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Syllabus |
1. Analog and Discrete Time Signals
(Representation model, Linear and Time Invariant Systems, Time and Frequency Representation, Sampling)
2. Filters
(Bode Diagrams, Analog Filters, Digital Filters - IIR and FIR)
3. Random Signals
(Random signal definition, Stochastic signals, Ergodic processes, Stationary signals, Correlation Functions, Power Spectrum Function, Wiener Filter, Kalman Filter)
4. Image Analysis and Processing
(Image Acquisition and Processing, Bidimensional Convolution, Image Filtering, Bidimensional Transforms, Image Spectrum Analysis, Bidimensional FIR, Bidimensional Estimating
Filters - Wiener and Kalman, Binary and Multilevel Morphological Image Filtering, Image analysis basis, Edge Detectors, Image Segmentation, Image Description)
5. Pattern Recognition
(Signal and Image Classification, Classification Techniques, Deep learning)
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Main Bibliography |
1. S. Haykin and B. Van Veen, Signals and Systems, John Wiley & Sons, New Jersey, 2nd edition, 2003.
2. Monsoon H. Hayes, Statistical Digital Signal Processing and Modeling, John Wiley & Sons, New York, USA, 1996.
3. William K. Pratt, Digital Image Processing, John Wiley & Sons, Inc.,3rd edition, 2001.
4. Linda G. Shapiro and George C. Stockman, Computer Vision, Prentice Hall, Upper Saddle River, New Jersey, 2001.
5. J.G. Proakis and D.G. Manolakis, Digital Signal Processing: Principles, Algorithms, Prentice Hall, New Jersey, 4th edition, 1996.
6. John W. Woods, Multidimensional Signal, Image and Video Processing and Coding, Academic Press, 2006.
7. Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification, Wiley Interscience, 2nd edition, 2000.
8. S. Theodoridis, K. Koutroumbas, Pattern Recognition, Academic Press, 4th edition, 2009.
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