Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

Study Plan

A.A. 2025/2026 A.A. 2024/2025

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.

It will be activated in the A.Y. 2026/2027
ModulesCreditsTAFSSD
12
C
INF/01
6
B
ING-INF/05
6
C
INF/01
1 module among the following
6
C
ING-INF/05
6
C
ING-INF/05
6
C
FIS/01
6
C
ING-INF/04
6
B
ING-INF/05
6
B
ING-INF/04
Final exam
3
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°- 3°
3
E
-
Between the years: 2°- 3°
6
F
-
Between the years: 2°- 3°
12
D
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Machine vision  (2025/2026)

Teaching code

4S012388

Academic staff

Francesco Setti, Dong Seon Cheng

Coordinator

Francesco Setti

Credits

6

Language

Italian

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Period

1st semester dal Oct 1, 2025 al Jan 30, 2026.

Courses Single

Authorized

Lessons timetable MoodleMoodle Seminars 0

Learning objectives

This course aims to provide the fundamentals of image and video acquisition and processing, focusing on the physics of digital image formation, image and video processing operations, and the application of these concepts in the engineering context. The goal is to provide the student with the tools necessary to model and solve concrete problems through the processing of visual data. At the end of the course the student must demonstrate knowledge and understanding of the main image processing techniques; have the ability to apply the acquired knowledge and to interpret the results in critical way; and knowing how to develop the skills necessary to continue his/her studies autonomously in the field of visual data analysis.

Learning assessment procedures

Written exam on all topics covered in the course, involving open-ended questions and exercises, lasting for 3 hours. The examination format is the same for both attending and non-attending students. There are no intermediate tests.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

To pass the exam, the students must show that:
- they understood the the physics of image formation and methods of image and video processing;
- they are able to apply the acquired knowledge to solve application scenarios described by means of questions and exercises.

The written exam will be evaluated with at most 33 points (each grade higher than 30 will bring to 30 cum Laude).