OVERVIEW

The course offers an introduction to state-of-the-art methods for visual data analysis. In particular it deals with image and video understanding.

AIMS AND CONTENT

LEARNING OUTCOMES

Learning the fundamental principles of computer vision, ranging from low-level algorithms to high-level approaches based on deep learning.

AIMS AND LEARNING OUTCOMES

At the end of the course, students will be able to:

- Understand the main computational vision methods, including classical methods and deep learning approaches.

- Design and implement a CV algorithm of medium difficulty and to analyse/modify algorithms created by others.

- Analyze the results obtained critically and exhaustively. Ability to present the methods studied with an adequate use of technical terms and tools.

PREREQUISITES

Calculus and linear algebra.

Digital image processing and machine learning principles.

TEACHING METHODS

Theoretical classes complemented by practical activities 
Final project (individual or pairs)

SYLLABUS/CONTENT

• 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.

RECOMMENDED READING/BIBLIOGRAPHY

material provided by the instructors (slides and papers), see course Aulaweb page

additional reference online book http://szeliski.org/Book/

TEACHERS AND EXAM BOARD

Exam Board

MATTEO MORO (President)

FRANCESCA ODONE (President)

LESSONS

LESSONS START

According to the calendar approved by the Degree Program Board: https://corsi.unige.it/en/corsi/11964/studenti-orario

EXAMS

EXAM DESCRIPTION

• During the semester, the instructors assign some tasks that must be completed and submitted in order to be eligible to take the exam.
• In addition, students will be required to carry out a project on a topic chosen from several options proposed by the instructors, to be done individually or in pairs.
• An oral exam, including questions related to the course content, the assignments, and the project, will constitute the final phase of the assessment.
• The final grade will be composed of 50% project and 50% oral exam.

Guidelines for students with certified Specific Learning Disorders, disabilities, or other special educational needs are available at https://corsi.unige.it/en/corsi/11964/studenti-disabilita-dsa

ASSESSMENT METHODS

• Knowledge of the main computational vision methods is assessed with the oral exam.
• Ability to design and implement a CV algorithm of medium difficulty is assessed by the final project 
• Ability analyse/modify algorithms created by others is assessed by hand-on activities.
• Ability to analyze the results obtained critically and exhaustively is assessed during the oral, with questions on the project.
• Ability to present the methods studied with an adequate use of technical terms and tools is assessed by project report and oral exam.

FURTHER INFORMATION

For further information, please refer to the course’s AulaWeb module or contact the instructor.