This document discusses using principal component analysis (PCA) to train a point distribution model to label images. It involves applying PCA to labeled images to project a mean shape, then iteratively modifying model points to fit local neighborhoods of landmark points. The process also uses multi-resolution analysis, line searches, and allows variation of principal components to initialize the model in a way that is relatively fast but can still robustly label new images.

1. A.Rahim Kadkhodamohammadi January, 2011

2. Point Distribution Model Training: Apply PCA to labeled images New image Project mean shape Iteratively modify model points to fit local neighborhood

3. Landmark points Interpolation Align all shape PCA

5. Looking for Landmarks Gray Level Profile (GLP) Shape deformation: Scaling Translation rotation Allowable displacement

11. Multi-resolution (Scale Space) Line Search Initialization Variation of Principal Component

12. ASM is relatively fast ASM too simplistic; not robust when new images are introduced May not converge to good solution

13. Questions and Comments are Welcome Thank You