Refining surface curvature with relaxation labeling

  • Richard C. Wilson
  • Edwin R. Hancock
Poster Session A: Color & Texture, Enhancement Image Analysis & Pattern Recognition, Segmentation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)


Our main contributions in this paper are twofold. In the first instance, we demonstrate how H - K surface labelling can be realised using dictionary-based probabilistic relaxation. To facilitate this implementation we have developed a dictionary of feasible surface-label configurations. These configurations observe certain constraints on the contiguity of elliptic and hyperbolic regions, and, on the continuity and thinness of parabolic lines. The second contribution is to develop a statistical model which allows scheme to be initialised using the probabilities of the different H - K labels to be estimated from surface normal information.


Hessian Matrix Differential Structure Surface Labelling Hyperbolic Region Label Assignment 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Richard C. Wilson
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

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