Ridge-Valley Lines Smoothing and Optimizing

  • Hao Jing
  • Weixiang Zhang
  • Bingfeng Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4282)


When detecting ridge-valley lines on 3D mesh model, estimation of the curvature and curvature derivatives may often yields to squiggly and noisy result, because the estimation is sensitive against unwanted surface noises. We present two algorithms to obtain smooth and noiseless ridge-valley lines. First, we apply an iterative procedure on ridge and valley vertices and their previous and next neighbors on connected feature lines, which leads to smooth lines. Secondly, we propose an algorithm to distinguish noises from meaningful feature lines based on graph theory model. Each separate feature line is considered as an undirected weighted graph which is called as Feature Graph. We can reasonably get rid of most noises and preserve meaningful feature lines through optimizing the minimal spanning tree of each feature graph.


Minimal Span Tree Feature Line Longe Path Left Image Feature Graph 
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 2006

Authors and Affiliations

  • Hao Jing
    • 1
  • Weixiang Zhang
    • 1
  • Bingfeng Zhou
    • 1
  1. 1.Institute of Computer Science and TechnologyPeking UniversityP.R. China

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