Extraction of 3D unfoliaged trees from image sequences via a generative statistical approach

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. © Springer-Verlag Berlin Heidelberg 2007.