I

INTRODUCTION TO PATTERN RECOGNITION

Basic concepts- Structure of a typical pattern recognition system.

4

Module 1

II

DECISION FUNCTIONS

Role of decision functions in pattern recognition- Linear and generalized decision functions - Concept of pattern space and weight space - Geometric properties - Implementation of decision functions.

5

Module 2

III

FEATURES

Feature vectors - Feature spaces - Problem of feature identification Feature selection and feature extraction.

4

Module 3

IV

CLUSTERING

Distance measures - Clustering transformation and feature ordering - Clustering in feature selection - Feature selection through entropy minimization.

5

Module 4

V

PATTERN CLASSIFICATION BY DISTANCE FUNCTIONS

Pattern classification by distance functions - Minimum distance classification - Cluster and cluster seeking algorithms - Pattern classification by likelihood functions.

4

Module 5

VI

PATTERN CLASSIFICATION BY STATISTICAL FUNCTIONS

Pattern classification using Statistical classifiers - Bayes’ classifier - Classification performance measures - Risk and error probabilities.

5

Module 6

VII

PATTERN RECOGNITION USING FUZZY CLASSIFIERS

Fuzzy and crisp classification - Fuzzy clustering - Fuzzy pattern recognition - Syntactic pattern recognition- Selection of primitives - Syntax analysis for pattern recognition.

5

Module 7

VIII

PATTERN RECOGNITION USING NEURAL CLASSIFIERS

Introduction - Neural network structures for PR, Neural network based pattern associators - Feed forward networks trained by back propagation - ART networks.

5

Module 8

IX

APPLICATION OF PATTERN RECOGNITION

Application of pattern recognition problem applied forclassification of leather images - Application of pattern recognition problem for classification of citrus fruit images.

3

Module 9

Total

40