1. 03.11.2008, Tim Landgraf Active Appearance Models AG KI, Journal Club 03 Nov 2008

2. 03.11.2008, Tim Landgraf

4. 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

5. 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 I x to image region I i, by altering b according to (I x – I i ) = ΔI offline online

6. 03.11.2008, Tim Landgraf creating the model: step by step 1.annotate landmark points 2.align the shapes 3.PCA (find modes of shape variation) 4.make data shape-free 5.normalize grey values 6.PCA (find modes of grey value variation) 7.PCA (on the combined model) Example: 122 landmarks for the face image

7. 03.11.2008, Tim Landgraf What the … PCA? Principal Component Analysis (aka: Karhunen-Loeve Transform) BASICS

8. 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

9. 03.11.2008, Tim Landgraf AAMexplorer

10. 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 */

11. 03.11.2008, Tim Landgraf Optimization vs. Learning Initial position optimum Small perturbations

12. 03.11.2008, Tim Landgraf Extras Iterative Approach: –b 1 = b 0 + kΔb with k \in {0.25, …, 2.0} –evaluate error and accept new estimate b 1, if better fit, otherwise change k Multi-resolution: use pyramids to extend the prediction to greater ranges

13. 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