Artificial vision

A.Y. 2020/2021
Overall hours
Learning objectives
Aim of the course is to give elements which allow to infer knowledge about the real world from digital images or videos. These skills mainly concern the 3D reconstruction of real objects, and the processing and recognition of elements and actions in a scene.
Expected learning outcomes
- Learn the image formation principles
- Learn the techniques to obtai 3D object reconstruction
- Learn machine learning techniques aimed at the identification and recognition of objects and actions in images or videos
Course syllabus and organization

Single session

Lesson period
Second semester
Course syllabus
Aim of this course is to examine the fundamental concepts in the field of computer vision, with special focus on the following topics:
- image formation process;
- camera calibration;
- motion analysis from video sequences;
- 3D reconstruction from stereo images;
- supervised image classification.
Prerequisites for admission
A good knowledge of the fundamentals of:
- probability and statistics
- signal and image processing
as taught in scientific undergraduate courses.
Teaching methods
Lectures and class exercises.
Teaching Resources
Web site:

Class material:
- Lecture slides
- Software code for exercises

Testo di riferimento:
- D.A. Forsyth, J. Ponce - Computer Vision - A Modern Approach - Pearson, 2nd edition
Assessment methods and Criteria
Written test. The grade obtained in the written test can be optionally improved by carrying out an extra project assigned by the teacher.
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours