IMAGE AND VIDEO UNDERSTANDING

The course aims at introducing the student to the principles, the algorithms and the main applications in the field of image and video understanding.

1. Knowledge and understanding
1.1. acquire the main models and algorithms of image and video understanding

2. Ability to apply knowledge and understanding
2.1. acquire the ability to apply the studied models to real problems
2.2. acquire the ability to critically assess the performance and the behavior of a model applied to a concrete problem

3. Judgement
3.1. ability to understand which characteristics of the various models of artificial intelligence are best suited to a given problem
3.2. ability to critically evaluate the theoretical characteristics of the proposed models

The student is expected to be familiar with the basic concepts of calculus and linear algebra.

Detection and recognition: face detection, eigenfaces, human detection, object recognition and categorization.

Image and video segmentation.

Tracking and re-identification in videos.

Machine learning and (deep) neural network methods.

Graph-based methods.

- R. Szeliski, Computer Vision: Algorithms and Applications. Springer.

- D. Forsyth and J. Ponce. Computer Vision: A modern Approach. Pearson.

- I. Goodfellow, Y. Bengio and A. Courville. Deep Learning. MIT Press

The exam consists of an oral test together with a discussion of a project agreed before with the teacher.

Powerpoint presentations and chalk talk.

English

To favor an "active" appraoch to the study of the topics covered in the classes, students will be asked to develop a simple project which will be discussed during the oral examination.

oral