|
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 |
Formular valoracions a partir de la gestió i ús eficient de la informació. |
| | CT3 |
Resoldre problemes complexes de manera crítica, creativa i innovadora en contextos multidisciplinars. |
| | CT4 |
Treballar en equips multidisciplinars i en contextos complexes. |
| | CT5 |
Comunicar idees complexes de manera efectiva a tot tipus d’audiències. |
| | CT7 |
Aplicar els principis ètics i de responsabilitat social com a ciutadà i com a professional. |
|
Type C
|
Code |
Competences Nuclear |
|
Type A
|
Code |
Learning outcomes |
| | A1 |
Analitza els problemes i les seves causes des d'un enfocament global i de mitjà i llarg termini.
| | | A8 |
Sap desenvolupar tècniques avançades de visió artificial en càmeres i altres sistemes encastats i ubics
| | | A9 |
Sap implementar tècniques avançades de visió artificial.
| | | A10 |
Utilitza tècniques de computació gràfica
| | | A11 |
Integra sistemes artificials que interactuen amb humans mitjançant visió artificial
| | | A12 |
Analitza continguts multimèdia mitjançant tècniques de reconeixement de patrons i visió artificial
| | | G1 |
Integra els coneixements teòrics amb les realitats a les quals es poden aplicar.
| | | G2 |
Aplica les tècniques apreses en contextos concrets.
|
|
Type B
|
Code |
Learning outcomes |
| | CT2 |
Formular valoracions a partir de la gestió i ús eficient de la informació.
| | | CT3 |
Resoldre problemes complexes de manera crítica, creativa i innovadora en contextos multidisciplinars.
| | | CT4 |
Treballar en equips multidisciplinars i en contextos complexes.
| | | CT5 |
Comunicar idees complexes de manera efectiva a tot tipus d’audiències.
| | | CT7 |
Aplicar els principis ètics i de responsabilitat social com a ciutadà i com 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 |
| Practicals using information and communication technologies (ICTs) in computer rooms |
|
10 |
15 |
25 |
| Presentations / expositions |
|
1 |
1.5 |
2.5 |
| Lecture |
|
25 |
33.5 |
58.5 |
| Problem solving, classroom exercises |
|
4 |
6 |
10 |
| Personal tuition |
|
1 |
0 |
1 |
| |
| Extended-answer tests |
|
2 |
5 |
7 |
| Objective short-answer 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. |
| Practicals using information and communication technologies (ICTs) in computer rooms |
Practical use of simulators related to course content and developing new functionalities. |
| Presentations / expositions |
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, classroom exercises |
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 tuition |
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 |
|
|
|
|
| Practicals using information and communication technologies (ICTs) 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 / expositions |
|
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 |
| Objective short-answer 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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