Machine Vision pp 685-720 | Cite as


  • Jürgen BeyererEmail author
  • Fernando Puente León
  • Christian Frese


The term detection refers to the recognition of known or unknown objects in an image and to the determination of their position and orientation. On the one hand, the objects that are to be detected can be test objects, whose presence, orientation or integrity has to be inspected. On the other hand, it might be necessary to detect defects or certain structures such as, e.g., features, in the image.


Local Binary Pattern Interest Point Impulse Response Function Optical Character Recognition Automate Visual Inspection 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Dana Ballard. Generalizing the Hough Transform to Detect Arbitrary Shapes. Pattern Recognition, 13(2):111–122, 1981.CrossRefGoogle Scholar
  2. [2]
    Jürgen Beyerer. Analyse von Riefentexturen. PhD thesis, Universität Karlsruhe (TH), 1994.Google Scholar
  3. [3]
    Jürgen Beyerer and Fernando Puente León. Die Radontransformation in der digitalen Bildverarbeitung. Automatisierungstechnik, 50(10):472–480, 2002.Google Scholar
  4. [4]
    Jürgen Brauer. Human Pose Estimation with Implicit Shape Models. PhD thesis, Karlsruhe Institute of Technology, 2014.Google Scholar
  5. [5]
    Ward Cheney. Analysis for applied mathematics. Springer, 2001.Google Scholar
  6. [6]
    Richard Duda and Peter Hart. Use of the Hough transformation to detect lines and curves in pictures. Communications of the ACM, 15(1):11–15, 1972.CrossRefzbMATHGoogle Scholar
  7. [7]
    Rafael Gonzalez and RichardWoods. Digital image processing. Pearson Prentice Hall, 3rd edition, 2008.Google Scholar
  8. [8]
    Martin Grafmüller, Jürgen Beyerer, and Kristian Kroschel. Decision Tree Classifier for Character Recognition Combining Support Vector Machines and Artificial Neural Networks. In Mathematics of Data/Image Coding, Compression, and Encryption with Applications, Proc. SPIE, volume 7799, San Diego, USA, August 2010.Google Scholar
  9. [9]
    Graham Hall, Trevor Terrel, John Senior, and Lesley Murphy. New fast discrete Radon transform for enhancing linear features in noisy images. Electronics Letters, 24(14):876–877, 1988.CrossRefGoogle Scholar
  10. [10]
    Robert Haralick and Linda Shapiro. Computer and robot vision. Addison-Wesley, 1992.Google Scholar
  11. [11]
    Chris Harris and Mike Stephens. A combined corner and edge detector. In Proceedings of the 4 th Alvey Vision Conference, pages 147–151, 1988.Google Scholar
  12. [12]
    Peter Hart. How the Hough Transform Was Invented. IEEE Signal Processing Magazine, pages 18–22, 2009.Google Scholar
  13. [13]
    Marko Heikkilä, Matti Pietikäinen, and Cordelia Schmid. Description of interest regions with local binary patterns. Pattern Recognition, 42(3):425–436, 2009.CrossRefzbMATHGoogle Scholar
  14. [14]
    Harro Heuser. Funktionalanalysis – Theorie und Anwendung. Teubner, 4th edition, 2006.Google Scholar
  15. [15]
    Joseph Horner and Peter Gianino. Phase-only matched filtering. Applied Optics, 23(6):812–816, March 1984.CrossRefGoogle Scholar
  16. [16]
    Paul Hough. Method and means for recognizing complex patterns. Patent US 3069654, 1962.Google Scholar
  17. [17]
    Kai Jüngling. Ein generisches System zur automatischen Detektion, Verfolgung und Wiedererkennung von Personen in Videodaten. PhD thesis, Karlsruhe Institute of Technology, 2011.Google Scholar
  18. [18]
    Avinash Kak and Malcolm Slaney. Principles of computerized tomographic imaging. Society for Industrial and Applied Mathematics, 2001.Google Scholar
  19. [19]
    Willi Kalender. Computed tomography: fundamentals, system technology, image quality, applications. Publicis, 3rd edition, 2011.Google Scholar
  20. [20]
    A. Kassim, T. Tan, and K. Tan. A comparative study of efficient generalised Hough transform techniques. Image and Vision Computing, 17(10):737–748, 1999.CrossRefGoogle Scholar
