COMP-644A: Pattern Recognition - Course Outline
Course: COMP-644ACourse prerequisites:
Title: Pattern Recognition (4 credits, 3 hours (2 lectures) per week)
Time & Place: MW 11:35-12:55, McConnell Engineering 103
Instructor: Godfried Toussaint , Room 307, McConnell
Phone: External - 398-7077, Internal - 5911
Office hours: Tues & Thurs, 16:00-17:00.
Teaching Assistant: Erin McLeish, Office: McConnell Engineering 109, phone: 398-5485, email: mcleish@cs.mcgill.ca, Office hours: Monday, Wednesdays 3 - 4pm.
Reminder:Introduction to pattern recognition via character recognition: grids, connectivity and contour tracing. Smoothing: regularization, local averaging, median filtering and polygonal approximation. Differentiation: image enhancement, the Laplacian operator and unsharp masking. Moments of area and perimeter for shape measurement. Medial axis transforms: skeletonization algorithms and medial axis algorithms. Topological feature extraction: convex hulls and convex deficiencies. Processing line drawings: Freeman chain coding and geometric probability. Detecting structure in noisy pictures: Hough transforms, proximity graphs, relative neighborhood graphs, sphere-of-influence graphs, alpha-hulls, crusts and beta-skeletons. Neural networks: non-parametric learning and error-correction rules. Bayesian decision theory: discrete and continuous case, Gaussian density functions, Mahalanobis distance. Feature selection: independence of measurements, redundancy and synergism, information theory and feature evaluation criteria and feature selection search strategies. Measuring string similarity. Estimation of parameters: maximum likelihood and Bayesian estimation. Estimation of misclassification: generalization, substitution, leave-one-out and bootstrap. Nearest neighbor decision rules: editing, condensing and efficient nearest neighbor search. Cluster analysis and unsupervised learning: decision-directed learning, graph-theoretic methods, agglomerative and divisive methods. Using context in pattern recognition: Markov methods and the Viterbi algorithm. Support Vector Machines. Music Information Retrieval.
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