C. Kervrann. Learning probabilistic deformation models from image sequences. Signal Processing, (71):155-171, 1998.
Download paper:
Adobe portable document (pdf)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
In This paper, we present an approach for an unsupervised learning of probabilistic deformation modes of 2D moving objects from images sequences. The object representation relies on a statistical description of global and local deformations applied to an a priori prototype shape. The optimal Bayesian estimate of the deformation process is obtained by maximizing a nonlinear joint probability distribution using stochastic and deterministic optimization techniques. The estimates obtained at time t are integrated in the deformation model as a priori knowledge for the segmentation at time \textit{t} + 1. Deformation modes are updated on-line using a principal component analysis of the distortions computed from the shapes estimated previously in the image sequence. In addition, the updated deformation modes are exploited for the real-world image sequences showing the tracking of hands undergoing 2D articulated movements
@article{98Ksp,
Author = {Kervrann, C.},
Title = {Learning probabilistic deformation models from image sequences},
Journal = {Signal Processing},
Number = {71},
Pages = {155--171},
Year = {1998}
}
Get EndNote Reference (.ref)
This document was translated automatically from BibTEX by bib2html (Copyright 2003 © Eric Marchand, INRIA, Vista Project).
