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Active Appearance Models Revisited

Iain Matthews and Simon Baker
Tech. Report, CMU-RI-TR-03-02, Robotics Institute, Carnegie Mellon University, April, 2003

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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.

author = {Iain Matthews and Simon Baker},
title = {Active Appearance Models Revisited},
year = {2003},
month = {April},
institution = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-03-02},
keywords = {ctive Appearance Models, AAMs, Active Blobs,Morphable Models, fitting, efficiency, Gauss-Newton gradient descent,inverse compositional image alignment.},
} 2017-09-13T10:44:45-04:00