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Feature Detection Using Curvature Maps and the Min-cut/Max-flow Algorithm

  • Timothy Gatzke
  • Cindy Grimm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4077)

Abstract

Automatic detection of features in three-dimensional objects is a critical part of shape matching tasks such as object registration and recognition. Previous approaches often required some type of user interaction to select features. Manual selection of corresponding features and subjective determination of the difference between objects are time consuming processes requiring a high level of expertise. The Curvature Map represents shape information for a point and its surrounding region and is robust with respect to grid resolution and mesh regularity. It can be used as a measure of local surface similarity. We use these curvature map properties to extract feature regions of an object. To make the selection of the feature region less subjective, we employ a min-cut/max-flow graph cut algorithm with vertex weights derived from the curvature map property. A multi-scale approach is used to minimize the dependence on user defined parameters. We show that by combining curvature maps and graph cuts in a multi-scale framework, we can extract meaningful features in a robust way.

Keywords

Feature Detection Shape Match Reeb Graph Object Registration Brown Medical School 
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

  • Timothy Gatzke
    • 1
  • Cindy Grimm
    • 1
  1. 1.Washington University in St. LouisSt. LouisUSA

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