Organisation
Bachelor: 3h/week course and 2h/week laboratory, Winter semester
Lecturer: Sorin Grigorescu
Laboratory: Sorin Grigorescu
Language: Romanian
Content
| Lecture | Module | Description | Course Materials |
| 1 | Introduction | Introduction to computer vision | slides |
| 2 | Image formation and color spaces | ||
| 3 | Filtering and Segmentation |
Image representation and noise | slides |
| 4 | Spatial filtering | ||
| 5 | Template matching | ||
| 6 | Region segmentation | slides | |
| 7 | Edge detection | ||
| 8 | Hough transform | ||
| 9 | Object Recognition |
Linear regression | slides |
| 10 | Logistic regression | ||
| 11 | Neural Networks | ||
| 12 | Convolutional Neural Networkds | slides | |
| 13 | Optics and 3D Reconstruction |
Ideal camera model | slides |
| 14 | Camera calibration | ||
| 15 | Stereo vision | slides | |
| 16 | Epipolar geometry and the fundamental matrix | ||
| 17 | Points of interest and correspondence matching | ||
| 18 | Object tracking |
Optical flow | slides |
| 19 | Dynamic models for object tracking |
Laboratory work
| Lab | Description | Lab Materials |
| 1 | Development of a computer vision application | details |
| 2 | Image manipulation | details |
| 3 | Thresholding | details |
| 4 | Edge detection | details |
| 5 | Correspondence points detection and 3D reconstruction | details |
| 6 | RGB-D data processing | details |
| 7 | Iterative Closest Point | details |
| 8 | Cluster extraction | details |
| 9 | Face detection | details |
| 10 | Object tracking | details |
Examination
Written and practical exam at the end of the semester.