Image description and modeling

Code:

MVCOMP03

Acronym:

DMI

Keywords
Classification	Keyword
CNAEF	Informatics Sciences

Instance: 2023/2024 - 1S

Active?	Yes
Responsible unit:	Department of Electrical and Computer Engineering
Course/CS Responsible:	Master in Computer Vision

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MVCOMP	5	Syllabus	1	-	6	42	162

Teaching language

English

Objectives

To know the fundamental characteristics of the digital image and its forms of representation.
Description of the visual content through local characteristics of color, shape and texture.
Apply the techniques of modeling and image representation to the problems of image processing and analysis.

Learning outcomes and competences

It is intended that the student identify the different problems associated with Image Description and Modeling. The student should acquire / consolidate knowledge in the area of Image description and modeling. In this scope the student will deepen technological tools (libraries in python; openCV). These tools support the future development of image description and modeling systems. The student should acquire learning skills that allow to continue studying in a way that will be largely self-directed or autonomous.

Working method

Presencial

Program

Image representation and modeling: space-frequency, orientation and phase, space-scale.
Wavelets and filter banks.
Image coding and reconstruction.
Description of color, shape and texture.
Applications of modeling and description of images.

Mandatory literature

Al Bovik; The essential guide to video processing. ISBN: 978-0-12-374456-2

Teaching methods and learning activities

Participatory lectures, practices in computer rooms, use of the virtual classroom, learning based on the resolution of practical cases, autonomous work and independent student study, group work and cooperative learning.

An active learning system is focused to stimulate the student to make his own research on the matters presented in the classes. During the classes computational/ experimental works are given to the student, to work in group, in order to gain experience with technology and concepts.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Teste	40,00
Trabalho escrito	60,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	60,00
Frequência das aulas	42,00
Trabalho escrito	60,00
Total:	162,00

Eligibility for exams

.

Calculation formula of final grade

The projects/practical assignments covering the course topics during the whole duration of the course will account for 60% of the final grade. The final exam will account for 40% of the final grade.