06EC752 - PATTERN RECOGNITION |
PART – A |
UNIT – I |
INTRODUCTION: Applications of pattern recognition, statistical decision
theory, image processing and analysis. |
UNIT – II |
PROBABILITY: Introduction, probability of events, random variables, Joint
distributions and densities, moments of random variables, estimation of
parameters from samples, minimum risk estimators. |
UNIT – III |
STATISTICAL DECISION MAKING: Introduction, Baye’s Theorem,
multiple features, conditionally independent features, decision boundaries,
unequal costs of error, estimation of error rates, the leaving-one-out
technique. Characteristic curves, estimating the composition of populations.
7 Hours |
UNIT – IV |
NONPARAMETRIC DECISION MAKING: Introduction, histograms,
Kernel and window estimators, nearest neighbor classification techniques,
adaptive decision boundaries, adaptive discriminate Functions, minimum
squared error discriminate functions, choosing a decision making technique. |
PART – B |
UNIT – V |
CLUSTERING: Introduction, hierarchical clustering, partitional clustering. |
UNIT – VI |
ARTIFICIAL NEURAL NETWORKS: Introduction, nets without hidden
layers. nets with hidden layers, the back Propagation algorithms, Hopfield
nets, an application. |
UNIT – VII |
PROCESSING OF WAVEFORMS AND IMAGES: Introduction, gray
level sealing transfoniiations, equalization, geometric image and
interpolation, Smoothing, transformations, edge detection, Laplacian and
sharpening operators, line detection and template matching, logarithmic gray
level sealing, the statistical significance of image features. |
REFERENCE |
Reference Books |
1. “Pattern Recognition and Image Analysis”, Eart Gose, Richard
Johnsonburg and Steve Joust, Prentice-Hall of India-2003.
2. “Pattern recognition (Pattern recognition a scene analysis)”
Duda and Hart.
3. “Pattern recognition: Statistical, Structural and neural
approaches”, Robert J Schalkoff, John Wiley. |
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