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Paul Fieguth Dept. of Systems Design Engineering Faculty of Engineering University of Waterloo Waterloo, Ontario Canada N2L 3G1 |
pfieguth@uwaterloo.ca Tel: (519) 888-4567 x33599 FAX: (519) 746-4791 |
Pattern recognition as a process of data analysis. Pattern features as components in a random vector representation. Classification techniques: distance measures in feature space, probabilistic decision theory, linear discriminants. Clustering and feature extraction. Applications: optical character recognition, speech recognition, robot vision, medical diagnosis, remote sensing.
Course Times: Mondays, Wednesdays, 10:30-11:50, E2-1307C
The course starts with a brief summary of SY DE 372: probabalistic classifiers, discriminant functions, unlabeled clustering, and feature extraction. More advanced topics will include some information theory (as it pertains to feature extraction), statistical estimation and error analysis (relating to parameter estimation), neural networks, self-organizing maps, and syntactical/grammatical pattern recognition.
Course Times: Mondays, Wednesdays 10:30-12:00, E2-1307C
Not a course in image processing per se; rather it is a course which will study the statistical modeling, analysis, and numerical methods of data processing, especially multidimensional data processing. The course will begin with an overview of inverse problems, ill-posedness, estimation theory, and Kalman filtering.
Models and analysis of linear systems. Discrete time systems, continuous time systems; difference and differential equations; impulse and frequency response. Complex frequency, functions of complex variables, transform domain techniques: Z transforms; Fourier analysis, Laplace transform. Transfer functions and frequency response, frequency domain analysis of linear systems; sampling theory, stability, and linear filters.
Digital technology, combinatorial logic, binary arithmetic, synchronous sequential circuits, design methodology, algorithmic state machines, microcomputer interfacing.
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