Computer Vision (2017/2018)

Course code
Name of lecturer
Umberto Castellani
Umberto Castellani
Number of ECTS credits allocated
Academic sector
Language of instruction
I sem. dal Oct 2, 2017 al Jan 31, 2018.

Lesson timetable

Go to lesson schedule

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.


- 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
- Laboratory exercise

Reference books
Author Title Publisher Year ISBN Note
E. Trucco, A. Verri Introductory techniques for 3D Computer Vision (Edizione 1) Prentice-Hall 1998 0132611082
R. Hartley, A. Zisserman Multiple View Geometry in Computer Vision (Edizione 2) Cambridge University Press 2004
Andrea Fusiello Visione Computazionale 2008

Assessment methods and criteria

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.