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Extraction of 3D Unfoliaged Trees from Image Sequences Via a Generative Statistical Approach

  • Hai Huang
  • Helmut Mayer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4713)

Abstract

In this paper we propose a generative statistical approach for the three dimensional (3D) extraction of the branching structure of unfoliaged deciduous trees from urban image sequences. The trees are generatively modeled in 3D by means of L-systems. A statistical approach, namely Markov Chain Monte Carlo – MCMC is employed together with cross correlation for extraction. Thereby we overcome the complexity and uncertainty of extracting and matching branches in several images due to weak contrast, background clutter, and particularly the varying order of branches when projected into different images. First results show the potential of the approach.

Keywords

Image Sequence Deciduous Tree Background Clutter Airborne Laser Scanner Epipolar Line 
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 2007

Authors and Affiliations

  • Hai Huang
    • 1
  • Helmut Mayer
    • 1
  1. 1.Institute of Photogrammetry and Cartography, Bundeswehr University Munich 

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