GI3019: Pattern Recognition (Satellite, Aerial, UAV) syllabus for Geo 2021 regulation (Professional Elective-III)

Pattern Recognition (Satellite, Aerial, UAV) detailed syllabus for Geoinformatics Engineering (Geo) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the Geo students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Geoinformatics Engineering 5th Sem scheme and its subjects, do visit Geo 5th Sem 2021 regulation scheme. For Professional Elective-III scheme and its subjects refer to Geo Professional Elective-III syllabus scheme. The detailed syllabus of pattern recognition (satellite, aerial, uav) is as follows.

Course Objectives:

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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

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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.

Course Outcomes:

•On completion of the course, the student is expected to

1. Provide basic knowledge about the fundamentals of pattern recognition and its applications.
2. Understand about unsupervised algorithms suitable for pattern classification.
3. Familiarize with the feature selection algorithms and methods of implementing them in applications.
4. Learn about the basis of algorithms used for training and testing the dataset.
5. Learn basic fuzzy system and neural network architectures, for applications in pattern recognition, image processing, and computer vision.

Text Books:

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Reference Books:

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.

For detailed syllabus of all the other subjects of Geoinformatics Engineering 5th Sem, visit Geo 5th Sem subject syllabuses for 2021 regulation.

For all Geoinformatics Engineering results, visit Anna University Geo all semester results direct link.