Academic Year 2023/2024 - Teacher: SEBASTIANO BATTIATO

Expected 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

  1. E. Trucco, A. Verri, “Introductory Techniques for 3-D Computer Vision”, Prentice Hall, 1998
  2. G. Bradski, A. Kaehler, “Learning OpenCV Computer Vision with the OpenCV Library” O'Reilly Media, 2008
  3. Mubarak Shah, "Fundamentals of Computer Vision" (pdf), 1997
  4. R. Hartley and A. Zisserman, “Multiple View Geometry in Computer Vision”, 2004
  5. D. A. Forsyth, J. Ponce, “Computer Vision a Modern Approach”, Prentice Hall PTR, 2002
  6. Richard Szeliski, Computer Vision: Algorithms and Application, Springer 2010

Course Planning

 SubjectsText References
1Fundamental Matrix; Estrinsic and Intrinsic Paramters; Camera CalibrationChapters 1-3 Trucco/Verri
2Low Level visionForsyth/Ponce, Zseliski
3SIFT and related issuesFundamentals of Computer Vision
4Mid Level visionForsyth/Ponce, Zseliski
5Face Recognition and DetectionLecture notes by the professor
6Generative AI e DeepfakeLecture notes by the professor
7Medical ImagingLecture notes by the professor
8Image Video REstorationLecture notes by the professor