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03.11.2008, Tim Landgraf
Active Appearance Models
AG KI, Journal Club 03 Nov 2008
03.11.2008, Tim Landgraf
03.11.2008, Tim Landgraf
03.11.2008, Tim Landgraf
The Idea
• Objects are modelled in shape and grey-level appearance (training necessary)
• New model instances are synthesized and matched onto the new image
• Model parameters are altered according to the quality of the fit
03.11.2008, Tim Landgraf
The Idea
• Generate new model x
x = μ + P*b
from mean model μ and some (b) linear combination of principal components P
• Fit Ix to image region Ii, by altering b according to (Ix – Ii ) = ΔI
offline
online
03.11.2008, Tim Landgraf
creating the model: step by step
1. annotate landmark points2. align the shapes3. PCA (find modes of shape
variation)4. make data shape-free 5. normalize grey values6. PCA (find modes of grey value variation)7. PCA (on the combined model)
Example: 122 landmarks for the face image
03.11.2008, Tim Landgraf
What the … PCA?
• Principal Component Analysis
(aka: Karhunen-Loeve Transform)
BASICS
03.11.2008, Tim Landgraf
PCA, cont.
• used for decorrelation, dimension reduction, generalization
• Data is assumed to be:– Linear– Gaussian (unimodal)
• Principal components: eigenvectors of the Covariance matrix
BASICS
03.11.2008, Tim Landgraf
AAMexplorer
03.11.2008, Tim Landgraf
Fitting the model onto the image
• x = μ + P*b
• simplest approach: Δb = A*ΔI
• „learn“ A:– perturbate known model b‘ = b + Δb and store
the change of image ΔI – Find A by multi-variate linear regression– (note:) A connects grey-value appearance
with all model params
/* reminder */
03.11.2008, Tim Landgraf
Optimization vs. Learning
Initial position
optimum
optimum
Small perturbations
03.11.2008, Tim Landgraf
Extras
• Iterative Approach:– b1 = b0 + kΔb with k \in {0.25, …, 2.0}
– evaluate error and accept new estimate b1, if better fit, otherwise change k
• Multi-resolution: use pyramids to extend the prediction to greater ranges
03.11.2008, Tim Landgraf
AAMs: Properties
• good results if initial guess within 20 pixels and 10% scale
• depends on training image background appearance, too