ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Student's work ECTS: 110 Hours of tutorials: 1 Expository Class: 14 Interactive Classroom: 25 Total: 150
Use languages English
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: Electronics and Computing, External department linked to the degrees
Areas: Computer Science and Artificial Intelligence, 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)
This subject covers the fundamental topics of image processing and analysis and is presented as the first part of another subject that introduces more advanced topics. In addition to the study and application of fundamental techniques, practical applications of these techniques will be studied to solve real problems. This course provides the necessary tools to apply the algorithms used in practical cases, as well as the bases to develop new algorithms and continue studying more advanced methods.
Learning outcomes:
* Understand the basic concepts and techniques of digital image processing
* Understand the basic concepts and techniques of digital image analysis
* Ability to apply different basic techniques to computer vision problems
* Knowing how to assess the adequacy of methodologies applied to specific problems
Part 1 (UDC)
* Perception and color
* Preprocessed: normalization and enhancement
* Image restoration
* Edge detection
* Image transformations
* Morphological operators
* Template matching
Part 2 (USC)
* Fundamentals of image segmentation.
- Definitions and classification
- Classical methods
- Evaluation of image segmentation methods.
* Fundamentals of multiscale image analysis.
Basic bibliography:
Gonzalez y Woods. Digital image processing. ISBN: 0-20-118075-8.
Complementary bibliography:
D.A. Forsyth y J. Ponce. Computer Vision. ISBN 0-13-085198-1.
Steger & Wiedemann. Machine Vision Algorithms and Applicacions. ISBN 978-3-527-4073.
CT1. Practice the profession with a clear awareness of its human, economic, legal and ethical dimension and with a clear commitment to quality and continuous improvement.
CT4- Ability to understand the meaning and application of gender perspective in different areas of knowledge in professional practice with the objective of achieving a more fair and egalitarian society.
CG2. Ability to analyze the needs of a company in the field of computer vision and determine the best technological solution for it.
CG4. Capacity for critical analysis and rigorous evaluation of technologies and methodology.
CG5. Ability to identify unsolved problems and provide innovative solutions.
CG7. Autonomous learning ability for specialization in one or more fields of study.
CE1. Know and apply the concepts, methodologies and technologies of image processing.
CE3. Know and apply the concepts, methodologies and technologies of image and video analysis.
The methodology followed uses the Virtual Campus of the USC-UDC as a basic platform. In the virtual classroom of the subject, the students will have all the information (theory material, class slides, practice scripts, etc.)
* Master sessions: oral exposition complemented with the use of audiovisual media and the introduction of questions for the students, in order to transmit knowledge and facilitate learning.
* Laboratory practices: Practical resolution of different image problems through the application of image processing techniques explained during the master sessions.
* Research: proposal of two practical situations in image analysis that require students to identify the problem under study, formulate it accurately, develop the relevant procedures, interpret the results and draw the appropriate conclusions from the work done.
The CT1, CG2, CG4, CG5 and CG7 competences are developed mainly in the development of research projects, and the CE1 and CE2 competences are developed in master classes, carrying out exercises and research projects. The CT4 objective is developed transversally during the course.
The evaluation of the subject consists of two parts:
60%: continuous assessment of activities carried out in the classroom.
40%: carrying out a research project throughout the course.
In the case of fraudulent performance of exercises or tests, the provisions of the Regulations for academic performance of students and review of qualifications will apply.
In application of the ETSE regulations on plagiarism (approved by the ETSE Council on 12/19/2019) , the total or partial copy of some practice or theory exercise will mean failure on both occasions of the course, with a grade of 0.0 in both cases.
This subject has 6 ECTS credits, corresponding to a total workload of 150h (presentiality of 7h / credit). This time can be broken down into the following sections:
PRESENTIAL WORK IN CLASSROOM:
* Master classes: 14 hours
* Laboratory practices: 15 hours
* Research (project): 10 hours
* Objective test: 3 hours
Total hours of classroom work in the classroom: 42 hours
PERSONAL WORK OF THE STUDENTS:
* Autonomous study: 24 hours
* Laboratory practices: 44 hours
* Research (project): 40 hours
Total: 108 hours
It is recommended to bring the matter up to date and the use of tutorials to clarify doubts and advise in the development of the project.
The virtual campus of the UDC.
The subject will be taught in English.
Eva Cernadas García
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881816459
- eva.cernadas [at] usc.es
- Category
- Professor: University Lecturer
| Friday | |||
|---|---|---|---|
| 15:30-18:30 | Grupo /CLIL_01 | English | Aula A9 |