Computer vision based analysis of the botanical tree's dynamical behaviors for the reproduction in virtual space

This paper deals with a method that analyzes a botanical tree's behaviors in real space by a computer vision approach so as to reproduce the analyzed behaviors in virtual space. Instead of applying unstable local tracking to the tree in a video sequence, we estimate the direction and strength of the wind that shakes the tree by a learning based method that classifies the input video sequence into one of the stored winds with different directions and strengths. In the learning phase, sample video sequences are used for constructing the Eigenspace and Fisherspace, which is obtained from Fisher discriminant analysis. In the classification phase, the input video sequence is compared with each of the stored sample sequences so that the direction and strength of the wind are estimated. An interpolation method improves the estimation accuracy. Experimental results demonstrate the effectiveness of the proposed method.