Computer Vision Basics

  • Mahdi Rezaei
  • Reinhard Klette
Chapter
Part of the Computational Imaging and Vision book series (CIVI, volume 45)

Abstract

In this chapter we present and discuss the basic computer vision concepts, techniques, and mathematical background that we use in this book. The chapter introduces image notations, the concept of integral images, colour space conversions, the Hough transform for line detection, camera coordinate systems, and stereo computer vision.

Bibliography

  1. 54.
    R.O. Duda, P.E. Hart, Use of the hough transformation to detect lines and curves in pictures. Commun. ACM 15, 11–15 (1972)CrossRefMATHGoogle Scholar
  2. 83.
    R. Haeusler, R. Klette, Disparity confidence measures on engineered and outdoor data, in Proceedings of the Iberoamerican Congress Pattern Recognition. LNCS 7441 (2012), pp. 624–631Google Scholar
  3. 88.
    S. Hermann, R. Klette, The naked truth about cost functions for stereo matching. MI-tech report 33, The University of Auckland (2009), www.mi.auckland.ac.nz/tech-reports/MItech-TR-33.pdf
  4. 89.
    S. Hermann, R. Klette, Iterative semi-global matching for robust driver assistance systems, in Proceedings of the Asian Conference on Computer Vision. LNCS 7726 (2012), pp. 465–478Google Scholar
  5. 90.
    H. Hirschmüller, Accurate and efficient stereo processing by semi-global matching and mutual information, in Proceedings of the IEEE Computer Vision Pattern Recognition, vol. 2 (2005) pp. 807–814Google Scholar
  6. 91.
    H. Hirschmüller, D. Scharstein, Evaluation of stereo matching costs on images with radiometric differences. IEEE Trans. Pattern Anal. Mach. Intell. 31, 1582–1599 (2009)CrossRefGoogle Scholar
  7. 112.
    W. Khan, V. Suaste, D. Caudillo, R. Klette, Belief propagation stereo matching compared to iSGM on binocular or trinocular video data, in Proceedings of the IEEE Intelligent Vehicles Symposium (2013), pp. 791–796Google Scholar
  8. 119.
    R. Klette, Concise Computer Vision: An Introduction into Theory and Algorithms (Springer, London, 2014)CrossRefMATHGoogle Scholar
  9. 168.
    S. Morales, R. Klette, A third eye for performance evaluation in stereo sequence analysis, in Proceedings of International Conference on Computer Analysis Images Patterns. LNCS 5702 (2009), pp. 1078–1086Google Scholar
  10. 247.
    J. Sun, N.N. Zheng, H.Y. Shum, Stereo matching using belief propagation. IEEE Trans. Pattern Anal. Mach. Intell. 25, 787–800 (2003)CrossRefMATHGoogle Scholar
  11. 269.
    P. Viola, M. Jones, Rapid object detection using a boosted cascade of simple features, in Proceedings of the IEEE Computer Vision Pattern Recognition, vol. 1 (2001), pp. 511–518Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mahdi Rezaei
    • 1
  • Reinhard Klette
    • 2
  1. 1.Department of Computer EngineeringQazvin Islamic Azad UniversityQazvinIran
  2. 2.Department of Electrical and Electronic EngineeringAuckland University of TechnologyAucklandNew Zealand

Personalised recommendations