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 |
Master the tools for managing their own identity and activities in a digital environment.
Search for and find information autonomously using criteria of importance, reliability and relevance, which is useful for creating knowledge
Organise information with appropriate tools (online and face-to-face) so that it can be updated, retrieved and processed for re-use in future projects.
Produce information with tools and formats appropriate to the communicative situation and with complete honesty.
Use IT to share and exchange the results of academic and scientific projects in interdisciplinary contexts that seek knowledge transfer.
| | CT3 |
Recognise the situation as a problem in a multidisciplinary, research or professional environment, and take an active part in finding a solution.
Follow a systematic method with an overall approach to divide a complex problem into parts and identify the causes by applying scientific and professional knowledge.
Design a new solution by using all the resources necessary and available to cope with the problem.
Draw up a realistic model that specifies all the aspects of the solution proposed.
Assess the model proposed by contrasting it with the real context of application, find shortcomings and suggest improvements.
| | CT4 |
Understand the team’s objective and identify their role in complex contexts.
Communicate and work with other teams to achieve joint objectives.
Commit and encourage the necessary changes and improvements so that the team can achieve its objectives.
Trust in their own abilities, respect differences and use them to the team’s advantage.
| | CT5 |
Produce quality texts that have no grammatical or spelling errors, are properly structured and make appropriate and consistent use of formal and bibliographic conventions
Draw up texts that are structured, clear, cohesive, rich and of the appropriate length, and which can transmit complex ideas.
Draw up texts that are appropriate to the communicative situation, consistent and persuasive.
Use the techniques of non-verbal communication and the expressive resources of the voice to make a good oral presentation.
Construct a discourse that is structured, clear, cohesive, rich and of the appropriate length, and which can transmit complex ideas.
Produce a persuasive, consistent and precise discourse that can explain complex ideas and effectively interact with the audience.
| | CT7 |
Be aware of gender and other inequalities in their activity as a URV student.
Analyse the major environmental problems from the perspective of their field of expertise in their student and/or professional activity.
Be able to give arguments based on social values and make proposals for the improvement of the community.
Be personally and professionally committed to applying the ethical and deontological concepts of their field of expertise.
|
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 |
IT-based practicals in computer rooms |
|
10 |
15 |
25 |
Presentations / oral communications |
|
1 |
1.5 |
2.5 |
Lecture |
|
25 |
33.5 |
58.5 |
Problem solving, exercises in the classroom |
|
4 |
6 |
10 |
Personal attention |
|
1 |
0 |
1 |
|
Extended-answer tests |
|
2 |
5 |
7 |
Short-answer objective tests |
|
1 |
5 |
6 |
|
(*) 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. |
IT-based practicals in computer rooms |
Practical use of simulators related to course content and developing new functionalities. |
Presentations / oral communications |
Students perform oral presentation of their work going in depth into specific topics of the subject. Assessment by the teacher. |
Lecture |
Explanation of theoretical contents by the teacher. |
Problem solving, exercises in the classroom |
Students perform in groups of 2 people some analyses and research tasks related to the main themes of the course. Preparation of a report. Final evaluation by the teacher. |
Personal attention |
Personal attention to each student by the teacher during the teacher's office hours. |
Description |
Enquiries /Tutorials: Resolution of theoretical and practical questions. Correction of practices. Exams review. |
Methodologies |
Competences
|
Description |
Weight |
|
|
|
|
IT-based practicals in computer rooms |
|
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. |
40 |
Presentations / oral communications |
|
Students perform in groups of 2 people some analyses and research tasks related to the main themes of the course. Preparation of a report. Oral presentation. Final evaluation by the teacher. |
20 |
Extended-answer tests |
|
Extended-answer tests |
20 |
Short-answer objective tests |
|
Objective short-answer tests |
20 |
Others |
|
|
|
|
Other comments and second exam session |
Students who fail the continuous assessment can recover parts suspended or not presented in the second call. In all written examinations can not bring any electronic device. |
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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