Brightness-contrast diffusion and the grouping of missing angles

  • Karsten Ottenberg
Edges and Contours
Part of the Lecture Notes in Computer Science book series (LNCS, volume 719)


We present a framework for contour fragment grouping. The contour fragments extracted from an image by an appropriate edge-detection procedure are assumed to be attributed with an estimate of brightness contrast. Based on this brightness contrast along detected contour fragments, we reconstruct a smooth (weak membrane) intensity function under the boundary conditions imposed by the contour fragments. This reconstructed intensity function subsequently serves as a potential field φ in a framework of different possible contour grouping processes. In this paper we treat the general problem of grouping two straight line contour fragments building an arbitrary (missing) angle. We solve the grouping problem in closed form a specific grouping process within the suggested framework.


Potential Field Laplace Equation Intensity Function Tangent Direction Brightness Contrast 
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  1. [1]
    K.Ottenberg, A Framework for Contour Grouping via Brightness Contrast Diffusion, submitted to CVGIP:Image Understanding.Google Scholar
  2. [2]
    A. Blake and A. Zisserman, Visual Reconstruction; The MIT Press, Cambridge, 1987.Google Scholar
  3. [3]
    D. Terzopoulos, The role of constraints and discontinuities in visible — surface reconstruction, in Proceedings 8th International Joint Conference on Artificial Intelligence, Karlsruhe, Germany, August 1983, pp. 1073–1077.Google Scholar
  4. [4]
    D. Terzopoulos, Regularization of Inverse Visual Problems Involving Discontinuities, IEEE Transactions on Pattern Analysis and machine Intelligence, 8(4), 1986, pp.413–424.Google Scholar
  5. [5]
    D. Terzopoulos, The Computation of Visible Surface Representations, IEEE Transactions on Pattern Analysis and machine Intelligence, 10(4), 1988, pp.417–438.Google Scholar
  6. [6]
    D. Geiger and F. Girosi, Parallel and Deterministic Algorithms for MRFs: Surface Reconstruction, IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(5), 1991, pp. 401–412.Google Scholar
  7. [7]
    N. Nordstroem, Biased Anisotropic Diffusion — A Unified Regularization and Diffusion Approach to Edge Detection, Image and Vision Computing, 8(4), 1990, pp.318–327.Google Scholar
  8. [8]
    D. Geiger and A. Yuille, A Common Framework for Image Segmentation, International Journal of Computer Vision, 6(3), 1991, pp. 227–243.Google Scholar
  9. [9]
    C. Caratheodory, Conformal Representation, Cambridge, 1932.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Karsten Ottenberg
    • 1
  1. 1.Forschungsabteilung Technische Systeme HamburgPhilips GmbH ForschungslaboratorienHamburg 54

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