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A hierarchical filter scheme for efficient corner detection

  • Tobias StammbergerEmail author
  • Markus Michaelis
  • Maximilian Reiser
  • Karl-Hans Englmeier
Low Level Processing I
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1296)

Abstract

There are a number of differential geometric based corner detectors in the literature. These operators require 5 or 9 convolutions with derivative kernels in 21) or 31) respectively, what is expensive in terms of time and memory requirements. In this paper we propose an efficient approach to calculate the response of these operators.

Keywords

Gaussian Curvature Corner Point Activity Image Corner Detector Corner Angle 
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.

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Refernces

  1. 1.
    Beaudet, P.R.: Rotationally invariant image operators. International Joint Conference on Pattern Recognition (1978) 579–583Google Scholar
  2. 2.
    Berzins, V.: Accuracy of Laplacian edge detectors. Computer Vision, Graphics, and Image Processing 27 (1984) 195–210Google Scholar
  3. 3.
    Drescher, L., Nagel, H.H.: Volumetric model and 3D trajectory of a moving car derived from monocular TV frame sequences of a street scene. Computer Graphics and Image Processing 20 (1982) 199–228Google Scholar
  4. 4.
    Deriche, R, Giraudon, G.: A computational approach for corner and vertex detection. International Journal of Computer Vision 10 (2) (1993) 101–124CrossRefGoogle Scholar
  5. 5.
    Florack, L.:. The syntactical structure of scalar images. PHD Thesis, University of Utrecht (1993)Google Scholar
  6. 6.
    Guiducci, A.: Corner characterization by differential geometry techniques. Pattern Recognition Letters 8 (1988) 311–318CrossRefGoogle Scholar
  7. 7.
    Kitchen, L., Rosenfeld, A.: Gray-level corner detection. Pattern Recognition Letters 1 (2) (1982) 95–102CrossRefGoogle Scholar
  8. 8.
    Krueger, W.M., Phillips, K.: The geometry of differential operators with application to image processing. IEEE PAMI 11 (1989) 1252–1264Google Scholar
  9. 9.
    Koenderink, J., van Doorn, A.: Gerneric neighborhood operators. IEEE PAMI 14 (6) (1992) 597–605Google Scholar
  10. 10.
    Lipschutz, M.: Differential Geometry. McGraw-Hill: New York (1969)Google Scholar
  11. 11.
    Monga, O., Benayoun, S.: Using partial derivatives of 3D images to extract typical surface features. Computer Vision and Image Understanding 61 (2) (1995) 171–189CrossRefGoogle Scholar
  12. 12.
    Nagel, H.H.: Displacement vectors derived from second-order intensity variations in image sequences. Computer Vision, Graphics and Image Processing 21 (1983) 85–117Google Scholar
  13. 13.
    Rohr, K.: Localization properties of direct corner detectors. Journal of Mathematical Imaging and Vision 4 (1994) 139–150CrossRefMathSciNetGoogle Scholar
  14. 14.
    Thirion, J.P., Gourdon, A.: Computing the differential characteristics of isointensity surfaces. Computer Vision and Image Understanding 61 (2) (1995) 190–202CrossRefGoogle Scholar
  15. 15.
    Van den Elsen, P.: Automatic registration of CT and MR brain images using correlation of geometrical features. IEEE Transactions on Medical Imaging 14 (2) (1995) 384–396CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Tobias Stammberger
    • 1
    • 2
    Email author
  • Markus Michaelis
    • 1
  • Maximilian Reiser
    • 2
  • Karl-Hans Englmeier
    • 1
  1. 1.GSF - National Research Center for Environment and HealthOberschleißheimGermany
  2. 2.Klinikum GroßhadernInstitut für Radiologische DiagnostikMünchenGermany

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