Identifying Data 2020/21
Subject (*) Fundamentals of Image Processing and Analysis Code 614535001
Study programme
Máster Universitario en Visión por Computador
Descriptors Cycle Period Year Type Credits
Official Master's Degree 1st four-month period
First Obligatory 6
Language
English
Teaching method Hybrid
Prerequisites
Department Ciencias da Computación e Tecnoloxías da Información
Coordinador
Barreira Rodriguez, Noelia
E-mail
noelia.barreira@udc.es
Lecturers
Barreira Rodriguez, Noelia
Rouco Maseda, Jose
E-mail
noelia.barreira@udc.es
jose.rouco@udc.es
Web
General description Esta materia trata os temas fundamentais de procesado e análise de imaxe e preséntase como a primeira parte de outra materia que introduce temas máis avanzados. Ademais do estudo e a aplicación de técnicas fundamentais, estudaranse aplicacións prácticas destas técnicas para resolver problemas reais. Esta materia aporta as ferramentas necesarias para aplicar os algoritmos utilizados en casos prácticos, ademais das bases para desenvolver novos algoritmos e continuar co estudo de métodos máis avanzados.
Contingency plan 1. Modificacións nos contidos

Non se realizarán cambios

2. Metodoloxías
*Metodoloxías docentes que se manteñen


- Prácticas de laboratorio
- Sesións maxistrais
- Proxecto de investigación
- Proba obxetiva


*Metodoloxías docentes que se modifican

3. Mecanismos de atención personalizada ao alumnado

- Correo electrónico: diariamente para facer consultas, solicitar
encontros virtuais para resolver dúbidas e realizar seguemiento das prácticas de laboratorio e dos proxectos de investigación.

- Moodle: Diariamente, segundo las necesidades do estudantado.

- Teams: 2 sesións semanais en grupo para analizar o avance dos
contidos teóricos, as prácticas de laboratorio e os proxectos de
investigación no horario asignado ás horas de prácticas de
laboratorio no horario oficial.

4. Modificacións na avaliación

Non se realizarán cambios

*Observacións de avaliación:

5. Modificacións da bibliografía ou webgrafía

- Non se realizarán cambios

Study programme competencies
Code Study programme competences
A1 CE1 - To know and apply the concepts, methodologies and technologies of image processing
A3 CE3 - To know and apply the concepts, methodologies and technologies of image and video analysis
B7 CG2 - Ability to analyze a company's needs in the field of computer vision and determine the best technological solution for it
B9 CG4 - Ability to critically analyze and rigorously evaluate technologies and methodology
B10 CG5 - Ability to identify unsolved problems and provide innovative solutions
B12 CG7 - Ability to learn autonomously for specialization in one or more fields of study
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

Learning aims
Learning outcomes Study programme competences
Understand the basic concepts and techniques of digital image processing. AC1
BC12
Understand the basic concepts and techniques of digital image analysis. AC3
BC12
Ability to apply different basic techniques for computer vision problems. BC7
BC10
CC1
Know how to assess the adequacy of the methodologies applied in specific problems. BC9

Contents
Topic Sub-topic
Digital image fundamentals
Human perception and color
Preprocessing: normalization and enhancement
Image denoising
Edge detection
Image transformations
Morphological operators
Template matching
Extraction of global features
Extraction of scale-invariant features
Hough transform
Image thresholding
Region growing and split-and-merge
Other segmentation techniques

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Objective test A1 A3 B10 3 0 3
Laboratory practice A1 A3 B10 15 44 59
Research (Research project) A1 A3 B7 B9 B10 B12 C1 10 40 50
Guest lecture / keynote speech A1 A3 C1 14 24 38
 
Personalized attention 0 0
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
Objective test Test with questions about the theoretical contents of the subject as well as practical problems.
Laboratory practice Analysis and resolution of practical cases using techniques learned in lectures.
Research (Research project) Proposal of two assignments in image analysis that require to identify the problem, to formulate it precisely, to develop suitable procedures, to interpret the results and to extract appropriate conclusions about the work.
Guest lecture / keynote speech Oral presentation using audiovisual material and student interaction designed to transmit knowledge and encourage learning.

Personalized attention
Methodologies
Research (Research project)
Laboratory practice
Description
Teachers will answer the doubts during the laboratory practice and they will provide personal advising for the supervised projects.

Assessment
Methodologies Competencies Description Qualification
Research (Research project) A1 A3 B7 B9 B10 B12 C1 Two assignments that consist of the development of image processing and computer vision applications. It will be assessed the suitability of the proposed solutions and the quality of the obtained results. 60
Objective test A1 A3 B10 Written test with theoretical questions and practical problems to be solved. 40
Laboratory practice A1 A3 B10 Practical exercises about the topics learned in the lectures. It will be assessed the suitability of the proposed solutions and the quality of the obtained results. 0
 
Assessment comments

The objective test is 40% of the final grade. However, students can achieve this percentage of the final grade with the laboratory exercises during the year. This way, if the laboratory exercises are presented, the exam is optional.

If a student presents the laboratory exercises and attends the objective test, the grade obtained in the objective test will prevail over the grade achieved in the laboratory exercises.


Sources of information
Basic David A. Forsyth, Jean Ponce (2003). Computer vision. Prentice - Hall
Rafael González, Richard Woods (2008). Digital Image Processing. Pearson
Carsten Steger, Markus Ulrich, Christian Wiedemann (2018). Machine Vision Algorithms and Applications. Wiley

Complementary


Recommendations
Subjects that it is recommended to have taken before

Subjects that are recommended to be taken simultaneously
Image Description and Modeling/614535004

Subjects that continue the syllabus
Advanced Image Processing and Analysis/614535002

Other comments


(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.