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.
Knowledge of the main computational vision methods, including classical methods and deep learning approaches. Ability to design and implement a CV algorithm of medium difficulty and to analyse/modify algorithms created by others. Ability to analyze the results obtained critically and exhaustively. Ability to present the methods studied with an adequate use of technical terms and tools.
Calculus and linear algebra.
Digital image processing and machine learning principles.
Theoretical classes complemented by practical activities Final project (individual or pairs)
Learn the principles and practical on: filters and local features. Image similarity: scale invariant interest points, descriptors and matching. Motion analysis and optical flow. 3D vision. Image representations. Object detection. Image segmentation Pose estimation and action recognition.
material provided by the instructors (slides and papers), see course Aulaweb page
additional reference online book http://szeliski.org/Book/
Ricevimento: Appointment by email: francesca.odone@unige.it (always specify name and surname, course name, degree name)
FRANCESCA ODONE (President)
NICOLETTA NOCETI
LORENZO ROSASCO (President Substitute)
ANNALISA BARLA (Substitute)
ALESSANDRO VERRI (Substitute)
In agreement with the calendar approved by the Degree Program Board of Computer Science.
