Monocular and Stereo Methods for AAM Learning from Video
Authors: Jason Saragih and Roland Göcke
Presented by Jason Saragih at the IEEE Computer Society Conference on
Computer Vision and Pattern Recognition CVPR2007, Minneapolis (MN),
USA, 18-23 June 2007
Abstract
The active appearance model (AAM) is a powerful method for modeling
deformable visual objects. One of the major drawbacks of the AAM is that
it requires a training set of pseudo-dense correspondences over the whole
database. In this work, we investigate the utility of stereo constraints
for automatic model building from video. First, we propose a new method
for automatic correspondence finding in monocular images which is based
on an adaptive template tracking paradigm. We then extend this method to
take the scene geometry into account, proposing three approaches, each
accounting for the availability of the fundamental matrix and calibration
parameters or the lack thereof. The performance of the monocular method
was first evaluated on a pre-annotated database of a talking face. We
then compared the monocular method against its three stereo extensions
using a stereo database.