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Performance Characterization of Landmark Operators

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Performance Characterization in Computer Vision

Part of the book series: Computational Imaging and Vision ((CIVI,volume 17))

Abstract

Prominent points in multi-dimensional digital images of different modalities are key features for a variety of computer vision tasks. As point landmarks we define, e.g., corners in 2D projection images or tips of anatomical structures in 3D spatial images, both of which are represented by geometric properties of the underlying intensity function. Note that, in the case of 3D spatial images, the geometry of the intensity function in general directly reflects the geometry of the depicted anatomical structures, which is generally not the case for 2D projection images.

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References

  • Beaudet, P.R. (1978) Rotationally invariant image operators, Proc. Intern. Joint Conf. on Pattern Recognition, Kyoto/Japan, Nov. 7–10, 579–583.

    Google Scholar 

  • Beil, W., Rohr, K. and Stiehl, H.S. (1997) Investigation of Approaches for the Localization of Anatomical Landmarks in 3D Medical Images, Proc. Computer Assisted Radiology and Surgery (CAR’97), Berlin, Germany, June 25–28, Lemke, H.U., Vannier, M.W. and Inamura, K. (eds.), Elsevier Amsterdam Lausanne, 265–270.

    Google Scholar 

  • Blom, J. (1992) Topological and Geometrical Aspects of Image Structure, PhD Thesis, University of Utrecht.

    Google Scholar 

  • Coelho, C., Heller, A., Mundy, J.L., Forsyth, D.A. and Zisserman, A. (1992) An Experimental Evaluation of Projective Invariants, Geometric Invariance in Computer Vision, Mundy, J.L. and Zisserman, A. (eds.), The MIT Press, Cambridge, MA, 87–104.

    Google Scholar 

  • Deriche, R. and Giraudon, G. (1993) A Computational Approach for Corner and Vertex Detection, Internat. J. of Computer Vision, 10 (2): 101–124.

    Article  Google Scholar 

  • Dreschler, L. and Nagel, H.-H. (1981) Volumetric Model and 3D-Trajectory of a Moving Car Derived from Monocular TV-Frame Sequences of a Street Scene, Proc. IJCAI’81, Vancouver, BC, 692–697, see also: Computer Graphics and Image Processing 20 (1982) 199–228.

    Google Scholar 

  • Florack, L.M.J., ter Haar Romeny, B.M., Koenderink, J.J. and Viergever, M.A. (1994) General Intensity Transformations and Differential Invariants, J. of Mathematical Imaging and Vision, 4: 171–187.

    Article  Google Scholar 

  • Förstner, W. (1986) A Feature Based Correspondence Algorithm for Image Matching, Intern. Arch. of Photogrammetry and Remote Sensing 26–3 /3, 150–166.

    Google Scholar 

  • Förstner, W. and Gülch, E. (1987) A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Centres of Circular Features, Proc. ISPRS Intercomission Conf. on Fast Processing of Photogrammetric Data, Interlaken/Switzerland, June 2–4, 281–305.

    Google Scholar 

  • Frantz, S., Rohr, K. and Stiehl, H.S. (1998a) Refined Localization of Three-Dimensional Anatomical Point Landmarks Using Multi-Step Differential Approaches, Medical Imaging 1998–Image Processing (MI’98), Proc. SPIE Internat. Symposium, Vol. 3338, Part One, Febr. 23–26, San Diego/CA, Hanson, K.M. (ed.), 28–38.

    Google Scholar 

  • Frantz, S., Rohr, K. and Stiehl, H.S. (1998b) Multi-Step Procedures for the Localization of 2D and 3D Point Landmarks and Automatic ROI Size Selection, Proc. European Conf. on Computer Vision (ECCV’98), June 1998, Freiburg, Germany, Vol. I, Lecture Notes in Computer Science 1406, Burkhardt, H. and Neumann, B. (eds.), Springer, Berlin, Heidelberg, 687–703.

    Google Scholar 

  • Hartkens, T., Rohr, K. and Stiehl, H.S. (1996) Evaluierung von Differentialoperatoren zur Detektion charakteristischer Punkte in tomographischen Bildern, 18. DAGMSymposium Mustererkennung, 11.-13. Sept. 1996, Heidelberg/Germany, Informatik aktuell, Jähne, B., Geißler, P., Haußecker, H. and Hering, F. (eds.), Springer-Verlag, Berlin, Heidelberg, 637–644.

