COMPUTER VISION E LABORATORIOModule COMPUTER VISION
Academic Year 2024/2025 - 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
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 |
Learning Assessment
Learning Assessment Procedures
Oral Examination
Project
The test is structured so that each student is given a grade according to the following scheme:
- Not approved: the student has not acquired the basic concepts and is not able to answer at least 60% of the questions or carry out the exercises.
- 18-23: the student demonstrates minimal mastery of the basic concepts, his content connection skills are modest, he is able to solve simple exercises.
- 24-27: the student demonstrates good mastery of the course contents, his skills in connecting the contents are good, he solves the exercises with few errors.
- 28-30 cum laude (distinction): the student has acquired all the contents of the course and is able to master them completely and connect them with a critical spirit; solves the exercises completely and without errors.
Students with disabilities and/or DSA must contact the teacher and the DMI CInAP contact person sufficiently in advance of the exam date
to communicate that they intend to take the exam taking advantage of the appropriate compensatory measures.