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. 2024/2025

This information is intended exclusively for students already enrolled in this course.
If you are a new student interested in enrolling, you can find information about the course of study on the course page:

Master's Degree in Computer Engineering for Intelligent Systems - Enrollment from 2025/2026

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.

CURRICULUM TIPO:

1° Year 

ModulesCreditsTAFSSD
6
B
ING-INF/05
12
B
ING-INF/05
6
B
ING-INF/04
12
C
ING-INF/06

2° Year   activated in the A.Y. 2025/2026

ModulesCreditsTAFSSD
3 modules among the following 
(A.A. 2025/2026 Internet of medical things not activated)
6
C
ING-INF/06
6
C
ING-INF/04 ,MED/50
6
C
ING-INF/06 ,MED/37
6
C
INF/01
6
C
INF/01 ,MED/50
6
C
MAT/07
24
E
-
activated in the A.Y. 2025/2026
ModulesCreditsTAFSSD
3 modules among the following 
(A.A. 2025/2026 Internet of medical things not activated)
6
C
ING-INF/06
6
C
ING-INF/04 ,MED/50
6
C
ING-INF/06 ,MED/37
6
C
INF/01
6
C
INF/01 ,MED/50
6
C
MAT/07
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
4 modules among the following:
- 1st year: Advanced visual computing and 3d modeling, Computer vision, Embedded & IoT systems design, Embedded operating systems, Robotics 
- 2nd year: Advanced control systems
6
B
ING-INF/04
6
B
ING-INF/05
6
B
ING-INF/05
6
B
ING-INF/05
6
B
ING-INF/04
6
B
ING-INF/05
Between the years: 1°- 2°
12
D
-
Between the years: 1°- 2°
Further activities
6
F
-

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

Computer Vision  (2024/2025)

Teaching code

4S009013

Teacher

Francesco Setti

Coordinator

Francesco Setti

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Period

Semester 1 dal Oct 1, 2024 al Jan 31, 2025.

Courses Single

Authorized

Lessons timetable MoodleMoodle Seminars 0

Learning objectives

The course will provide fundamentals of 3D computer vision. We will explore multiple view geometry in computer vision starting from projective transformations, camera models, epipolar geometry, stereovision, multicamera 3D reconstruction. We will explore the related calibration procedures, discuss problems related to uncertainty, duality and uncalibrated cameras. At the end of the course the student will have to demonstrate the ability to apply the acquired knowledge, in particular know how to design and implement a new vision/processing system of spatial data acquired with cameras and other devices.

Prerequisites and basic notions

We assume the students have a basic expertise in computer science and mathematics, i.e. linear algebra.
We also assume the students are familiar with Python programming, including array manipulation and linear algebra with numpy.
Some prior exposure to Google Colab environment and OpenCV library will be helpful, but we will provide all the necessary support.

Program

- Projective geometry and transformations 
- 2D vision: camera models, affine transformations, computation of camera matrix, two-view geometry, epipolar geometry, foundamental matrix, triangulation, homographies 
- Multiple-view geometry: trifocal tensor, multifocal tensor, factorization 
- Calibration, uncalibrated vision, auto-calibration, duality,uncertainty

Bibliography

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Didactic methods

The course is organized into lectures, exercises, and practical computer exercises.

Learning assessment procedures

The exam is composed by three parts:
1. Homework assignments: there will be four programming assignments over the semester, each of them worth 10%. Students must submit the assignment before the due date. A penalty of 1% will be applied for each day overdue up to 5 days. After 5 days, the assignment will be considered late and will be worth 4%. All the assignments must be completed to pass the exam!
2. Course Project: students will work alone or in small groups to produce a substantial project. Students will submit the project through Moodle one week before the exam in the form of both (1) a technical report, and (2) code to reproduce results.
3. Project Discussion: students will be asked to defend their project in an oral discussion. During the project discussion, students can be asked about any topic listed in the course program!

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, students must demonstrate that they:
* have understood the principles and models of 3D vision systems
* are able to present their topics in a precise and organic way
* know how to apply their knowledge acquired to solve application problems presented in the form of exercises, questions and computer projects.

Criteria for the composition of the final grade

The grade will be composed by:
* Homework Assignments (40%)
* Course Project (40%)
* Project Discussion (20%)
The student must fulfill all the parts to pass the exam.

Exam language

Inglese/English