Image description and modeling

Code:

MVCOMP03

Acronym:

DMI

Keywords
Classification	Keyword
CNAEF	Informatics Sciences

Instance: 2025/2026 - 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	7	Syllabus	1	-	6	42	162

Teaching language

English

Objectives

The aim of this course is to become familiar with the fundamental characteristics of the digital image and its forms of representation, the description of visual content through local characteristics of colour, shape and texture, and the practical application of these concepts to problems of image processing and analysis.

Learning outcomes and competences

Study programme competencies: Specific
A1 CE1 – To know and apply the concepts, methodologies and technologies of image processing

Study programme competencies: Basic / General
B1 CB6 – To possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context
B2 CB7 – Students can apply their acquired knowledge and problem-solving skills in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study
B6 CG1 – Ability to analyze and synthesize knowledge
B8 CG3 – Ability to develop computer vision systems depending on existing needs and apply the most appropriate technological tools

Study programme competencies: Transversal / Nuclear
C1 CT1 – Practice the profession with a clear awareness of its human, economic, legal and ethical dimensions and with a clear commitment to quality and continuous improvement
C2 CT2 – Ability to work as a team, organize and plan

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Unidades curriculares que são recomendadas para serem frequentadas em simultâneo:

• Fundamentos de aprendizagem automática para visão computacional


• Fundamentos de análise e processamento de imagem

Program

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

Mandatory literature

Bovik, Alan; The essential guide to image processing. ISBN: 978-0-12-374457-9
Al Bovik; The essential guide to video processing. ISBN: 978-0-12-374456-2

Complementary Bibliography

Bovik, Alan (Ed.); Handbook of image and video processing. ISBN: 978-0-12-119792-6
Mallat, Stephane; A wavelet tour of signal processing: The sparse way. ISBN: 978-0-12-374370-1
Nixon, Mark; Feature extraction and image processing for computer vision. ISBN: 9780123965493
Sonka, M; Hlavac, V.; Boyle, R.; Image Processing, Analysis, and Machine Vision. ISBN: 978-0-49-508252-1
Forsyth, David A; Ponce, Jean; Computer Vision: A Modern Approach. ISBN: 978-0-13608-592-8
Szeliski, Richard; Computer Vision: Algorithms and Applications. ISBN: 978-1-84882-934-3
Petrou, Maria; García-Sevilla, Pedro; Image processing: Dealing with texture. ISBN: 978-0-470-02628-1
Mirmehdi, M.; Xie, X.; Suri, J. (Eds.); Handbook of texture analysis. ISBN: 978-1-84816-115-3

Teaching methods and learning activities

Guest lecture/keynote:
Participatory lectures to learn the theoretical content of the subject

Case study:
Elaboration and presentation of selected state-of-the-art methodologies related to the subject.

Objective test:
Continuous self-evaluation tests during the course. Evaluation by examination at the end of the course as an alternative.

Laboratory practice:
Analysis and resolution of practical cases to strengthen the practical application of the theoretical content. Practice in computer classrooms, learning based on the resolution of practical cases, autonomous work and independent study of the students, and group
work and cooperative learning.

Research (Research project):
Learning is based on the resolution of practical cases, autonomous work and independent study of the students, and group work and cooperative learning.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Teste	25,00
Apresentação/discussão de um trabalho científico	15,00
Trabalho laboratorial	40,00
Trabalho prático ou de projeto	20,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	40,00
Frequência das aulas	42,00
Apresentação/discussão de um trabalho científico	20,00
Elaboração de projeto	20,00
Trabalho laboratorial	40,00
Total:	162,00

Eligibility for exams

Carrying out practical work/projects proposed throughout the semester.

Calculation formula of final grade

Case study (15).
Competences: A1 B1 B2 B6 B8 C1 C2
Elaboration and presentation of works on selected state-of-the-art

Objective test (25):
Competences: A1 B1 B2 B6 B8 C2 C1
Continuous self-evaluation tests during the course. Evaluation by examination at the end of the course as an alternative

Laboratory practice (40)
Competences: A1 B1 B2 B6 B8 C1 C2
Analysis and resolution of practical cases to strengthen the practical application of theoretical content

Research project (20):
Competences:A1 B1 B2 B6 B8 C1 C2
Resolution of practical cases of application of the subject through autonomous work of the student and using the techniques learned during the course