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
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:
Laurea magistrale in Ingegneria e scienze informatiche - Enrollment from 2025/2026The 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
Modules | Credits | TAF | SSD |
---|
2° Year activated in the A.Y. 2014/2015
Modules | Credits | TAF | SSD |
---|
Modules | Credits | TAF | SSD |
---|
Modules | Credits | TAF | SSD |
---|
Modules | Credits | TAF | SSD |
---|
Tre insegnamenti a scelta tra i seguenti
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.
Advanced recognition systems (2014/2015)
Teaching code
4S02792
Teacher
Coordinator
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
II sem. dal Mar 2, 2015 al Jun 12, 2015.
Learning outcomes
The course of Advanced Pattern Recognition Systems aims at providing the students with practical software instruments for solving real recognition problems, as those coming from the surveillance/quality-control/automotive/entertainment scenarios. To this sake, the lessons are organized as practical problems, which will be faced by considering theory elements coming from the Pattern Recognition course, and embedding them into MATLAB or C software. It is clear that the Laboratory sessions in this course are fundamental, and will take most of the time.
Program
PROBLEM: Detection and recognition of: people, faces, scenes and objects in general. Associated algorithms and techniques: Generative Learning, Discriminative Learning, Hybrid Learning, Boosting, GIST, SIFT (reminders of), covariances, SURF, SDALF, Bag of Words.
PROBLEM: Modeling of moving objects: tracking of single objects, groups of objects, action recognition, expression recognition. Associated algorithms and techniques: Monte Carlo Methods (Particle Filtering), Online Learning, Spline, Snakes
Examination Methods
Project or Seminar
Teaching materials e documents
-
Lab.1 - Valutazione classificatori (zip, it, 400 KB, 3/20/15)
-
Lab.2 - Classificatori discriminativi (pdf, it, 74 KB, 3/20/15)
-
Lab.3 - Riconoscimento oggetti (zip, it, 8 KB, 4/17/15)
-
Lab.4 - Motion detection (zip, it, 4 KB, 5/15/15)
-
Lab.5 - Descrittori di forma (zip, it, 1877 KB, 5/29/15)
-
Lez.0 - Introduzione al corso (pdf, it, 7978 KB, 3/10/15)
-
Lez.1 - Valutazione classificatori (zip, it, 5204 KB, 3/10/15)
-
Lez.2 - Classificatori Discriminativi (zip, it, 4803 KB, 4/10/15)
-
Lez.3 - Riconoscimento di oggetti (zip, it, 15978 KB, 4/10/15)
-
Lez.4 - Riconoscimento del movimento (zip, it, 7103 KB, 5/12/15)
-
Lez.5 - Descrittori di forma (zip, it, 6714 KB, 5/26/15)