Type A
|
Code |
Competences Specific | | A1 |
Integrate the fundamental technology, applications, services and systems of Computer Security and Artificial Intelligence,in a broader, multidisciplinary context.
|
| A8 |
Design and develop computer systems, applications and services for the protection of privacy and information security in ubiquitous systems.
|
| A9 |
Apply computational, mathematical, statistical and artificial intelligence methods in order to model, design and develop applications, services, smart systems and knowledge-based systems.
|
| A10 |
Use and develop methodologies, methods, techniques, specific-use programmes, regulations and standards for graphic computing.
|
| A11 |
Conceptualise, design, develop and evaluate the person-computer interaction of computer products, systems, applications and services using advanced artificial intelligence techniques interaction.
|
| A12 |
Create and operate virtual environments, and create, manage and distribute multimedia content guaranteeing the protection of privacy and copyright by techniques of computer security and Artificial Intelligence.
|
| G1 |
Project, calculate and design products, processes and installations in the areas of Computer Security and Artificial Intelligence
|
| G2 |
Perform mathematical modelling, calculation and simulation in company technology and engineering centres, particularly in tasks of research, development and innovation in the areas of Computer Security and Artificial Intelligence
|
Type B
|
Code |
Competences Transversal | | CT2 |
Forming opinions on the basis of the efficient management and use of information |
| CT3 |
Solve complex problems critically, creatively and innovatively in multidisciplinary contexts. |
| CT4 |
Work in multidisciplinary teams and in complex contexts. |
| CT5 |
Communicate complex ideas effectively to all sorts of audiences |
| CT7 |
Apply ethical principles and social responsibility as a citizen and a professional. |
Type C
|
Code |
Competences Nuclear |
Type A
|
Code |
Learning outcomes |
| A1 |
Analyse the problems and their causes from a global focus in the medium and long term.
| | A8 |
Develop advanced artificial vision techniques in cameras and other embedded and ubiquitous systems.
| | A9 |
Know how to implement advanced computer vision techniques.
| | A10 |
Use graphic computation techniques.
| | A11 |
Use artificial systems that interact with humans by means of artificial vision.
| | A12 |
Analyse multimedia content using pattern recognition and artificial vision techniques.
| | G1 |
Integrate theoretical knowledge into the realities to which it may apply.
| | G2 |
Apply the techniques learned in a specific context.
|
Type B
|
Code |
Learning outcomes |
| CT2 |
Manage information and knowledge by making efficient use of the information technologies.
| | CT3 |
Recognise the situation as a problem in a multidisciplinary, research or professional environment, and take an active part in finding a solution.
| | CT4 |
Participate in the group in a good working environment and help to solve problems.
| | CT5 |
Produce a persuasive, consistent and precise discourse that can explain complex ideas and effectively interact with the audience.
| | CT7 |
Apply ethical and socially responsible principles as a citizen and a professional.
|
Type C
|
Code |
Learning outcomes |
Topic |
Sub-topic |
Chapter 1.- Image Processing. |
Filtering, image compensation and image enhancement, morphological operations. |
Chapter 2.- Geometrical Feature Extraction. |
Identification of corners, lines and basic geometrical shapes. |
Chapter 3.- Color and Texture Analysis. |
Color models, texture types, extraction of textural features, geometric methods. |
Chapter 4.- Image Segmentation and Classification. |
Unsupervised segmentation based on contours and regions, supervised classification, methods of decision theory, probabilistic methods, neural networks. |
Chapter 5.- Stereoscopic Vision. |
Calibration of cameras and camera systems, epipolar geometry, image rectification, matching, triangulation. |
Chapter 6.- Perception and 3D-modeling. |
Generation of depth maps, extraction of basic geometric elements,
automatic generation of scenes, scene recognition, geometric hashing. |
Methodologies :: Tests |
|
Competences |
(*) Class hours
|
Hours outside the classroom
|
(**) Total hours |
Introductory activities |
|
1 |
1.5 |
2.5 |
Presentations / oral communications |
|
1 |
1.5 |
2.5 |
Assignments |
|
13 |
19.5 |
32.5 |
Reading written documents and graphs |
|
26 |
38.5 |
64.5 |
Forums of debate |
|
1 |
1.5 |
2.5 |
Personal attention |
|
1 |
0 |
1 |
|
Multiple-choice objective tests |
|
2 |
5 |
7 |
|
(*) On e-learning, hours of virtual attendance of the teacher. (**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies
|
Description |
Introductory activities |
Introduction to the course: motivation, objectives, contents, teaching methods, bibliography and evaluation. |
Presentations / oral communications |
Students perform oral presentation of their work going in depth into specific topics of the subject. Assessment by the teacher. |
Assignments |
Students perform in groups of 2 people some analyses and research tasks related to the main themes of the course. Practical use of simulators. Preparation of a report. Final evaluation by the teacher. |
Reading written documents and graphs |
The students have to prepare the units of the course. |
Forums of debate |
Share questions with the teacher and their mates in the Forum. The teacher or any student can reply the questions.
The collaboration between them is part of the learning process
|
Personal attention |
Personal attention to each student by the teacher. |
Description |
Enquiries /Tutorials: Resolution of theoretical and practical questions. Correction of practices. Exams review. |
Methodologies |
Competences
|
Description |
Weight |
|
|
|
|
Presentations / oral communications |
|
Oral presentation. Final evaluation by the teacher.
|
30 |
Assignments |
|
Elaboration by the students of practical work related to the main topics of the course using the tools of computer vision explained in the practical classes. Elaboration of a report. |
50 |
Multiple-choice objective tests |
|
Realització de proves tipus test. |
20 |
Others |
|
|
|
|
Other comments and second exam session |
The student has to go to second call with all the assessments tests not past on the first call. |
Basic |
E. Trucco, A. Verri, Introductory Techniques for 3-D Computer Vision, Prentice Hall, 1998
L. Shapiro, G. Stockman, Computer Vision, Prentice Hall, 2001
D.A. Forsyth, Computer Vision: A Modern Approach, Pearson Education, 2012
N.J. Hackensack, Handbook of pattern recognition and computer vision, Imperial College Press, 2010
R. Szeliski, Computer vision: algorithms and applications, Springer, 2011
|
|
Complementary |
O. Faugeras, Three-Dimensional Computer Vision, MIT Press, 1993
E.R. Davies, Machine Vision: Theory, Algorithms, Practicalities, Academic Press, 1997
|
|
(*)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. |
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