Teaching
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. Richard Szeliski, Computer Vision: Algorithms and Applications, Springer, 2011
4. David. A. Forsyth, Jean Ponce, Computer Vision: A Modern Approach, Pearson, 2011
5. E. Trucco, A. Verri, “Introductory Techniques for 3-D Computer Vision”, Prentice Hall, 1998.
6. S. Nedevschi, R. Danescu, F. Oniga, T. Marita, Tehnici de viziune artificiala in conducerea automata a autovehiculelor, Editura UT Press, 2012
7. S. Nedevschi, T. Marita, R. Danescu, F. Oniga, R. Brehar, I. Giosan, S. Bota, A. Ciurte, A. Vatavu, Image Processing - Laboratory Guide, UT Press, 2016
Pattern Recognition
Lecture Curricula:
Introduction
Probability Review
Bayesian Decision Theory 1
Bayesian Decision Theory 2
Maximum Likelihood and K Nearest Neighbors Estimation
Kernel Density Estimation
Linear Discriminant Functions, Perceptron
Multilayer Perceptron
Kernel Methods
Support Vector Machines
Ensemble Methods
Clustering Methods
Feature Selection and Performance Estimation
Textbooks and references:
1. R. O. Duda, E. Hart, D. G. Stork, “Pattern Classification”, John Wiley [[&]] Sons, 2001
2. S. Theodoridis, K. Koutroumbas, “Pattern Recognition”, 4th edition, Academic Press, 2008.
3. C.M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006
4. Murphy, “Machine Learning: A Probabilistic Perspective”, The MIT Press, 2012
5. I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, The MIT Press, 2016
6. Convolutional Neural Networks for Visual Recognition, http://cs231n.stanford.edu/, 2019
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/
2. David Forsyth, Jean Ponce “Computer Vision A Modern Approach”, Pearson, 2002
3. S. Nedevschi, R. Danescu, F. Oniga, T. Marita, “Tehnici de viziune artificiala in conducerea automata a autovehiculelor“, Editura UT Press, 2012
4. Daniel Scharstein, Richard Szeliski, “A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms”, IJCV
5. Heiko Hirschmuller, “Stereo Processing by Semiglobal Matching and Mutual Information”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 30, NO. 2, FEBRUARY 2008
6. Raphael Labayrade and Didier Aubert, “IN-VEHICLE OBSTACLES DETECTION AND CHARACTERIZATION BY STEREOVISION”
7. Zhencheng Hu, Francisco Lamosa and Keiichi Uchimura,”A Complete U-V-Disparity Study for Stereovision Based 3D Driving Environment Analysis”
8. Ciprian Pocol, Sergiu Nedevschi, Marc-Michael Meinecke, “Obstacle Detection Based on Dense Stereovision for Urban ACC Systems, WIT 2010, Hamburg, Germany
9. IEEE Transactions on Pattern Analyses and Machine Intelligence
10. IEEE Transactions on Intelligent Transportation Systems
11. IEEE Transactions on Image Processing
12. CVPR publications
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. IEEE Transactions on Pattern Analyses and Machine Intelligence
5. IEEE Transactions on Image Processing
6. IEEE Transactions on Intelligent Transportation Systems
