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Active Appearance Models Revisited (2003)
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| Venue: | International Journal of Computer Vision |
| Citations: | 452 - 39 self |
BibTeX
@ARTICLE{Matthews03activeappearance,
author = {Iain Matthews and Simon Baker},
title = {Active Appearance Models Revisited},
journal = {International Journal of Computer Vision},
year = {2003},
volume = {60},
pages = {135--164}
}
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Abstract
Active Appearance Models (AAMs) and the closely related concepts of Morphable Models and Active Blobs are generative models of a certain visual phenomenon. Although linear in both shape and appearance, overall, AAMs are nonlinear parametric models in terms of the pixel intensities. Fitting an AAM to an image consists of minimizing the error between the input image and the closest model instance; i.e. solving a nonlinear optimization problem. We propose an efficient fitting algorithm for AAMs based on the inverse compositional image alignment algorithm. We show how the appearance variation can be "projected out" using this algorithm and how the algorithm can be extended to include a "shape normalizing" warp, typically a 2D similarity transformation. We evaluate our algorithm to determine which of its novel aspects improve AAM fitting performance.
Keyphrases
active appearance model appearance variation related concept aam fitting performance nonlinear parametric model input image pixel intensity active blob model instance generative model novel aspect inverse compositional image alignment algorithm efficient fitting algorithm similarity transformation nonlinear optimization problem morphable model certain visual phenomenon
