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 future freshmen who will enroll for the 2025/2026 academic year.If you are already enrolled in this course of study, consult the information available on the course page:
Laurea magistrale in Computer Engineering for Intelligent Systems [LM-32] - Enrollment until 2024/2025The 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
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2° Year It will be activated in the A.Y. 2026/2027
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4 modules among:
- 1st year - Embedded operating systems, Embedded & IoT Systems design, Robotics, Computer vision, Advanced visual computing and 3D modeling - delivered in 2025/2026
- 2nd year - Advanced control systems - delivered in 2026/20273 modules among:
- 2nd year - Advanced methods for biomedical signal processing, Neurohealth, Medical robotics, Internet of Medical things - delivered in 2026/2027
- 1st or 2nd year - Mathematical modeling for Industrial and medical digital twins, Cloud computing and distributed systems - delivered in 2025/2026 or in 2026/2027 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.
Computer Vision (2025/2026)
Teaching code
4S009013
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Period
1st semester dal Oct 1, 2025 al Jan 30, 2026.
Courses Single
Authorized
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, fundamental matrix, triangulation, homographies
- Multiple-view geometry: trifocal tensor, multifocal tensor, factorization
- Calibration, uncalibrated vision, auto-calibration, duality,uncertainty
Bibliography
Didactic methods
The course is organized in 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!
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
