Insights
The profs uses all range of the voting (from 18 throughout 30) when making students pass, unlike other prof tendencies of using 26-28-30 in magistrale. He’s exigent but doesn’t push you during the oral exam, giving you time to explain and thinking about the question. The theory is a lot of stuff for 6 CFU if that wasn’t obvious enough. As far as i know the presentation can be done in english or italian alike. The prof can ask some questions during the presentation of the project, and since 99% of projects and papers are about machine learning, after the presentation the theory question will travel towards the general part (part 1 in my notes). This means you could selectively study the 2nd part from everything around your project in depth theory-wise and then focus on the 1st part of the course, a little easier imo. Your call, i already studied everything anyway and i’m prepared for whatever will come my way.
Summaries
Computer vision → field for information extraction from images
1. Image Formation Process
From 3D scene to 2D image, we need to
- Geometric relationships, 3D point in 2D pic
- Radiometric relationships, light info
- Digitization
Pinhole model → tiny hole that captures light rays We convert 3D points in a 2D image plane following the rules of perspective projection
Stereo model
Stereo model → using 2 images to recover 3D structure by triangulation (2 point distance difference, the higher, the further the points are in depth)
Epipole → point in one stereo image that lines with the point where the 1st photo was taken E Epipolar line → corresponding points lie on the same epipolar lines in both images
Digitalization
Sensors convert light to electrical signals Key processes:
- Sampling → space, colour discretization in pixels, hue
- Quantization → light discretization in brightness
Sensor characteristics
- Signal-to-noise ratio