Pattern Recognition

Objectives:
• To implement pattern recognition and machine learning theories
• To design and implement certain important pattern recognition techniques
• To apply the pattern recognition theories to applications of interest
• To implement the entropy minimization, clustering transformation and feature ordering

UNIT I
INTRODUCTION: Basic concepts, Applications, Fundamental problems in pattern Recognition system design, Design concepts and methodologies, Examples of Automatic Pattern recognition systems, Simple pattern recognition model
DECISION AND DISTANCE FUNCTIONS: Linear and generalized decision functions, Pattern space and weight space, Geometrical properties, implementations of decision functions, Minimum-distance pattern classifications.

UNIT II
PROBABILITY: Probability of events, Random variables, Joint distributions and densities, Movements of random variables, Estimation of parameter from samples. STATISTICAL DECISION MAKING: Introduction, Baye’s theorem, Multiple features, Conditionally independent features, Decision boundaries, Unequal cost of error, estimation of error rates, the leaving-one-out-techniques, characteristic curves, estimating the composition of populations. Baye’s classifier for normal patterns.

UNIT III
NON PARAMETRIC DECISION MAKING: Introduction, histogram, kernel and window estimation, nearest neighbour classification techniques. Adaptive decision boundaries, adaptive discriminate functions, Minimum squared error discriminate functions, choosing a decision making techniques.
CLUSTERING AND PARTITIONING: Hierarchical Clustering: Introduction, agglomerative clustering algorithm, the single-linkage, complete-linkage and average-linkage algorithm. Ward’s method Partition clustering-Forg’s algorithm, K-means’s algorithm, Isodata algorithm.

UNIT IV
PATTERN PREPROCESSING AND FEATURE SELECTION: Introduction, distance measures, clustering transformation and feature ordering, clustering in feature selection through entropy minimization, features selection through orthogonal expansion, binary feature selection.

UNIT V
SYNTACTIC PATTERN RECOGNITION & APPLICATION OF PATTERN RECOGNITION: Introduction, concepts from formal language theory, formulation of syntactic pattern recognition problem, syntactic pattern description, recognition grammars, automata as pattern recognizers, Application of pattern recognition techniques in bio metric, facial recognition, IRIS scon, Finger prints, etc.,

TEXT BOOKS:
1. "Pattern recognition and Image Analysis" by Gose, Johnsonbaugh, Jost, PHI
2. "Pattern Recognition Principle" by Tou, Rafael, Gonzalez, Pearson Education

REFERENCE BOOK:
1. "Pattern Classification" Richard duda, Hart, David Strok, John Wiley.
2. Digital Image Processing, M.Anji Reddy, Y.Hari Shankar, BS Publications.

3. Statistical Pattern Recognition by Andrew R. Webb

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