A framework for low level feature extraction

  • Wolfgang Förstner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 801)


The paper presents a framework for extracting low level features. Its main goal is to explicitely exploit the information content of the image as far as possible. This leads to new techniques for deriving image parameters, to either the elimination or the elucidation of ”buttons”, like thresholds, and to interpretable quality measures for the results, which may be used in subsequent steps. Feature extraction is based on local statistics of the image function. Methods are available for blind estimation of a signal dependent noise variance, for feature preserving restoration, for feature detection and classification, and for the location of general edges and points. Their favorable scale space properties are discussed.


low level features keypoints edge detection segments local image statistics noise estimation restoration adaptive thresholds scale space quality evaluation 


  1. 1.
    V. Berzins. Accuracy of Laplacian Edge Detectors. CVGIP, 27:185–210, 1984.Google Scholar
  2. 2.
    J. Bigün. A Structure Feature for Some Image Processing Applications Based on Spiral Functions. CVGIP, 51:166–194, 1990.Google Scholar
  3. 3.
    R. Brügelmann and W. Förstner. Noise Estimation for Color Edge Extraction. In W. Förstner and S. Winter, editors, Robust Computer Vision, 90–107. Wichmann, Karlsruhe, 1992.Google Scholar
  4. 4.
    J. Canny. A Computational Approach to Edge Detection. IEEE T-PAMI, 8(6):679–698, 1986.Google Scholar
  5. 5.
    W. Förstner. Statistische Verfahren für die automatische Bildanalyse und ihre Bewertung bei der Objekterkennung und-vermessung, Volume 370 of Series C. Deutsche Geodätische Kommission, München, 1991.Google Scholar
  6. 6.
    W. Förstner. Determination of Local Scale in an Image. Technical report, Institut für Photogrammetrie, Bonn, 1993.Google Scholar
  7. 7.
    W. Förstner. Feature Extraction in Digital Photogrammetry. Photogrammetric Record, 14(82):595–611, 1993.Google Scholar
  8. 8.
    W. Förstner and E. Gülch. A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Circular Features. In Proceedings of the Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, 281–305, 1987.Google Scholar
  9. 9.
    C. Fuchs, T. Löcherbach, H.-P. Pan, and W. Förstner. Land Use Mapping from Remotely Sensed Images. In Colloquim on Advances in Urban Spatial Information and Analysis, Wuhan, 1993.Google Scholar
  10. 10.
    G. H. Granlund. In Search for a General Picture Processing Operator. CVIP, 8:155–178, 1978.Google Scholar
  11. 11.
    G. H. Granlund. Image Sequence Analysis. In S. J. Pöppl and H. Handels, editors, Mustererkennung, 1–18. DAGM, 1993.Google Scholar
  12. 12.
    U. Grenander. Advances in Pattern Theory. The Annals of Statistic, 17:1–30, 1989.Google Scholar
  13. 13.
    R. M. Haralick and L. G. Shapiro. Robot and Computer Vision. Addison-Wesley, 1992.Google Scholar
  14. 14.
    D. Heeger. Optical Flow from Spatiotemporal Filters. In Proceedings of 1st ICCV, 181–190, 1987.Google Scholar
  15. 15.
    J. Heikkilä. Multiscale Representation with Förstner Operator. The Photogrammetric Journal of Finland, 11(2):40–59, 1989.Google Scholar
  16. 16.
    M. Kass and A. Witkin. Analyzing Oriented Patterns. CVGIP, 37:362–385, 1987.Google Scholar
  17. 17.
    H. Knutson and G. H. Granlund. Texture Analysis Using Two-Dimensional Quadrature Filters. In Workshop Computer Architecture for Pattern Analysis and Image Data Base Management, Pasadena, 1983.Google Scholar
  18. 18.
    T. S. Lee, D. Mumford, and A. Yuille. Texture Segmentation by Minimizing Vector-Valued Energy Functionals: The Coupled Membrane Model. In Computer Vision — ECCV '92, Proceedings, 165–173. Springer, 1992.Google Scholar
  19. 19.
    T. Lindeberg and J. Gårding. Direct Computation of Shape Cues by Multi-Scale Retinoptic Processing. Technical Report 117, Computational Vision and Active Perception Laboratory, Stockholm University, 1993.Google Scholar
  20. 20.
    D. E. McClure. Image Models in Pattern Theory. In A. Rosenfeld, editor, Image Modelling, 259–275. Academic Press Inc., Orlando Florida, 1980/81.Google Scholar
  21. 21.
    P. Meer, J. Jolion, and A. Rosenfeld. A Fast Parallel Algorithm for Blind Estimation of Noise Variance. IEEE T-PAMI, 12(2):216–223, 1990.Google Scholar
  22. 22.
    A. Papoulis. Probability, Random Variables, and Stochastic Processes. Electrical Engineering. McGraw-Hill, 2 edition, 1984.Google Scholar
  23. 23.
    L. Rosenthaler, F. Heitger, O. Kübler, and R. von der Heydt. Detection of General Edges and Keypoints. In Computer Vision — ECCV '92, Proceedings, 78–86. Springer, 1992.Google Scholar
  24. 24.
    V. Torre and T. A. Poggio. On Edge Detection. IEEE T-PAMI, 8(2):147–163, 1986.Google Scholar
  25. 25.
    H. Vorhees and T. Poggio. Detecting Blobs as Textons in Natural Images. In Image Understanding Workshop, LA, Proceedings, 1987.Google Scholar
  26. 26.
    U. Weidner. Informationserhaltende Filterung und ihre Bewertung. In B. Radig, editor, Mustererkennung, Proceedings, 193–201. DAGM, Springer, 1991.Google Scholar
  27. 27.
    U. Weidner. Parameterfree Information-Preserving Surface Restoration. In Computer Vision — ECCV '94, Proceedings, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Wolfgang Förstner
    • 1
  1. 1.Institut für PhotogrammetrieUniversität BonnBonn

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