ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 30 Interactive Classroom: 20 Total: 51
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Electronics and Computing
Areas: Computer Science and Artificial Intelligence, Languages and Computer Systems
Center Higher Technical Engineering School
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable
Understand the basic concepts of digital image processing, the different available techniques, and their application scope.
Once the course has been passed, the student:
- Will understand the basic concepts of digital image processing.
- Will know how to implement and apply fundamental algorithms and techniques for the processing, analysis, and interpretation of digital images.
- Will develop the ability to apply the most suitable computer vision tool to real-world problems.
1. Image formation
2. Basic image processing
3. Edge, corner, and blob detectors
4. Stereo vision and reconstruction
5. Object detection
6. Image segmentation
BASIC
- Richard Szeliski, Computer Vision: Algorithms and Applications, Springer, 2010.
- Rafael C. González and Richard E. Woods, Digital Image Processing, Pearson, 4th ed., 2018
SUPPLEMENTARY
- Gary Bradski and Adrian Kaehler, Learning OpenCV 4, O’Reilly, 2019.
- Simon Prince, Computer Vision: Models, Learning, and Inference, Cambridge University Press, 2012.
BASIC AND GENERAL
- CB2: Students must be able to apply their knowledge to their work or vocation in a professional way and possess the competencies usually demonstrated through the development and defense of arguments and the resolution of problems within their area of study.
- CB4: Students must be able to convey information, ideas, problems, and solutions to both specialized and non-specialized audiences.
- CG4: Ability to select and justify the appropriate methods and techniques to solve a specific problem, or to develop and propose new methods based on artificial intelligence.
- CG5: Ability to design new computational systems and/or evaluate the performance of existing systems that integrate AI models and techniques.
TRANSVERSAL
- TR1: Ability to communicate and share knowledge, skills, and competencies.
- TR3: Ability to create new models and solutions independently and creatively, adapting to new situations. Initiative and entrepreneurial spirit.
- TR5: Ability to develop AI-based models, techniques, and solutions that are ethical, non-discriminatory, and trustworthy.
SPECIFIC
- CE12: Understand the fundamentals of AI algorithms and models for solving complex problems, understand their computational complexity, and be able to design new models.
The course will combine individual and collaborative work, encouraging active student participation in both lectures and tutorials. Theoretical and practical sessions will aim to progressively develop the expected competencies through student-centered methodologies.
Each thematic block will be introduced by the instructor with a presentation of its objectives, key contents, and recommended resources. From there, students will be provided with supplementary documentation, readings, examples, and support materials to guide their independent study. These sessions will support competencies such as CG4, CG5, and CE12.
Throughout the course, various tasks will be proposed, including practical exercises, problem-solving, oral presentations, or small projects, which may be carried out individually or in groups. These activities will have predefined delivery or presentation deadlines, which will be communicated through the course’s official communication tools.
In practical sessions, students will apply their acquired knowledge using specific software tools for each topic. These sessions will foster competencies such as CB2, CB4, CG5, TR1, TR3, and CE12.
Student work will be carried out independently, always with the support and guidance of the teaching staff. The course will also include lab guides, seminars, and other complementary activities to reinforce learning.
Assessment will consider both the theoretical part (40%) and the practical part (60%). To pass the course, a global grade equal to or higher than 5 out of 10 is required, as detailed below:
- Theoretical part: Assessed through a single exam held on the official date. A minimum grade of 4 out of 10 is required to pass the course. Otherwise, the exam must be repeated in the resit opportunity.
- Practical part: The practical exercises will correspond to the topics described in the Contents Section. At the end of each topic, the students will be required to solve an exercise, that will be assessed in the next session. A minimum grade of 4 out of 10 is required in this part to pass the course.
Students who do not attend the exam and do not participate in any mandatory evaluation activity will be marked as Not Presented.
To pass the course in the second opportunity, students must complete all pending mandatory assessment components, as previously specified. Grades from the rest of the course will be retained.
Class attendance will not be compulsory nor will it have any impact on the qualification, according to what it is allows in article 1 of the Regulations for class attendance in the USC undergraduate and master's degree programs.
In accordance with the ETSE plagiarism policy (approved by the ETSE Council on 19/12/2019), total or partial copying of any practical or theoretical exercise will result in failure of both course opportunities, with a grade of 0 in each case.
- Classroom time: 50 total hours, including 24 hours of lectures and 26 hours of interactive sessions.
- Personal study time: 100 total hours, including 40 hours of independent study of theory and practical work, and 60 hours dedicated to assignments, projects, and other activities.
It is recommended to have passed the courses "Signal Acquisition and Processing", "Supervised Machine Learning", "Unsupervised Machine Learning", and "Neural Networks and Deep Learning".
Students are encouraged to solve, implement, test, and validate all proposed exercises and practices. It is also important to make use of tutorials to resolve doubts.
Teaching will be reinforced through the use of the Virtual Campus, which will serve as the central hub for accessing course materials (presentations, texts, exercises, readings...), and for communication with instructors via email, forums, or online tutorials.
The course will be taught in Galician.
Xosé Manuel Pardo López
- Department
- Electronics and Computing
- Area
- Languages and Computer Systems
- Phone
- 881816438
- xose.pardo [at] usc.es
- Category
- Professor: University Lecturer
Nicolas Vila Blanco
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881815509
- nicolas.vila [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
Cesar Díaz Parga
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- cesardiaz.parga [at] usc.es
- Category
- Xunta Pre-doctoral Contract
Marta Nuñez Garcia
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- martanunez.garcia [at] usc.es
- Category
- Researcher: Ramón y Cajal
Constanza De La O Andion Garcia
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- constanza.andion.garcia [at] usc.es
- Category
- Ministry Pre-doctoral Contract
| Monday | |||
|---|---|---|---|
| 15:30-17:30 | Grupo /CLIL_01 | Spanish, Galician | IA.03 |
| Wednesday | |||
| 15:30-17:30 | Grupo /CLE_01 | Galician | IA.01 |
| Thursday | |||
| 17:00-19:00 | CLIL_02 | Galician, Spanish | IA.03 |
| 01.12.2026 16:00-20:00 | Grupo /CLE_01 | IA.01 |
| 01.12.2026 16:00-20:00 | CLIL_02 | IA.01 |
| 01.12.2026 16:00-20:00 | Grupo /CLIL_01 | IA.01 |
| 06.23.2026 16:00-20:30 | Grupo /CLIL_01 | IA.01 |
| 06.23.2026 16:00-20:30 | Grupo /CLE_01 | IA.01 |
| 06.23.2026 16:00-20:30 | CLIL_02 | IA.01 |