At a glance
Francesca Odone - UniGe | MaLGa & DIBRIS - firstname.lastname@example.org
Jul 5 2021 , Jul 9 2021
Live streaming platform TBA hopefully relaxing covid restrictions will let the course to be also delivered in-person: 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.
A certificate of attendance (2 credits suggested according to the ECTS grading scale) will be sent to all participants.
An exam certificate (no grade, 6 credits suggested according to the ECTS grading scale) will be issued to those who will take and pass the exam
CVCC is a 20 hours crash course on computer vision, including principles and algorithms and hands-on activities. The course is linked to DL. The two courses are self-contained and can be taken independently, with very little overlap, but students interested in both will be provided extra information to better relate the two subjects.
Digital images fundamentals and image formation
Image filters and image features
Video analysis: motion estimation
Multi-view systems: depth estimation
Image understanding: 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
To apply, complete the application form
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.