Teaching

Pattern Recognition

Lecture Curricula:

Introduction
Probability Review
Bayesian Decision Theory 1
Bayesian Decision Theory 2
Parametric Methods for Density Estimation
Nonparametric Methods for Density Estimation
Linear Discriminant Functions, Perceptron
Kernel Methods
Support Vector Machines
Image Classification Pipeline
Loss Functions and Optimization
Back Propagation and Neural Networks

Textbooks and references:

1. R. O. Duda, E. Hart, D. G. Stork, “Pattern Classification”, John Wiley and Sons, 2001
2. S. Theodoridis, K. Koutroumbas, “Pattern Recognition”, 4th edition, Academic Press, 2008.
3. C.M. Bishop, “Pattern Recognition and Machine Learning”, second edition, Springer, 2016
4. K.P. Murphy, “Machine Learning: A Probabilistic Perspective”, The MIT Press, 2012
5. K.P. Murphy, “Probabilistic Machine Learning: An Introduction”, The MIT Press, 2022
6. I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, The MIT Press, 2016
7. Convolutional Neural Networks for Visual Recognition, http://cs231n.stanford.edu/, 2019
8. S. Nedevschi, “Pattern Recognition Systems – Lecture Notes”, TUCN 2022

Lab curricula:

Leat Mean Squares
RANSAC
Hough Transform
Distance Transform
Statistical Data Analysis
Principal Component Analysis
K-means Clustering
K-Nearest Neighbor Classifier
Naive Bayes Classifier
Perceptron Classifier
AdaBoost Method
Support Vector Machine

References:

1.   S. Nedevschi, R. Varga, F. Oniga, R. Brehar, I. Giosan, A. Petrovai, "Pattern Recognition Systems - Laboratory Works", UTPRESS, 2023 
2.   S. Nedevschi, R. Varga, F. Oniga, R. Brehar, I. Giosan, A. Petrovai, "Sisteme de Recunoastere a Formelor - Lucrari de Laborator", UTPRESS, 2023

Additional matherials:

OpenCV 2.4.3 project - Visual Studio 2013

OpenCV 2.4.3 project - Visual Studio 2013 - simplified

OpenCV 3.1 project - Visual Studio 2015

Introduction to OpenCV [ro]

Introduction to OpenCV [eng]

Data files for Least Mean Squares
Data files for RANSAC
Data files for Hough Transform
Data files for Distance Transform
Data files for Statistical data Analysis
Data files for k-means Clustering
Data files for K-Nearest Neighbor Classifier
Data files for Naive Bayes Classifier
Data files for Perceptron Classifier
Data files for AdaBoost Method
Data files for Support Vector Machine

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Image processing

Lecture Curricula:

Computer vision and applications. Vision system structure and functions. Image acquisition systems.
Image formation and sensing. Camera model
Binary image processing: Simple Geometric Properties.
Binary image processing: Labeling, Contour Tracing, Polygonal Approximation
Binary image processing: Mathematical Morphology
Grayscale image processing: Mathematical methods for grayscale image processing, Statistic features of the grayscale images, Histogram processing, Point Processing
Grayscale image processing: Convolution and Fourier Transform
Grayscale image processing: Noise in images
Grayscale image processing: Digital Filtering
Grayscale image segmentation: Edge based segmentation (first order differential methods).
Grayscale image segmentation: Edge based segmentation (second order differential methods, edge linking, contour closing,).
Stereo-vision basics. Epipolar geometry. Depth computation.
Color images: Color models. Color based segmentation.

Textbooks and references:

1. Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, 4th Edition, Pearson, March 30, 2017.
2. Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins , “Digital Image Processing Using MATLAB”, 2nd ed., Mc Graw Hill, 2010.
3. E. Trucco, A. Verri, “Introductory Techniques for 3-D Computer Vision”, Prentice Hall, 1998.
4. S. Nedevschi, R. Danescu, F. Oniga, T. Marita, Tehnici de viziune artificiala in conducerea automata a autovehiculelor, UTPRESS,  2012.
5. S. Nedevschi, "Image Processing - Lecture Notes", TUCN 2022.
6. S. Nedevschi, T. Marita, R. Danescu, F. Oniga, R. Brehar, I. Giosan, R. Varga, C. Vancea, "Image Processing - Laboratory Works" 2nd edition, UTPRESS, 2023.
7. S. Nedevschi, T. Marita, R. Danescu, F. Oniga, R. Brehar, I. Giosan, C. Vancea, R. Varga, "PROCESAREA IMAGINILOR -Îndrumător de laborator", Ediția a 2-a, UTPRESS, 2023. 


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Computer Vision

Lecture Curricula:

Introduction and Image Classification pipeline
Loss Functions and Optimization
Neural Networks and Backpropagation
Convolutional Neural Networks
Hardware and Software for CNN
Training Neural Networks
CNN Architectures
Detection and Segmentation
Projective Geometry
Stereovision
Corner Detectors
SIFT / SURF Features
Optical Flow
Point Cloud Segmentation
                                           

Textbooks and references:

1. Convolutional Neural Networks for Visual Recognition,  http://cs231n.stanford.edu/, 2022
2. David Forsyth, Jean Ponce “Computer Vision A Modern Approach”, 2nd edition, Pearson, 2011
3. Richard Szeliski, Computer Vision: Algorithms and Applications, 2nd edition, Springer, 2022.
4. S. Nedevschi, R. Danescu, F. Oniga, T. Marita, “Tehnici de viziune artificiala in conducerea automata a autovehiculelor“, UTPRESS,  2012
5. IEEE Transactions on Pattern Analyses and Machine Intelligence
6. IEEE Transactions on Image Processing
7. IEEE Transactions on Intelligent Transportation Systems
8. IEEE Transactions on Intelligent Vehicles
9. CVPR, ICCV, WACV, ECCV publications

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Computer Vision for Autonomous Systems

Lecture Curricula:

Introduction
Probability Review
Recursive State Estimation
Gaussian Filters
Nonparametric Filters
Robot Motion
Measurements
Mobile Robot Localization
Grid and Monte Carlo Localization
Occupancy Grid Mapping
Simultaneous Localization and Mapping
Planning and Collision Avoidance
Navigation

Textbooks and references:

1. S. Thrun, W. Burgard, D. Fox, “Probabilistic Robotics”, MIT Press, 2005
2. R. Siegwart, I. Nourbakhsh, “Autonomous Mobile Robots”, MIT Press, 2004
3. Emanuele Trucco, Alessandro Verri, “Introductory Techniques for 3-D Computer Vision”, Prentice Hall, 1998
4. S. Nedevschi, R. Danescu, F. Oniga, T. Marita, “Tehnici de viziune artificiala in conducerea automata a autovehiculelor“, UTPRESS,  2012
5. IEEE Transactions on Pattern Analyses and Machine Intelligence
6. IEEE Transactions on Image Processing
7. IEEE Transactions on Intelligent Transportation Systems
8. IEEE Transactions on Intelligent Vehicles
9. CVPR, ICCV, WACV, ECCV publications

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