8/16/99 Computer Vision and Modeling
8/16/99 Principal Components with SVD
8/16/99 Linear Dimension Reduction: High-dimensional Input Space
8/16/99 Linear Subspace: += + 1.7=
8/16/99 Linear Subspace:
8/16/99 Principal Components Analysis: m
8/16/99 Examples: Data: Kirby, Weisser, Dangelmayer 1993
8/16/99 Examples: Data: PCA New Basis Vectors
8/16/99 Examples: Data: PCA EigenLips
8/16/99 Examples: Face Recognition with Eigenfaces (Turk+Pentland, ):
8/16/99 Examples: Face Recognition System (Moghaddam+Pentland):
8/16/99 Examples: Visual Cortex Hubel
8/16/99 Examples: Visual Cortex Hubel
8/16/99 Examples: Receptive Fields Hubel
8/16/99 Examples: Receptive Fields Hancock et al: The principal components of natural images
8/16/99 Examples: Receptive Fields Hancock et al: The principal components of natural images
8/16/99 Examples: Active Appearance Models (AAM): (Cootes et al)
8/16/99 Examples: Active Appearance Models (AAM): (Cootes et al)
8/16/99 Examples: Active Appearance Models (AAM): (Cootes et al)
8/16/99 Examples: 3D Morphable Models (Blanz+Vetter)
8/16/99 Examples: 3D Morphable Models (Blanz+Vetter)
8/16/99 Review E(V) VV Constrain - Analytically derived: Affine, Twist/Exponential Map Learned: Linear/non-linear Sub-Spaces
8/16/99 S = (p,…,p ) E(S) Constrain 1n Non-Rigid Constrained Spaces
8/16/99 Non-Rigid Constrained Spaces Nonlinear Manifolds: Linear Subspaces : Small Basis Set Principal Components Analysis Mixture Models
8/16/99 Examples: Eigen Tracking (Black and Jepson)
8/16/99 Examples: Shape Models for tracking:
8/16/99 More generic Feature/Shape Models: Visual Motion Contours: Blake, Isard, Reynard
8/16/99 More generic Feature/Shape Models: Visual Motion Contours: Blake, Isard, Reynard
8/16/99 Linear Discriminant Analysis:
8/16/99 Fisher’s linear discriminant:
8/16/99 Example: Eigenfaces vs Fisherfaces Glasses or not Glasses ?
8/16/99 Example: Eigenfaces vs Fisherfaces Input New Axis Belhumeur, Hespanha, Kriegman 1997
8/16/99 Nonlinear Manifolds Nonlinear Manifolds: Linear Subspaces : Small Basis Set Principal Components Analysis Mixture Models