Fundamentals of image analysis and processing

Code:

MVCOMP01

Acronym:

FPAI

Keywords
Classification	Keyword
CNAEF	Informatics Sciences

Instance: 2022/2023 - 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	3	Syllabus	1	-	6	42	162

Teaching language

English

Objectives

Understand the basic concepts and techniques of digital image processing.

Understand the basic concepts and techniques of digital image analysis.

Ability to apply different basic techniques for computer vision problems.

Know how to assess the adequacy of the methodologies applied in specific problems.

Learning outcomes and competences

.

Working method

Presencial

Program

Introduction to CV libraries and programming frameworks. Fundamental concepts of CV. HVS and perception. Color spaces. Preprocessing: normalization and enhancement. Image transformations. Local operators (denoising, edge detection, morphology, etc.). Pattern/Template matching.

Basic feature points and interest regions (Blobs, Corners). Multiscale-analysis. Segmentation algorithms (fundamental and classic methods). Evaluation of segmentation. Hough transform: lines and circles. 

Mandatory literature

David A. Forsyth; Computer vision. ISBN: 0-13-085198-1

Teaching methods and learning activities

This curricular unit addresses the most fundamental topics in image processing and analysis and presents itself as the first in a sequence with another curricular unit where the advanced topics are presented. In addition to the study and application of fundamental techniques of image processing and analysis, applications in this area are studied that aim to solve real problems. This approach gives students the necessary tools to apply the algorithms studied in practical cases, as well as the basis for developing new algorithms and pursue the study for more advanced methods.

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 evaluation consists of the students’ results in the projects (accounting for 60% of the final grade) and in a written exam (accounting for 40% of the final grade).