    Google Scholar 

  • Hartkens, T., Rohr, K. and Stiehl, H.S. (1999) Performance of 3D differential operators for the detection of anatomical point landmarks in MR and CT images, Medical Imaging 1999 - Image Processing (MI’99), Proc. SPIE Internat. Symposium, Febr. 20–26, San Diego/CA, to appear.

    Google Scholar 

  • Heyden, A. and Rohr, K. (1996) Evaluation of Corner Extraction Schemes Using Invariance Methods, Proc. 13th Internat. Conf. on Pattern Recognition (ICPR’96), Vienna, Austria, Aug. 25–29, Vol. I, IEEE Computer Society Press, 895–899.

    Google Scholar 

  • Kitchen, L. and Rosenfeld, A. (1982) Gray-level corner detection, Pattern Recognition Letters 1: 95–102.

    Article  Google Scholar 

  • Neumann, H. and Stiehl, H.S. (1987) Towards a Testbed for Evaluation of Early Vision Processes, Proc. Internat. Conf. on Computer Analysis of Images and Patterns (CAIP’87), Wismar, GDR, Sept. 2–4, Yaroslayskii, L.P., Rosenfeld, A. and Wilhelmi, W. (eds.), Akademie-Verlag, Berlin, 202–208.

    Google Scholar 

  • Rohr, K. (1987) Untersuchung von grauwertabhängigen Transformationen zur Ermittlung des optischen Flusses in Bildfolgen, Diplomarbeit, Institut für Nachrichtensysteme, Universität Karlsruhe.

    Google Scholar 

  • Rohr, K. (1990) Über die Modellierung und Identifikation charakteristischer Grauwertverläufe in Realweltbildern, 12. DA GM–Symposium Mustererkennung, 24.-26. Sept. 1990, Oberkochen-Aalen, Germany, Informatik-Fachberichte 254, Großkopf, R.E. (ed.), Springer-Verlag, Berlin, Heidelberg, 217–224.

    Google Scholar 

  • Rohr, K. (1992) Recognizing Corners by Fitting Parametric Models, Internat. J. of Computer Vision 9 (3): 213–230.

    Article  Google Scholar 

  • Rohr, K. (1994) Localization Properties of Direct Corner Detectors, J. of Mathematical Imaging and Vision 4 (2): 139–150.

    Article  MathSciNet  Google Scholar 

  • Rohr, K. (1997) On 3D Differential Operators for Detecting Point Landmarks, Image and Vision Computing 15 (3): 219–233.

    Article  Google Scholar 

  • Rohr, K. and Stiehl, H.S. (1997) Characterization and Localization of Anatomical Landmarks in Medical Images, Proc. 1st Aachen Conf. on Neuropsychology in Neurosurgery, Psychiatry, and Neurology, Dec. 12–14, Aachen/Germany, Hütter, B.O. and Gilsbach, J.M. (eds.), Verlag der Augustinus Buchhandlung, 9–12.

    Google Scholar 

  • Schmid, C., Mohr, R. and Bauckhage, C. (1998) Comparing and Evaluating Interest Points, Proc. Int. Conf. on Computer Vision (ICCV’98), Bombay, India, Jan. 4–7, Narosa Publishing House, New Delhi Madras, 230–235.

    Google Scholar 

  • Sobotta, J. (1988) Atlas der Anatomie des Menschen, 1. Band, Ferner, H. and

    Google Scholar 

  • Staubesand, J. (eds.), Urban und Schwarzenberg, München, Wien, Baltimore. Thirion, J.-P. (1996) New Feature Points based on Geometric Invariants for 3D Image Registration, Internat. J. of Computer Vision 18(2):121–137.

    Google Scholar 

  • Zuniga, O.A. and Haralick, R.M. (1983) Corner detection using the facet model, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Washington/D.C., June 19–23, 30–37.

    Google Scholar 

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Rohr, K., Stiehl, H.S., Frantz, S., Hartkens, T. (2000). Performance Characterization of Landmark Operators. In: Klette, R., Stiehl, H.S., Viergever, M.A., Vincken, K.L. (eds) Performance Characterization in Computer Vision. Computational Imaging and Vision, vol 17. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9538-4_23

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  • DOI: https://doi.org/10.1007/978-94-015-9538-4_23

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-5487-6

  • Online ISBN: 978-94-015-9538-4

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