Fundamentals of image analysis and processing

Code:

MVCOMP01

Acronym:

FPAI

Keywords
Classification	Keyword
CNAEF	Informatics Sciences

Instance: 2024/2025 - 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	4	Syllabus	1	-	6	42	162

Teaching language

English

Objectives

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 studying and applying 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 pursuing the study of more advanced methods.

Learning outcomes and competences

Learning outcomes:

• 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 to specific problems.

Competencies:

CT1. Practice the profession with a clear awareness of its human, economic, legal and ethical dimensions and a commitment to quality and continuous improvement.
CG2. Ability to analyze the needs of a company in the field of computer vision and determine the best technological solution for it.
CG4. Capacity for critical analysis and rigorous evaluation of technologies and methodology.
CG5. Ability to identify unsolved problems and provide innovative solutions.
CG7. Autonomous learning ability for specialization in one or more fields of study.
CE1. Know and apply the concepts, methodologies and technologies of image processing.
CE3. Know and apply image and video analysis concepts, methodologies and technologies.

Working method

Presencial

Program

Part 1 (UDC)
* Perception and colour
* Preprocessed: normalization and enhancement
* Image restoration
* Edge detection
* Image transformations
* Morphological operators
* Template matching

Part 2 (USC)
* Extraction of global image properties (key points, blobs, corners, MSERs)
* Extraction of Invariant Properties at Scale (SIFT)
* Segmentation through thresholding
* Segmentation by fitting to a model (Hough transform)
* Segmentation through division and growth of regions
* Other segmentation techniques

Mandatory literature

Rafael C. Gonzalez; Digital image processing. ISBN: 0-20-118075-8

Complementary Bibliography

David A. Forsyth; Computer vision. ISBN: 0-13-085198-1
Carsten Steger; Machine vision algorithms and applications. ISBN: 978-3-527-40734-7

Teaching methods and learning activities

The methodology followed uses the Virtual Campus of the USC-UDC as a basic platform. In the virtual classroom of the subject, the students will have all the information (theory material, class slides, practice scripts, etc.)

* Master sessions: oral exposition complemented with audiovisual media and the introduction of questions for the students to transmit knowledge and facilitate learning.
* Laboratory practices: Practical resolution of different image problems by applying image processing techniques explained during the master sessions.
* Research: Propose two practical situations in image analysis that require students to identify the problem under study, formulate it accurately, develop the relevant procedures, interpret the results, and draw the appropriate conclusions from the work done.

The CT1, CG2, CG4, CG5 and CG7 competencies are developed mainly in developing research projects, and the CE1 and CE2 competencies are developed in master classes, carrying out exercises and research projects.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Trabalho prático ou de projeto	40,00
Trabalho laboratorial	60,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	24,00
Frequência das aulas	42,00
Trabalho escrito	2,00
Trabalho de investigação	40,00
Trabalho laboratorial	44,00
Elaboração de projeto	10,00
Total:	162,00

Eligibility for exams

Carry out laboratory practices and projects based on practical cases.

Calculation formula of final grade

The evaluation of the curricular unit consists of two parts that must be passed independently with a minimum grade of 10 out of 20:

60%: The part related to the presentation of the master sessions can be overcome through the continuous evaluation of laboratory practices, which will assess the adequacy of the proposed solutions to the problems, the quality of the results obtained and the understanding of the techniques used. Alternatively, this part will be evaluated through a final written test with theoretical questions and practical problems. It is mainly used to assess CE1 and CE3 competencies.

40%: Resolution of two practical cases (research project). The adequacy of the proposed solutions to the problems, the quality of the results obtained and the understanding of the techniques used will be assessed. It mainly evaluates the CT1, CG2, CG4, CG5 and CG7 competencies.