Type A
|
Code |
Competences Specific | | CP3 |
Be able to evaluate the computational complexity of a problem, know algorithmic strategies that may lead to its resolution and recommend, develop and implement the one that will guarantee the best performance in accordance with the established requirements.
|
| CP5 |
Be able to acquire, obtain, formalise and represent human knowledge in a computable format to solve problems through an IT system in any field of application, particularly those related to aspects of computation, perception and action in intelligent environments and settings. |
| CP7 |
Have knowledge of and develop computational learning techniques and design and implement applications and systems that use them, including those dedicated to the automatic extraction of information and knowledge from large volumes of data.
|
Type B
|
Code |
Competences Transversal | | CT5 |
Communicate information clearly and precisely to a variety of audiences. |
Type C
|
Code |
Competences Nuclear |
Type A
|
Code |
Learning outcomes |
| CP3 |
Be familiar with and know how to use techniques of preprocessing, segmentation and image classification.
Design applications oriented toward inspection and quality control.
| | CP5 |
Know the stages that integrate a computer vision system.
Know the process of image formation.
| | CP7 |
Know how to apply basic Vision methods in the resolution of specific problems.
Design applications oriented toward inspection and quality control.
|
Type B
|
Code |
Learning outcomes |
| 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
Draw up texts that are appropriate to the communicative situation, consistent and persuasive
|
Type C
|
Code |
Learning outcomes |
Topic |
Sub-topic |
Tema 1.- Introduction |
Concept and objectives of Computer Vision. Image acquisition. Levels of image processing. |
Tema 2.- Image formation. |
Geometric foundations: mathematical representation of an image, basic geometric transformations, perspective transformation, parallel transformation. Illumination techniques. Color. Charge-coupled device: camera model, lens focus and perception, stereoscopic vision. |
Tema 3.- Preprocessing techniques. |
Basic relationships between pixels. Spatial domain. Frequency domain. Filtering techniques: normalization, equalization, smoothing, edge detection. |
Tema 4.- Representation techniques. |
Segmentation: detection of borders, thresholding, region growing, split and merge. Feature Extraction. |
Tema 5.- Description and recognition techniques. |
Description: border descriptors, region descriptors. Recognition. Interpretation. |
Tema 6.- Computer vision applications. |
Industrial applications: inspection, quality control. Medical applications. Office applications. |
Methodologies :: Tests |
|
Competences |
(*) Class hours
|
Hours outside the classroom
|
(**) Total hours |
Introductory activities |
|
2 |
0 |
2 |
IT-based practicals in computer rooms |
|
15 |
30 |
45 |
Assignments |
|
0 |
13 |
13 |
Lecture |
|
9 |
33 |
42 |
Problem solving, exercises in the classroom |
|
15 |
29 |
44 |
Personal attention |
|
2 |
0 |
2 |
|
Multiple-choice objective tests |
|
2 |
0 |
2 |
|
(*) 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 |
Comprehensive description of the course: contents, assessment, etc. |
IT-based practicals in computer rooms |
Practical exercises related to the theoretical concepts in lectures using standard computer vision software. |
Assignments |
Performing work related to theoretical concepts in lectures: the work will be theoretical or practical. |
Lecture |
Explanation by the teacher of Computer Vision concepts, techniques, methodologies, etc., related to the course content. |
Problem solving, exercises in the classroom |
Solving exercises under the supervision of the teacher. |
Personal attention |
Personal attention by teachers of the subject. Includes personalized attention devoted to the evaluation of some projects and exercises. |
Description |
Personal attention by teachers of the subject. Includes personalized attention devoted to the evaluation of some projects and exercises. |
Methodologies |
Competences
|
Description |
Weight |
|
|
|
|
IT-based practicals in computer rooms |
|
Practical exercises related to the theoretical concepts in lectures using standard computer vision software. |
50% |
Assignments |
|
Performing work related to theoretical concepts in lectures: the work will be theoretical or practical. |
25% |
Problem solving, exercises in the classroom |
|
Solving exercises under the supervision of the teacher. |
15% |
Multiple-choice objective tests |
|
Solve tests related to the theoretical contents of the course. |
10% |
Others |
|
|
|
|
Other comments and second exam session |
A la segona convocatòria s'hauran de tornar a realitzar les proves parcials d'avaluació continua que l'alumne no hagi aconseguit una nota minima. La nota mitjana ponderada de totes les proves parcials ha de ser com a mínim de 5. |
Basic |
Escalera, A. de la, Visión por Computador, Prentice Hall, 2001
Ajenjo, A.D, Tratamiento Digital de Imágenes, Anaya Multimedia, 1994
Forsyth, D.A.; Ponce, J., Computer vision a modern approach, Prentice Hall, 2011
Pajares Martinsanz, Gonzalo, Visión por computador: imágenes digitales y aplicaciones , Ra-Ma, 2007
|
|
Complementary |
Vitrià, J., Visió per Computador, Servei Publicacions U.A.B., 1995
Fu, K.S.; Gonzalez, R.C.; Lee, C.S.G, Robótica: Control, Detección, Visión e Inteligencia, McGraw Hill, 1990
|
|
(*)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. |
|