COMPUTER VISION E LABORATORIOModule COMPUTER VISION
Academic Year 2023/2024 - Teacher: SEBASTIANO BATTIATOExpected Learning Outcomes
To provide an introduction to the computer vision field by presenting the main theroretical basis and related applicative scenario.
Both theoretical and prcatical aspects will be introduced.
Course Structure
Oral lectures with several practical sessions
Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
Detailed Course Content
Image formation, 2d 3d calibration - Stereo
Low Level Vision: Filters and Features: Edges, Texture, Laplacian Pyramid,Corner Detection (Harris, …), SIFT
Mid level vision: Segmentation , Image Video Restoration
Medical Imaging, DeepFake e Generative AI, Face Recognition
Textbook Information
- E. Trucco, A. Verri, “Introductory Techniques for 3-D Computer Vision”, Prentice Hall, 1998
- G. Bradski, A. Kaehler, “Learning OpenCV Computer Vision with the OpenCV Library” O'Reilly Media, 2008
- Mubarak Shah, "Fundamentals of Computer Vision" (pdf), 1997
- R. Hartley and A. Zisserman, “Multiple View Geometry in Computer Vision”, 2004
- D. A. Forsyth, J. Ponce, “Computer Vision a Modern Approach”, Prentice Hall PTR, 2002
- Richard Szeliski, Computer Vision: Algorithms and Application, Springer 2010
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | Fundamental Matrix; Estrinsic and Intrinsic Paramters; Camera Calibration | Chapters 1-3 Trucco/Verri |
| 2 | Low Level vision | Forsyth/Ponce, Zseliski |
| 3 | SIFT and related issues | Fundamentals of Computer Vision |
| 4 | Mid Level vision | Forsyth/Ponce, Zseliski |
| 5 | Face Recognition and Detection | Lecture notes by the professor |
| 6 | Generative AI e Deepfake | Lecture notes by the professor |
| 7 | Medical Imaging | Lecture notes by the professor |
| 8 | Image Video REstoration | Lecture notes by the professor |