|CREDITS||6 credits during the 1st year of 10852 COMPUTER SCIENCE (LM-18) GENOVA|
|SCIENTIFIC DISCIPLINARY SECTOR||INF/01|
The course offers an introduction to state-of-the-art methods for visual data analysis. In particular it deals with image and video understanding.
Learning how to represent image content adaptively by means of shallow or deep computational models and biologically-inspired hierarchical models, and how to tackle image classification and categorization problems.
Students will be provided with an an overview of state-of-the-art methods for modeling and understanding the semantics of a scene. Students will get acquainted with the problem of representing the image content adaptively by means of shallow or deep computational models. Then it will address image classification and categorization problems. Possible extensions to depth and motion information will also be discussed.
Students will be involved in project activities.
Calculus and linear algebra.
Digital image processing and machine learning principles.
Theoretical classes complemented by practical activities
material provided by the instructors (slides and papers), see course Aulaweb page
additional reference online book http://szeliski.org/Book/
FRANCESCA ODONE (President)
LORENZO ROSASCO (President Substitute)
ANNALISA BARLA (Substitute)
ALESSANDRO VERRI (Substitute)
All class schedules are posted on the EasyAcademy portal.
|21/07/2022||09:00||GENOVA||Esame su appuntamento|
|22/07/2022||09:00||GENOVA||Esame su appuntamento|
|15/09/2022||09:00||GENOVA||Esame su appuntamento|
|16/09/2022||09:00||GENOVA||Esame su appuntamento|
|09/02/2023||09:00||GENOVA||Esame su appuntamento|
|10/02/2023||09:00||GENOVA||Esame su appuntamento|