ECE 590 C

• Graduate Seminar and Thesis Research

In recent years, important breakthroughs have been made in computer vision. New vision problems have been addressed, and novel solutions to challenging classical problems have been proposed. These accomplishments have been made feasible by recent advances in statistical image modeling, linear-algebra techniques, graph/information-theoretic methods, and other methodologies. We will be reading an eclectic mix of classic and recent papers with the goal to become more familiar with some of these new techniques and methodologies. In particular, the course will survey recent developments in:

1. Dimensionality reduction (PCA, ICA, LDA)
   
2. Statistical graphical models (MRFs, CRFs, generative models)
   
3. Discriminative learning and classifiers (AdaBoost, SVMs, Neural nets)
   
4. Graph-theoretical approaches in vision (Graph matching, Spectral
   clustering)
   
5. Feature extraction and selection (Filtering, SIFT, blobs, FOA,
   segmentation)
   
6. 3D structure extraction and representation
   

Masters students in ECE may not use ECE 590's toward their MS degree. This seminar will be given in 3169 Beckman Institute.