Learning Active Appearance Models from Image Sequences
نویسندگان
چکیده
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.
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