Course Syllabus

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To give a basic and robust understanding of digital image processing and computer vision.

Introduction to digital image formation, digital image processing, and 3D reconstruction by stereoscopy.

  1. Image formation
    • Pin-hole model;
    • Spatial and intensity sampling;
    • Sampling theorem (very short review);
    • The need for optics;
    • Defects introduced by optics;
    • Defects introduced by sensor;
    • Colour cameras (bayer, 3-sensors, stacked).
  2. A realistic pin-hole projection model
  3. Calibration of the projection model
  4. Image processing and enhancement
    • Point operators: linear and non-linear;
    • Spatial filtering;
    • Introduction to denoising algorithms;
    • Notes on image registration;
    • Simple local feature detection: point-based, statistics.
  5. Image segmentation
    • Segmentation based on local features;
    • Notes on advanced denoising algorithms.
  6. Model-based vision (just mentioning)
  7. Point-based stereometry
    • Basic terminology;
    • Triangulation and stereo-matching;
    • Stereomatching algorithms (e.g., correlation).
  8. Examples of applications of the previous concepts in optics,
    optometry, ophthalmology

Linear 3D geometry (lines, planes), linear algebra.

Classes and practices, both programming and hands-on.

Selected parts from well-known textbooks like, e.g.,

  • David A. Forsyth and Jean Ponce, "Computer Vision: A Modern Approach" 2nd edition, Pearson, 2012
  • Emanuele Trucco, Alessandro Verri, "Introductory techniques for 3D Computer Vision", Prentice Hall, 1998
  • Rafael C. Gonzalez and Richard E. Woods, "Digital Image Processing" 3rd edition, Pearson, 2007

1st semester

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Key information

Field of research
INF/01
ECTS
6
Term
First semester
Activity type
Mandatory to be chosen
Course Length (Hours)
47
Degree Course Type
2-year Master Degreee
Language
English

Staff

    Teacher

  • LM
    Letizia Marchesi
  • LP
    Luca Presotto
  • Domenico Giorgio Sorrenti
    Domenico Giorgio Sorrenti

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Bibliography

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Enrolment methods

Manual enrolments
Self enrolment (Student)