Identifying Data 2022/23
Subject (*) Computer Vision II Code 614544018
Study programme
Máster Universitario en Intelixencia Artificial
Descriptors Cycle Period Year Type Credits
Official Master's Degree 2nd four-month period
 First Optional 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
Ramos García, Lucia
 E-mail
noelia.barreira@udc.es
l.ramos@udc.es
Web
General description O obxectivo principal desta materia é profundizar nas técnicas de visión por computador, en concreto, en técnicas avanzadas de segmentación, clasificación, detección e seguemento de obxectos, así como nas aplicacións da IA no campo da visión. Ademais do estudio de técnicas avanzadas de procesado e análise de imaxe, estudiaranse aplicacións nesta área para resolver problemas reais. Esta materia proporciona as ferramentas necesarias para aplicar os algoritmos estudiados en casos prácticos así como para desenvolver novos algoritmos.

Study programme competencies
Code Study programme competences
A24 CE23 - Understanding and command of basic concepts and techniques of digital image processing
A25 CE24 - Ability to apply different techniques to computer vision problems
A26 CE25 - Knowledge and ability to design systems for detecting, classifying and tracking objects in images and video
A27 CE26 - Understanding and command of the multiple ways to represent images and signals in terms of their associated data and their main features
B1 CG01 - Maintaining and extending theoretical foundations to allow the introduction and exploitation of new and advanced technologies in the field of AI
B3 CG03 - Searching and selecting that useful information required to solve complex problems, with a confident handling of bibliographical sources in the field
B5 CG05 - Working in teams, especially of multidisciplinary nature, and being skilled in the management of time, people and decision making
B6 CB01 - Acquiring and understanding knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, frequently in a research context
B7 CB02 - The students will be able to apply the acquired knowledge and to use their capacity of solving problems in new or poorly explored environments inside wider (or multidisciplinary) contexts related to their field of study
B10 CB05 - The students will acquire learning abilities to allow them to continue studying in way that will mostly be self-directed or autonomous
C3 CT03 - Use of the basic tools of Information and Communications Technology (ICT) required for the student's professional practice and learning along her life
C4 CT04 - Acquiring a personal development for practicing a citizenship under observation of the democratic culture, the human rights and the gender perspective
C8 CT08 - Appreciating the importance of research, innovation and technological development in the socioeconomic and cultural progress of society

Learning aims
Learning outcomes Study programme competences
To know and to know how to use advanced image analysis techniques AC24
AC25
 BC1
BC3
BC6
BC10
 CC4
CC8
To know and to know how to use advanced image processing techniques AC23
AC24
 BC1
BC3
BC6
BC10
 CC4
CC8
To know how to analyse, design and develop solutions based on advances image processing and analysis techniques AC24
AC26
 BC5
BC7
 CC3
To know how to evaluate the suitability of the methodologies applied in specific problems AC24
AC25
 BC6
BC7
 CC3

Contents
Topic Sub-topic
Advanced image processing techniques.
Advanced image processing techniques.
Advanced segmentation techniques.
Advanced image processing and analysis applications.

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Laboratory practice A25 B1 B3 B7 C3 14 42 56
Research (Research project) A25 A26 B5 B7 C3 7 35 42
Mixed objective/subjective test A24 A27 B1 B7 2 0 2
Guest lecture / keynote speech A24 A27 B1 B6 B10 C4 C8 21 21 42
 
Personalized attention 8 0 8
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
Laboratory practice Analysis and resolution of practical cases using techniques learned in lectures.
Research (Research project) Proposal of 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.
Mixed objective/subjective test Test with questions about the theoretical contents of the subject as well as practical problems.
Guest lecture / keynote speech Oral presentation using audiovisual material and student interaction designed to transmit knowledge and encourage learning.

Personalized attention
Methodologies
Laboratory practice
Research (Research project)
Description
Teachers will answer the doubts during the laboratory practice.

Teachers will provide personal advising for the supervised projects.

Assessment
Methodologies Competencies Description Qualification
Laboratory practice A25 B1 B3 B7 C3 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. 40
Research (Research project) A25 A26 B5 B7 C3 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
Mixed objective/subjective test A24 A27 B1 B7 Written test with theoretical questions and practical problems to be solved. 0
 
Assessment comments
The laboratory practice during the year is 40% of the final grade. However, students can achieve this percentage of the final grade with mixed test. This way, if the laboratory exercises are submitted, the exam is optional. 

If a student submits the laboratory practice and takes the mixed test, the mark obtained in the mixed test will prevail over the mark got in the laboratory practice. 

If the student does not deliver any of the assignments or takes the exam, he/she will be considered as "absent". 

In the second chance: 

  • In case of an "absent" student in the first chance, the assessment will comprise the research projects (up to 60%) and the mixed text (up to 40%). 
  • If the student has summited the laboratory practice and/or any research project but he/she has not passed the subject, the first chance marks in both parts will be kept. To pass the subject, the student should submit the non-delivered/failed research projects and/or take the mixed test. 

Sources of information
Basic R. Szeliski (2010). Computer vision: algorithms and applications. Springer
M. Elgendy (2020). Deep Learning for Vision Systems. Manning
M. Sonka, V. Hlavac, R. Boyle (2015). Image Processing, Analysis and Machine Vision. Cengage Learning

Complementary I. Goodfellow, Y, Bengio, A. Courville (2016). Deep Learning. MIT Press


Recommendations
Subjects that it is recommended to have taken before
Computer Vision I/614544017

Subjects that are recommended to be taken simultaneously
Deep Learning /614544013

Subjects that continue the syllabus

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.