    Mori S, Nishida H, Yamada H: Optical Character Recognition. John Wiley & Sons, New York; 1999.

  21. 21.

    Maitre H, Zinn-Justin J: Entropy, information and image. In Progress in Picture Processing. Springer, North Holland, Amsterdam; 1996:81–115.

  22. 22.

    Gonzalez RC, Woods RE: Digital Image Processing. Prentice-Hall, Englewood Cliffs, NJ, USA; 2002.

  23. 23.

    Rahman CA, Badawy W, Radmanesh A: A real time vehicle's license plate recognition system. Proceedings of IEEE Conference on Advanced Video and Signal Based Surveillance, July 2003, Miami, Fla, USA 163–166.

  24. 24.

    Abutaleb AS: Automatic thresholding of gray-level pictures using two-dimensional entropy. Computer Vision, Graphics and Image Processing 1989, 47(1):22–32. 10.1016/0734-189X(89)90051-0

  25. 25.

    Sezgin M, Sankur B: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 2004, 13(1):146–165. 10.1117/1.1631315

  26. 26.

    Paulus DWR, Hornegger J: Pattern Recognition and Image Processing in C++. F. Vieweg & Sohn, Braunschweig/Wiesbaden, Germany; 1995.

Download references

Author information

Correspondence to Danijela Ristić.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Ristić, D., Gräser, A. Performance Measure as Feedback Variable in Image Processing. EURASIP J. Adv. Signal Process. 2006, 027848 (2006).

Download citation


  • Image Processing
  • Information Technology
  • Control Point
  • Quantum Information
  • Recognition System