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

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.

1° Year

ModulesCreditsTAFSSD
9
B
ING-INF/04
Compulsory activities for Embedded & Iot Systems
Compulsory activities for Robotics Systems
6
B/C
INF/01
6
B/C
ING-INF/05
Compulsory activities for Smart Systems & Data Analytics
6
B/C
INF/01 ,ING-INF/06
6
B/C
ING-INF/05

2° Year  activated in the A.Y. 2022/2023

ModulesCreditsTAFSSD
Compulsory activities for Embedded & Iot Systems
Compulsory activities for Robotics Systems
Compulsory activities for Smart Systems & Data Analytics
6
B/C
ING-INF/05
Final exam
24
E
-
ModulesCreditsTAFSSD
9
B
ING-INF/04
Compulsory activities for Embedded & Iot Systems
Compulsory activities for Robotics Systems
6
B/C
INF/01
6
B/C
ING-INF/05
Compulsory activities for Smart Systems & Data Analytics
6
B/C
INF/01 ,ING-INF/06
6
B/C
ING-INF/05
activated in the A.Y. 2022/2023
ModulesCreditsTAFSSD
Compulsory activities for Embedded & Iot Systems
Compulsory activities for Robotics Systems
Compulsory activities for Smart Systems & Data Analytics
6
B/C
ING-INF/05
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
Between the years: 1°- 2°
Further activities
3
F
-
Between the years: 1°- 2°
Training
3
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.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S009013

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

The teaching is organized as follows:

Teoria

Credits

5

Period

Primo semestre

Academic staff

Umberto Castellani

Laboratorio

Credits

1

Period

Primo semestre

Academic staff

Umberto Castellani

Learning outcomes

This course is aimed at providing the student with the practical and theoretical tools that enables the recovery of the three-dimensional structure of a scene starting from its two-dimensional projections, the images. Formal methods are provided for image acquisition and extraction of 3D information on several applicative contexts by focusing on the processing of real and noisy data. At the end of the course, the student will be able to implement a new vision system, also in a research context, through the integration of different formal methods on the 3D estimation from images and the use of different acquisition sensors. This knowledge will allow the student to: i) exploit the knowledge of computer vision on different applicative scenarios; ii) master the analysis of real and heterogeneous data; iii) address real time performances. At the end of the course, the student will be able to: i) identify the vision method most suitable to the involved applicative context, and customize the vision system involving other disciplines like machine learning; ii) continue independently his/her studies in the field of computer vision and analysis of 3D data independently.

Program

- Geometry of the pinhole camera
- Calibration
- Epipolar geometry
- Triangulation
- Planes and homographies
- Structure and motion from images
- Autocalibration
- Dealing with noise and outliers
- Image matching
- Non-rigid objects
- Laboratory exercise

Bibliography

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Examination Methods

The exam can be obtained with three different options:
A) Oral with discussion on lab exercise (max 28/30, average between the two modalities).
B) Project with discussion on lab exercise (max 28/30, average between the two modalities).
C) Oral+Project (average between the two modalities).
Oral is a discussion on the program. The aim is to verify the knowledge of theoretical and practical aspects of involved topics.
The discussion of lab exercise consists of the delivering of an archive with the scripts that implement the vision algorithms described in the program. The discussion aims at verifying the correct practical implementation of the theoretical aspects addressed during the course.
The project is focused on a specific and innovative topic that is identified with the teacher. The topic can be an open issue of the state of the art or a specific applicative theme. The student will be able to generalize the knowledge acquired during the course for the solution of new computer vision problems.

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