Faculty of Science

Objectives

To introduce students to the nature of patterns and how they can be recognized.

Contents

Applications of pattern recognition; statistical decision theory; image processing and analysis; pattern recognition models-deterministic; fuzzy set; geometry neural network; statistical; structured; Pattern recognition design methodology; classifier design and evaluation; pattern analysis; Probability – probability of values; random variables; joint distribution of random variables; estimation of parameters; minimum risk estimation. Statistical decision making; non-parametric decision making; clustering; algorithms; artificial neural networks for Pattern Recognition; processing waveforms and images; image analysis; Pattern recognition application compiler, vision; signal processing; text processing waveform analysis; Pattern recognition implementation; interactive systems; special architecture.