| 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 |
| C2 |
CT02 - Command in understanding and expression, both in oral and written forms, of a foreign language |
| 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 |
| 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 |
| Know and understand the fundamental characteristics of the digital image and its forms of representation.
Know, understand and know how to apply digital image processing techniques.
Know, understand and know how to apply digital image analysis techniques.
Ability to apply different techniques to computer vision problems. |
AC23
AC24
AC25
AC26
|
BC1
BC3
BC5
BC6
BC7
BC10
|
CC2
CC3
CC8
|
| Contents |
| Topic |
Sub-topic |
| Introduction to computer vision. |
|
| Programming environments and libraries for computer vision. |
|
| Color spaces and preprocessing. |
|
| Local operators. |
|
| Fundamentals of image segmentation. |
|
| Fundamentals of multiscale analysis. |
|
| Planning |
| Methodologies / tests |
Competencies |
Ordinary class hours |
Student’s personal work hours |
Total hours |
| Guest lecture / keynote speech |
A24 A25 A26 A27 B1 B3 B5 B6 B7 B10 C2 C3 C8 |
10 |
22 |
32 |
| Laboratory practice |
A24 A25 A26 A27 B1 B3 B5 B6 B7 B10 C2 C3 C8 |
7 |
21 |
28 |
| Case study |
A24 A25 A26 A27 B1 B3 B5 B6 B7 B10 C2 C3 C8 |
4 |
10 |
14 |
| |
| Personalized attention |
|
1 |
0 |
1 |
| |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
| Methodologies |
|
Methodologies |
Description |
| Guest lecture / keynote speech |
The teacher presents a topic to the students with the aim of providing a set of information with a specific scope. This teaching methodology will be applied to the training activity "Theory classes". |
| Laboratory practice |
The teaching staff of the subject poses to the students a problem or problems of a practical nature whose resolution requires the understanding and application of the theoretical-practical contents included in the contents of the subject. Students can work on the solution to the problems raised individually or in groups. This teaching methodology will be applied to the training activity "Practical laboratory classes" and may be applied to the training activity of "Problem-based learning sessions, seminars, case studies and projects". |
| Case study |
Students are presented with a work scenario, real or fictitious, that presents a certain problem. Students must apply the theoretical-practical knowledge of the subject to find a solution to the question or questions raised. As a general rule, the case study will be carried out in groups. The different working groups will present and share their solutions. |
| Personalized attention |
|
Methodologies
|
| Guest lecture / keynote speech |
|
| Description |
| The teaching staff will assist the students in individualized tutorial sessions dedicated to orientation in the study and the resolution of doubts about the contents and work of the subject. |
|
| Assessment |
|
Methodologies
|
Competencies |
Description
|
Qualification
|
| Guest lecture / keynote speech |
A24 A25 A26 A27 B1 B3 B5 B6 B7 B10 C2 C3 C8 |
The part related to the presentation of the master sessions will be evaluated through written tests and/or through the continuous evaluation of laboratory practices, which will evaluate the adequacy of the proposed solutions to the problems, the quality of the results obtained and the understanding of the techniques used. |
40 |
| Case study |
A24 A25 A26 A27 B1 B3 B5 B6 B7 B10 C2 C3 C8 |
Resolution of case studies. 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. |
60 |
| |
|
Assessment comments |
All assignment and test notes will be retained until the second chance. There the students will be able to repeat some of the assessment activities. The final grade will be the one calculated taking into account the maximum marks between the corresponding activities in both opportunities. A student will be classified as Absent if he / she does not present any assessment exercise or take any test at any of the opportunities.
The total or partial copy of any exercise of practice or theory will suppose a fail in both occasions of the course, with a qualification of 0,0 in both cases.
|
| Sources of information |
|
Basic
|
Richard Szeliski (2010). Computer Vision: Algorithms and Applications. Springer Science.
|
|
|
Complementary
|
D.A. Forsyth y J. Ponce (2002). Computer Vision--A Modern Approach. Prentice Hall.
Gonzalez & Woods (2009). Digital image processing. Pearson.
Steger, Carsten and Ulrich, Markus and Wiedemann, Christian (2018). Machine vision algorithms and applications. John Wiley.
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| Recommendations |
| Subjects that it is recommended to have taken before |
|
| Subjects that are recommended to be taken simultaneously |
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| Subjects that continue the syllabus |
|
| Other comments |
|
- It is recommended to keep up with the study of theory, the performance of practical exercises, and problem solving. We also consider it important to make good use of tutorials for the discussion of practical exercises and as a means of immediate resolution of doubts. - As reflected in the various regulations applicable to university teaching, the gender perspective should be incorporated into this subject (non-sexist language will be used, bibliography from authors of both sexes will be used, the intervention of male and female students in class will be encouraged...). - Work will be done to identify and modify sexist prejudices and attitudes, and we will influence the environment to change them and promote values of respect and equality. - Situations of discrimination on the grounds of gender should be detected and actions and measures should be proposed to correct them. |
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