Learning active appearance models from image sequences

Authors:
Jason Saragih;Roland Goecke
Affiliations:
Australian National University;Australian National University and National ICT Australia, Canberra, Australia
Venue:
VisHCI '06 Proceedings of the HCSNet workshop on Use of vision in human-computer interaction - Volume 56
Year:
2006

Citing 9
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Automatic Construction of Active Appearance Models as an Image Coding Problem

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Accurate 3D Tracking of Rigid Objects with Occlusion Using Active Appearance Models

WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
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IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering

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Abstract

One of the major drawbacks of the Active Appearance Model (AAM) is that it requires a training set of pseudo-dense correspondences. Most methods for automatic correspondence finding involve a groupwise model building process which optimises over all images in the training sequence simultaneously. In this work, we pose the problem of correspondence finding as an adaptive template tracking process. We investigate the utility of this approach on an audio-visual (AV) speech database and show that it can give reasonable results.