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Robust Identification of Locally Planar Objects Represented by 2D Point Clouds under Affine Distortions

  • Dominic Mai
  • Thorsten Schmidt
  • Hans Burkhardt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6376)

Abstract

The matching of point sets that are characterized only by their geometric configuration is a challenging problem. In this paper, we present a novel point registration algorithm for robustly identifying objects represented by two dimensional point clouds under affine distortions. We make no assumptions about the initial orientation of the point clouds and only incorporate the geometric configuration of the points to recover the affine transformation that aligns the parts that originate from the same locally planar surface of the three dimensional object. Our algorithm can deal well with noise and outliers and is inherently robust against partial occlusions. It is in essence a GOODSAC approach based on geometric hashing to guess a good initial affine transformation that is iteratively refined in order to retrieve a characteristic common point set with minimal squared error. We successfully apply it for the biometric identification of the bluespotted ribbontail ray Taeniura lymma.

Keywords

Point Cloud Object Recognition Invariant Mapping Iterative Close Point Planar Object 
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 2010

Authors and Affiliations

  • Dominic Mai
    • 1
  • Thorsten Schmidt
    • 1
  • Hans Burkhardt
    • 1
  1. 1.Computer Science DepartmentUniversity of FreiburgFreiburg i. Br.Germany

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