At a glance
Francesca Odone - UniGe | MaLGa & DIBRIS - email@example.com
Jul 5 2021 , Jul 9 2021
either Teams or Via Dodecaneso 35, Genova, Italy
Application deadline: May 16
Notification of acceptance: June 1
Visual perception, as a key element of Artificial Intelligence, allows us to build smart systems sensitive to surrounding environments, interactive robots, video-cameras with real time algorithms running on board. With similar algorithms our smart-phones can log us in by recognizing our face, read text automatically, improve the quality of the photos we shoot.
At the core of these applications are computer vision models, often boosted by machine learning algorithms.
This crash course will present the basic principles of computer vision and visual perception in artificial agents. It will include theoretical classes, application examples, hand-on activities.
Students will be provided with an overview of state-of-the-art methods for modelling and understanding the surrounding scene or an image content. In the first part we will present elements of classical computer vision (feature detection, depth estimation, motion analysis). In a second part, students will get acquainted with the problem of representing and understanding the image content adaptively by means of shallow or deep machine learning algorithms.
The course is linked to the introductory Deep Learning course. The two courses are self-contained and can be taken independently, but students interested in both will be provided extra information to better relate the two subjects.
computer vision main tasks, digital images fundamentals, and image formation
Image filters and image features
classification and detection of objects in images
Once accepted, each candidate has to follow the instructions in the acceptance email and proceed with the payment. The registration fee is non-refundable.
students and postdocs: waived
professionals: EUR 150
UniGe students and IIT affiliates: no fee
Issa Mouawad - UniGe | MaLGa & DIBRIS - firstname.lastname@example.org
Modiana Pasquinelli - UniGe | MaLGa & DIBRIS - email@example.com
Slides and readings will be provided.
Some reference books:
E. Trucco, A. Verri Introductory Techniques for 3-D Computer Vision Prenctice Hall 1998
R. Szeliski Computer Vision: Algorithms and Applications https://szeliski.org/Book/
I. Goodfellow, Y. Bengio, A. Courville Deep Learning https://www.deeplearningbook.org/
The course is linked to the course Deep Learning: hands on introduction. The two courses are self-contained and can be taken independently, but students interested in both will be provided extra information to better relate the two subjects.