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.

A.A. 2018/2019

Academic calendar

The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.

Academic calendar

Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..

Definition of lesson periods
Period From To
I semestre Oct 1, 2018 Jan 31, 2019
II semestre Mar 4, 2019 Jun 14, 2019
Exam sessions
Session From To
Sessione invernale d'esame Feb 1, 2019 Feb 28, 2019
Sessione estiva d'esame Jun 17, 2019 Jul 31, 2019
Sessione autunnale d'esame Sep 2, 2019 Sep 30, 2019
Degree sessions
Session From To
Sessione Estiva Jul 18, 2019 Jul 18, 2019
Sessione Autunnale Oct 17, 2019 Oct 17, 2019
Sessione Invernale Mar 18, 2020 Mar 18, 2020
Holidays
Period From To
Sospensione dell'attività didattica Nov 2, 2018 Nov 3, 2018
Vacanze di Natale Dec 24, 2018 Jan 6, 2019
Vacanze di Pasqua Apr 19, 2019 Apr 28, 2019
Festa del Santo Patrono May 21, 2019 May 21, 2019
Vacanze estive Aug 5, 2019 Aug 18, 2019

Exam calendar

Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.

Exam calendar

Should you have any doubts or questions, please check the Enrolment FAQs

Academic staff

B C D F G L M O P Q R S V

Belussi Alberto

alberto.belussi@univr.it +39 045 802 7980

Bombieri Nicola

nicola.bombieri@univr.it +39 045 802 7094

Bonacina Maria Paola

mariapaola.bonacina@univr.it +39 045 802 7046

Boscaini Maurizio

maurizio.boscaini@univr.it

Busato Federico

federico.busato@univr.it

Calanca Andrea

andrea.calanca@univr.it +39 045 802 7847

Carra Damiano

damiano.carra@univr.it +39 045 802 7059

Castellani Umberto

umberto.castellani@univr.it +39 045 802 7988

Cicalese Ferdinando

ferdinando.cicalese@univr.it +39 045 802 7969

Cristani Matteo

matteo.cristani@univr.it 045 802 7983

Cristani Marco

marco.cristani@univr.it +39 045 802 7841

Cubico Serena

serena.cubico@univr.it 045 802 8132

Dall'Alba Diego

diego.dallalba@univr.it +39 045 802 7074

Dalla Preda Mila

mila.dallapreda@univr.it

Farinelli Alessandro

alessandro.farinelli@univr.it +39 045 802 7842

Favretto Giuseppe

giuseppe.favretto@univr.it +39 045 802 8749 - 8748

Fummi Franco

franco.fummi@univr.it 045 802 7994

Giachetti Andrea

andrea.giachetti@univr.it +39 045 8027998

Giacobazzi Roberto

roberto.giacobazzi@univr.it +39 045 802 7995

Lovato Pietro

pietro.lovato@univr.it +39 045 802 7035

Maris Bogdan Mihai

bogdan.maris@univr.it +39 045 802 7074

Masini Andrea

andrea.masini@univr.it 045 802 7922

Mastroeni Isabella

isabella.mastroeni@univr.it +39 045 802 7089

Menegaz Gloria

gloria.menegaz@univr.it +39 045 802 7024

Merro Massimo

massimo.merro@univr.it 045 802 7992

Muradore Riccardo

riccardo.muradore@univr.it +39 045 802 7835

Oliboni Barbara

barbara.oliboni@univr.it +39 045 802 7077

Paci Federica Maria Francesca

federicamariafrancesca.paci@univr.it +39 045 802 7909

Pravadelli Graziano

graziano.pravadelli@univr.it +39 045 802 7081

Quaglia Davide

davide.quaglia@univr.it +39 045 802 7811

Rizzi Romeo

romeo.rizzi@univr.it +39 045 8027088

Romeo Alessandro

alessandro.romeo@univr.it +39 045 802 7974-7936; Lab: +39 045 802 7808

Segala Roberto

roberto.segala@univr.it 045 802 7997

Setti Francesco

francesco.setti@univr.it +39 045 802 7804

Villa Tiziano

tiziano.villa@univr.it +39 045 802 7034

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 enrolment year.

CURRICULUM TIPO:
ModulesCreditsTAFSSD
12
B
(ING-INF/05)
12
B
(ING-INF/05)
6
B
(ING-INF/05)
6
B
(ING-INF/05)
ModulesCreditsTAFSSD
6
B
(INF/01)
6
B
(ING-INF/05)
Other activities
4
F
-
Final exam
24
E
-

1° Year

ModulesCreditsTAFSSD
12
B
(ING-INF/05)
12
B
(ING-INF/05)
6
B
(ING-INF/05)
6
B
(ING-INF/05)

2° Year

ModulesCreditsTAFSSD
6
B
(INF/01)
6
B
(ING-INF/05)
Other activities
4
F
-
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°2 modules among the following
6
C
(INF/01)
6
C
(INF/01)
6
C
(SECS-P/10)
6
C
(INF/01)
Between the years: 1°- 2°

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.




SPlacements in companies, public or private institutions and professional associations

Teaching code

4S001409

Credits

6

Coordinatore

Andrea Giachetti

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Language

Italian

The teaching is organized as follows:

Teoria

Credits

5

Period

I semestre

Academic staff

Andrea Giachetti

Laboratorio

Credits

1

Period

I semestre

Academic staff

Andrea Giachetti

Learning outcomes

The course aims to provide the knowledge necessary to understand and use algorithms to process digital images and different types of spatially related data (volumes and surfaces). Therefore, algorithms and data structures will be presented to effectively code the data, segment the regions of interest, characterize with descriptors, recognize objects and align the structures (registration).

At the end of the course, the student will have to demonstrate knowledge and understanding skills that allow him to exploit data acquired by multimodal probes to perform 3D reconstruction, measurement, recognition and information fusion.

In addition, the student must demonstrate that he is able to use notions of computational geometry, algebra, and algorithms on graphs to solve practical problems in various application contexts, autonomously select the most appropriate data structures and the best algorithms.

The student will then be able to present an application project describing effectively motivations and choices, continuing the studies independently in the Visual Computing domain.

Program

Theory
1 Introduction
Spatially referenced data in 2D and 3D, scattered data, gridded data, sensors
2 Data structures
Images and volumes, binary and label data, topology. Scattered data, triangulation and interpolation
Surface and contour representations: curve approximations, splines; point clouds, meshes, meshing algorithms Moving Least Squares, ball pivoting.
3 Segmentation
Classification-based approaches, graph based approaches,
4 Contour/surface based segmentation
Snakes and active surfaces 3D, Level Sets, fast marching algorithm, distance maps and geodesic distances
5 Model based segmentation ,
Model fitting Hough transform, statistical modelling with training data: Point Distribution Models
6 Shape analysis
Moments, binary region descriptors, invariance properties.
Contour based descriptors, local features, signatures, Fourier descriptors, context in local descriptors.
Skeletons and medial lines,
Differential geometry of surfaces, descriptors on meshes
7 Image and texture features
8 Spatial registration
Problem descriptions and approaches. Landmarks based registration,
Intensity based registration


Laboratorio Matlab
Introduzione a Matlab, images, contours, sampling and interpolation, splines
Voxel based segmentation
2D Snake implementation
2D Shape retrieval
Mesh processing, 3D shape retrieval

Examination Methods

Written test (20/30) and evaluation of programming skills (10/30)

To pass the exam, the student must show
- they have understood the principles related to surface and volume Data encoding and processing
- they are able to describe these concepts in a clear and exhaustive way
- they are able to apply the acquired knowledge in different applicative contexts

Written test:
The written test is composed by a few open questions and/or exercises testing the understanding of the different topics of the course.

Laboratory:
The lab exam will consist of a small project applying methods learned in the course to application specific data

Type D and Type F activities

Modules not yet included

Career prospects


Module/Programme news

News for students

There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.

Gestione carriere


Graduation

List of theses and work experience proposals

theses proposals Research area
Analisi ed identificazione automatica del tono/volume della voce AI, Robotics & Automatic Control - AI, Robotics & Automatic Control
Analisi e percezione dei segnali biometrici per l'interazione con robot AI, Robotics & Automatic Control - AI, Robotics & Automatic Control
Integrazione del simulatore del robot Nao con Oculus Rift AI, Robotics & Automatic Control - AI, Robotics & Automatic Control
BS or MS theses in automated reasoning Computing Methodologies - ARTIFICIAL INTELLIGENCE
Sviluppo sistemi di scansione 3D Computing Methodologies - COMPUTER GRAPHICS
Sviluppo sistemi di scansione 3D Computing Methodologies - IMAGE PROCESSING AND COMPUTER VISION
Dati geografici Information Systems - INFORMATION SYSTEMS APPLICATIONS
Analisi ed identificazione automatica del tono/volume della voce Robotics - Robotics
Analisi e percezione dei segnali biometrici per l'interazione con robot Robotics - Robotics
Integrazione del simulatore del robot Nao con Oculus Rift Robotics - Robotics
BS or MS theses in automated reasoning Theory of computation - Logic
BS or MS theses in automated reasoning Theory of computation - Semantics and reasoning
Proposte di tesi/collaborazione/stage in Intelligenza Artificiale Applicata Various topics
Proposte di Tesi/Stage/Progetto nell'ambito delle basi di dati/sistemi informativi Various topics

Attendance

As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance at the course of study is not mandatory.
Please refer to the Crisis Unit's latest updates for the mode of teaching.

Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.