  21. [21]
    Violet Leavers. Shape detection in computer vision using the Hough transform. Springer, 1992.Google Scholar
  22. [22]
    Alain Lehmann, Bastian Leibe, and Luc Van Gool. Fast PRISM: Branch and Bound Hough Transform for Object Class Detection. International Journal of Computer Vision, 94(2):175–197, 2011.CrossRefGoogle Scholar
  23. [23]
    Bastian Leibe, Aleš Leonardis, and Bernt Schiele. Combined Object Categorization and Segmentation with an Implicit Shape Model. In ECCVWorkshop on Statistical Learning in Computer Vision, pages 17–32, 2004.Google Scholar
  24. [24]
    Bastian Leibe, Aleš Leonardis, and Bernt Schiele. Robust object detection with interleaved categorization and segmentation. International Journal of Computer Vision, 77:259–289, 2008.CrossRefGoogle Scholar
  25. [25]
    Elliott H. Lieb and Michael Loss. Analysis, volume 14 of Graduate Studies in Mathematics. American Mathematical Society, 1997.Google Scholar
  26. [26]
    Lesley Murphy. Linear feature detection and enhancement in noisy images via the Radon transform. Pattern Recognition Letters, 4(4):279–284, 1986.MathSciNetCrossRefGoogle Scholar
  27. [27]
    Ana Pérez Grassi. Variable illumination and invariant features for detecting and classifying varnish defects. PhD thesis, Karlsruhe Institute of Technology, 2010.Google Scholar
  28. [28]
    Johann Radon. Über die Bestimmung von Funktionen durch ihre Integralwerte längs gewisser Mannigfaltigkeiten. Berichte über die Verhandlungen der Königlich Sächsischen Gesellschaft der Wissenschaften, Mathematisch-Physikalische Klasse, 69:262–277, 1917.Google Scholar
  29. [29]
    Paul Rattey and Allen Lindgren. Sampling the 2-D Radon transform. IEEE Transactions on Acoustics, Speech and Signal Processing, 29(5):994–1002, October 1981.CrossRefGoogle Scholar
  30. [30]
    Jennifer Sander, Michael Heizmann, Igor Goussev, and Jürgen Beyerer. Global evaluation of focussed Bayesian fusion. In Jerome Braun, editor, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications, Proc. SPIE, volume 7710, 2010.Google Scholar
  31. [31]
    Günter Saur, Stéphane Estable, Karin Zielinski, Stefan Knabe, Michael Teutsch, and Matthias Gabel. Detection and classification of man-made offshore objects in TerraSAR-X and RapidEye imagery: Selected results of the DeMarine-DEKO project. In IEEE OCEANS, 2011.Google Scholar
  32. [32]
    Hanns Schulz-Mirbach. Anwendung von Invarianzprinzipien zur Merkmalgewinnung in der Mustererkennung. VDI-Verlag, 1995.Google Scholar
  33. [33]
    Wesley Snyder and Hairong Qi. Machine vision. Cambridge University Press, 2004.Google Scholar
  34. [34]
    Josef Stoer and Roland Bulirsch. Introduction to numerical analysis. Springer, 3rd edition, 2002.Google Scholar
  35. [35]
    Ansgar Trächtler. Tomographische Methoden in der Meßtechnik, volume 897 of Fortschritt-Berichte VDI, 8. VDI-Verlag, 2001.Google Scholar
  36. [36]
    A. vander Lugt. Signal detection by complex spatial filtering. IEEE Transactions on Information Theory, 10(2):139–145, 1964.CrossRefGoogle Scholar
  37. [37]
    Vijaya Kumar, Fred Dickey, and John DeLaurentis. Correlation filters minimizing peak location errors. Journal of the Optical Society of America A, 9(5):678–682, 1992.CrossRefGoogle Scholar
  38. [38]
    Vijaya Kumar and L. Hassebrook. Performance measures for correlation filters. Applied Optics, 29(20):2997–3006, 1990.CrossRefGoogle Scholar
  39. [39]
    WolfgangWalter. Analysis 2. Springer, 4th edition, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Jürgen Beyerer
    • 1
    Email author
  • Fernando Puente León
    • 2
  • Christian Frese
    • 3
  1. 1.Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung and The Karlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.Karlsruhe Institute of TechnologyKarlsruheGermany
  3. 3.Fraunhofer-Institut für Optronik, Systemtechnik und BildauswertungKarlsruheGermany

Personalised recommendations