GI3019 Pattern Recognition (Satellite, Aerial, UAV) Syllabus:

GI3019 Pattern Recognition (Satellite, Aerial, UAV) Syllabus – Anna University Regulation 2021

COURSE OBJECTIVES:

 To make the undergraduate level students to understand the concepts of pattern recognition, feature extraction and other advanced methods.

UNIT I PATTERN CLASSIFIER

Overview of Pattern Recognition, Types of Pattern recognition – Discriminant Functions – Supervised Learning – Parametric Estimation – Maximum Likelihood Estimation – Bayes Theorem – Bayesian Belief Network, Naive Bayesian Classifier, non-parametric density estimation, histograms, kernels, window estimators.

UNIT II CLUSTERING

Unsupervised learning – Clustering Concept – Hierarchical Clustering Procedures – Partitional Clustering – Clustering of Large Data Sets – EM Algorithm – Grid Based Clustering – Density Based Clustering.

UNIT III FEATURE EXTRACTION AND SELECTION

Entropy Minimization – Karhunen Loeve Transformation – Feature Selection Through Functions Approximation – Binary Feature Selection – K-NN.

UNIT IV HIDDEN MARKOV MODELS AND SUPPORT VECTOR MACHINES

State Machines – Hidden Markov Models: Maximum Likelihood for the HMM, The Forward and Backward Algorithm, Sum-Product Algorithm for the HMM, Scaling Factors, The Viterbi Algorithm, Extensions Of The Hidden Markov Model – Support Vector Machines: Maximum Margin Classifiers, Relevance Vector Machines.

UNIT V RECENT ADVANCES

Fuzzy Classification: Fuzzy Set Theory, Fuzzy And Crisp Classification, Fuzzy Clustering, Fuzzy Pattern Recognition – Introduction to Neural Networks: Elementary Neural Network For Pattern Recognition, Hebbnet, Perceptron, ADALINE, Back Propagation.

TOTAL:45 PERIODS
COURSE OUTCOMES:

On completion of the course, the student is expected to
CO1 Provide basic knowledge about the fundamentals of pattern recognition and its applications.
CO2 Understand about unsupervised algorithms suitable for pattern classification.
CO3 Familiarize with the feature selection algorithms and methods of implementing them in applications.
CO4 Learn about the basis of algorithms used for training and testing the dataset.
CO5 Learn basic fuzzy system and neural network architectures, for applications in pattern recognition, image processing, and computer vision.

TEXTBOOKS:

1. Andrew Webb, “Statistical Pattern Recognition”, Arnold publishers, London, Second edition, 2002.

REFERENCES

1. C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer, Second Edition, 2011.
2. R. O. Duda, P. E. Hart, D. G. Stork, “Pattern Classification”, John Wiley, 2001.
3. Narasimha Murthy, V. Susheela Devi, “Pattern Recognition”, Springer 2011.
4. Menahem Friedman, Abraham Kandel, “Introduction to Pattern Recognition Statistical, Structural, Neural and Fuzzy Logic Approaches”, World Scientific publishing Co. Ltd, 2020.
5. Robert J. Schalkoff, “Pattern Recognition Statistical, Structural and Neural Approaches”, John Wiley & Sons Inc., 1992.
6. S. Theodoridis, K. Koutroumbas, “Pattern Recognition”, Fourth Edition, Academic Press, 2009.