Courses:

Subspace (Manifold) learning Theory: PCA Applications: Eigen faces, Active Shape & Active Appearance Models. Additional topics: kernel PCA, LLE

Boundary Detection Theory: Calculus of variations Applications: Mumford-Shah functional, snakes, level sets

EM Theory: EM algorithm Applications: segmentation, tracking

Graph algorithms Theory: Graph cut algorithms Applications: segmentation, stereo

Clustering Theory: hierarchical, k-means, spectral Applications: grouping in images

Graphical Models Theory: MRFs, inference in graphical models Applications: regularization, part/layer models

Shape descriptors Shape context, SIFT Medial axis, skeletons

Transformations and their manipulation Theory: diffeomorphisms, splines Applications: shape representation, registration

Information Theoretic Methods Theory: entropy and mutual information Application: alignment, segmentation

Classification Theory: nearest neighbor, perceptron, Fisher Linear Discriminant, SVMs, Ada Boosting Applications: object detection/recognition

This reading seminar aims to build up the mathematical background necessary to read papers and follow modern research in computer vision, as well as to improve communication skills, such as presenting research work, reviewing papers, surveying a field.

Everyone participating in the class must read the papers and come to class with questions on the assigned paper and on how it relates to other methods that attempt to solve the same problem.

Everyone will also be expected to present one or two papers during the semester and lead the discussion after the presentation.