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RI | Publications | Active Appearance Models Revisited
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Active Appearance Models Revisited
I. Matthews and S. Baker
International Journal of Computer Vision, Vol. 60, No. 2, November, 2004, pp. 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 minimising the error between the input image and the closest model instance; i.e.~solving a nonlinear optimisation problem. We propose an efficient fitting algorithm for AAMs based on the inverse compositional image alignment algorithm. We show that the effects of appearance variation during fitting can be precomputed (``projected out'') using this algorithm and how it can be extended to include a global shape normalising warp, typically a 2D similarity transformation. We evaluate our algorithm to determine which of its novel aspects improve AAM fitting performance.
| Notes |
Sponsor: US Office of Naval Research
Grant ID: N00014-00-1-0915
Associated center: VASC
Associated labs/groups: People Image Analysis Consortium, Vision for Safe Driving, and Face Group
Associated project: AAM Fitting Algorithms
Number of pages: 52
Note: In Press
| Text Reference |
I. Matthews and S. Baker, "Active Appearance Models Revisited," International Journal of Computer Vision, Vol. 60, No. 2, November, 2004, pp. 135 - 164.
| BibTeX Reference |
@article{Matthews_2004_4601,
author = "Iain Matthews and Simon Baker",
title = "Active Appearance Models Revisited",
journal = "International Journal of Computer Vision",
month = "November",
year = "2004",
volume = "60",
number = "2",
pages = "135 - 164",
note = "In Press"
}