ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Student's work ECTS: 109 Hours of tutorials: 1 Expository Class: 14 Interactive Classroom: 26 Total: 150
Use languages English
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: External department linked to the degrees
Areas: Área externa Máster en Visión por Computador / Computer Vision
Center Higher Technical Engineering School
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
The aim of this course is to become familiar with the fundamental characteristics of the
digital image and its forms of representation, the description of visual content through
local characteristics of colour, shape and texture, and the practical application of these
concepts to problems of image processing and analysis.
Image representation and modeling: space-frequency, orientation and phase, space-scale
Wavelets and filter banks
Image coding and reconstruction
Description of colour, shape and texture
Image modelling and description applications
Basic
1. Bovik, Alan. "The essential guide to image processing". 1st Edition, 2009.
ISBN: 978-0-12-374457-9.
2. Bovik, Alan (Ed.). "Handbook of image and video processing". 2nd Edition,
2005. ISBN: 978-0-12-119792-6.
3. Mallat, Stephane. "A wavelet tour of signal processing: The sparse way". 3rd
Edition, 2009. ISBN: 978-0-12-374370-1.
4. Nixon, Mark. "Feature extraction and image processing for computer vision".
3rd Edition, 2012. ISBN: 9780123965493.
5. Sonka, M; Hlavac, V.; Boyle, R. "Image Processing, Analysis, and Machine
Vision". 3rd Edition, 2009. ISBN: 978-0-49-508252-1.
6. Forsyth, David A; Ponce, Jean. “Computer Vision: A Modern Approach”.
Pearson. 2nd Edition, 2012. ISBN: 978-0-13608-592-8.
7. Szeliski, Richard. “Computer Vision: Algorithms and Applications”. Springer.
1st Edition, 2010. ISBN 978-1-84882-934-3.
8. Petrou, Maria; García-Sevilla, Pedro. "Image processing: Dealing with texture".
2006. ISBN: 978-0-470-02628-1.
9. Mirmehdi, M.; Xie, X.; Suri, J. (Eds.). "Handbook of texture analysis". 2008.
ISBN: 978-1-84816-115-3.
10. Recent papers from relevant scientific journals and conferences: IJCV, IEEE TPAMI, ICCV, CVPR, NIPS, ECCV, etc.
Study programme competences: Specific
A1 CE1 - To know and apply the concepts, methodologies and technologies of image processing
Study programme competences: Basic / General
B1 CB6 - To possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context
B2 CB7 - That students are able to apply their acquired knowledge and problem-solving skills in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of studyB6 CG1 - Ability to analyze and synthesize knowledge
B8 CG3 - Ability to develop computer vision systems depending on existing needs and apply the most appropriate technological tools
Study programme competences: Transversal / Nuclear
C1 CT1 - Practice the profession with a clear awareness of its human, economic, legal and ethical dimensions and with a clear commitment to quality and continuous improvement
C2 CT2 - Ability to work as a team, organize and plan
Guest lecture / keynote:
speech Participatory lessons with the aim of learning the theoretical content of the subject
Case study:
Elaboration and presentation of selected state-of-the-art methodologies related to the subject.
Objective test:
Continuous self-evaluation tests during the course. Evaluation by examination at the end of the course as an alternative.
Laboratory practice:
Analysis and resolution of practical cases with the aim of strengthening the practical application of the theoretical content. Practice in computer classrooms, learning based on the resolution of practical cases, autonomous work and independent study of the students, and group
work and cooperative learning.
Research (Research project):
Learning based on the resolution of practical cases, autonomous work and independent study of the students, and group work and cooperative learning.
-Case study (15).
Competences: A1 B1 B2 B6
B8 C1 C2
Elaboration and presentation of works on selected state-of-the-art
-Objective test (25):
Competences: A1 B1 B2 B6
B8 C2 C1
Continuous self-evaluation tests during the course. Evaluation by examination at the end of the course as an alternative
-Laboratory practice (40)
Competneces: A1 B1 B2 B6
B8 C1 C2
Analysis and resolution of practical cases with the aim of strengthening the practical application of theoretical content
- Research (Research project) (20):
Competences:A1 B1 B2 B6
B8 C1 C2
Resolution of practical cases of application of the subject through autonomous work of the student, and using the techniques learned during the course
Recommended study time for students is about 2 hours per week. Additionally, we estimate that they should spend about 6,5 hours / week working in a number of assignments. All of these activities add up to around 120h/semester.
Subjects that are recommended to be taken simultaneously:
Fundamentals of Machine Learning for Computer Vision /614535007
Fundamentals of Image Analysis and Processing/614535001