Skip to main content
SpringerLink
Log in

Image acquisition using aperture controladapted to spatio-temporal properties

  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract.

Image processing strongly relies on the quality of the input images, as images of appropriate quality can significantly decrease the development effort for image processing and computer vision algorithms. A flexible acquisition system for image enhancement, which is able to operate in real time under changing brightness conditions, is suggested. The system is based on controlling the aperture of the acquisition camera lens, which makes it useable in combination with all types of image sensors. The control scheme is based on an adaptive image quality estimator and can be used to enhance a variety of spatio-temporal properties. Those properties are either characterized by a time-varying or spatial characteristic, or both, i.e. spatio-temporal characteristics of the imaged scene. A region of interest is derived from the more abstract spatio-temporal property. We present results for aperture control adapted to regions of interest characterized by 2D and 3D spatio-temporal properties. We investigate control implemented in software and aimed towards different spatio-temporal properties. Hardware configuration and real-time acquisition capability for static and dynamic changing image contents is demonstrated, and adaptation time and improvement of image quality are measured and compared.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Jain AK (1989) Fundamentals of digital image processing, chapt 7: Image enhancement. Prentice-Hall, Upper Saddle River, NJ, pp 233-266

  2. Kuno T, Sugiura H, Matoba N (1998) A new automatic exposure system for digital still cameras. IEEE Trans Consumer Electron 44(1):192-199

    Article  Google Scholar 

  3. Takahashi K, Kyuma K, Tamura K, Tsuda Y (1998) Image pickup device using plural control parameters for exposure control. US Patent US5831676

  4. Riggs NGP (1993) Automatic control of television camera iris. UK Patent GB2259423

  5. Kurashige T, Imaide T, Tarumizu H (1994) Auto-iris method and apparatus for imaging device. US Patent US5315394

  6. Hwang MS, Lee GT, Hwang SH, Kim GD, Kim HS, Sim GH (2000) Auto iris control circuit in digital camera. Korean Patent KR247660

  7. Marchant JA, Onyango CM (2003) Model-based control of image acquisition. Image Vision Comput 21:161-170

    Article  Google Scholar 

  8. Nayar SK, Mitsunaga T (2000) High dynamic range imaging: spatially varying pixel exposures. In: Proceedings of the conference on computer vision and pattern recognition, Hilton Head Island, SC

  9. Schrey O, Huppertz J, Filimonovic G, Bußmann A, Brockherde W, Hosticka BJ (2001) A 1K\(\times\)1K high dynamic range CMOS image sensor with on-chip programmable region of interest readout. In: Proceedings of the European conference on solid-state circuits, Villach, Austria

  10. Robertson MA, Borman S, Stevenson RL (1999) Dynamic range improvement through multiple exposures. In: Proceedings of the international conference on image processing, Kobe, Japan

  11. Huber R, Nowak C, Spatzek B, Schreiber D (2002) Adaptive aperture control for video acquisition. In: Proceedings of the IEEE workshop on applications of computer vision, Orlando, FL, pp 320-324

  12. Haruki T, Kikuchi K (1992) Video camera system using fuzzy logic. IEEE Trans Consumer Electron 38(3):624-634

    Article  Google Scholar 

  13. Imaide T, Kurashige T, Takagi Y, Tarumizu H (1992) A 9:16 video camera with scene-adaptive intelligent control. IEEE Trans Consumer Electron 38(3):601-606

    Article  Google Scholar 

  14. Jain AK (1989) Fundamentals of digital image processing, chap 3.9: Color coordinate systems, Prentice-Hall, Upper Saddle River, NJ, pp 66-71

  15. Martin J, Crowley J (1995) Comparison of correlation techniques. In: Proceedings of Intelligent Autonomous Systems, Karlsruhe, Germany, pp 86-93

  16. Shannon CE (1948) A mathematical theory of communication. Bell Sys Tech J 27:379-423; 623-656

    MathSciNet  Google Scholar 

  17. Marchant JA (2002) Testing a measure of image quality for acquisition control. Image Vision Comput 20:449-458

    Article  Google Scholar 

  18. Murino V, Foresti GL, Regazzoni CS (1996) Adaptive camera regulation for investigation of real scenes. IEEE Trans Ind Electron 43(5):588-600

    Article  Google Scholar 

  19. Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79-86

    MathSciNet  MATH  Google Scholar 

  20. Huber PJ (1981) Robust statistics, chap 1: Generalities. Wiley, New York, pp 1-19

  21. Unbehauen H (2000) Regelungstechnik III, chap 5: Adaptive Regelsysteme, 6th edn. Vieweg, Braunschweig/Wiesbaden, Germany, pp 133-261

  22. Förstner W (1996) 10 pro’s and con’s against performance characterization of vision algorithms. In: Proceedings of the workshop on performance characteristics of vision algorithms, Cambridge, UK

  23. Huber R, Nowak C, Spatzek B, Schreiber D (2002) Reliable detection of obstacles on staircases. In: Proceedings of the IAPR workshop on machine vision applications, Nara, Japan, pp 467-479

  24. Otsu N (1979) A threshold selection method from grey-level histograms. IEEE Trans Sys Man Cybern Part B Cybern 9(1):62-66

    Google Scholar 

  25. Rosenfeld A (1983) On the connectivity properties of grayscale pictures. Pattern Recog 16(1):47-50

    Article  Google Scholar 

  26. Lowe DG (1991) Fitting parameterized 3-D models to images. IEEE Trans Pattern Anal Mach Intell 13(5):441-450

    Article  Google Scholar 

  27. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679-698

    Google Scholar 

  28. Duda RO, Hart PE (1972) Use of the Hough transformation to detect lines and curves in pictures. Commun ACM 15(1):11-15

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Advanced Computer Vision GmbH - ACV, Tech Gate Vienna, Donau-City-Straße 1, 1220, Vienna, Austria

    Reinhold Huber, Christoph Nowak & Bernhard Spatzek

Authors
  1. Reinhold Huber
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christoph Nowak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bernhard Spatzek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reinhold Huber.

Additional information

Received: 30 August 2003, Accepted: 17 May 2004, Published online: 20 August 2004

This work was carried out within the K plus Competence Center ADVANCED COMPUTER VISION and was funded from the K plus program.

We thank Professor Walter Kropatsch for critical comments and fruitful discussions on the paper content and methodology.

Austrian patent granted under no. A 705/2002.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Huber, R., Nowak, C. & Spatzek, B. Image acquisition using aperture controladapted to spatio-temporal properties. Machine Vision and Applications 15, 204–215 (2004). https://doi.org/10.1007/s00138-004-0154-5

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-004-0154-5

Keywords:

Search

Navigation