Competencies and objectives

 

Course context for academic year 2025-26

Computer vision is an area of artificial intelligence whose main objective is to enable machines to interpret images and videos. Thanks to the possibility of analysing visual inputs, it has applications in many fields such as medicine, industry, security, etc. Currently, the use of advanced deep learning techniques and the ability to process large amounts of data have led to significant advances in computer vision.

In this subject we will study in depth the techniques of image and video processing and analysis through the use of advanced artificial vision tools. As for image and video sequence analysis techniques, it will allow students to understand how temporal patterns can be extracted from visual data and how they can be applied to applications such as surveillance, object tracking and anomalous event detection. In addition, they will study three-dimensional data processing and analysis techniques that can be used in applications such as robotics, virtual and augmented reality, industrial inspection, biomedical engineering. Finally, image and video generation using deep neural networks will be studied, allowing students to understand how these techniques can be used to synthesise images and video from deep learning models.

By the end of the course, students are expected to have acquired skills in the use of advanced computer vision techniques and be able to apply them to solve problems in different fields. Students are also expected to understand the limitations and challenges in the use of these techniques, and to be aware of the latest trends and developments in the field of computer vision. 

 

 

Course competencies (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2025-26

Transversal Competences

  • CT1 : Be able to lead projects related to artificial intelligence, as well as to manage work teams.
  • CT2 : Demonstrate computer and information skills in the field of artificial intelligence.
  • CT3 : Show oral and written communication skills.

 

General Competences

  • CG10 : Be able to use engineering principles and modern computer technologies to research, design and implement new applications of artificial intelligence,
  • CG2 : Be able to develop and learn in a self-directed or autonomous way topics related to artificial intelligence.
  • CG6 : Being able to adapt to environments related to artificial intelligence, fostering teamwork, creativity,
  • CG7 : To be able to adapt to the constant evolution of the discipline and to understand and apply new technical and scientific developments related to artificial intelligence.
  • CG8 : Knowing how to plan, design, develop, implement and maintain products, applications and services related to artificial intelligence, taking into account technical, economic and efficiency aspects.

 

Specific Competences

  • CE12 : Know and apply advanced computer vision techniques for applications in different fields.

 

Basic Competences

  • CB10 : That students possess the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous.
  • CB6 : 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
  • CB7 : That students know how to apply the knowledge acquired and their ability to solve problems in new or little-known environments within broader (or multidisciplinary) contexts related to their area of ¿¿study
  • CB8 : Students are able to integrate knowledge and deal with the complexity of making judgements on the basis of incomplete or limited information, including reflections on the social and ethical responsibilities associated with information which, while incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgements.
  • CB9 : Students are able to communicate their conclusions and the ultimate knowledge and rationale behind them to specialist and non-specialist audiences in a clear and unambiguous way.

 

 

 

Learning outcomes (Training objectives)

- Know and understand advanced analysis techniques using artificial vision on image, video and 3D scenes.

- Design neural network architectures for the detection, tracking and recognition of regions of interest in the scene.

- Conceive integral 3D vision solutions to particular problems in different fields.

- Know and implement video sequence analysis techniques to tackle different problems and application levels.

- Identify the best neural network architectures to solve image and video generation problems for different fields.

 

 

Specific objectives stated by the academic staff for academic year 2025-26

Specifically, the main objectives are the following:

  • To understand advanced deep learning techniques in artificial vision, and how they can be used for the detection, recognition and classification of objects, scenes and events, among others.
  • To acquire advanced knowledge in 3D and 2.5D data processing and analysis techniques, and to understand how they can be applied in different fields.
  • Learn methods and techniques for analysing image and video sequences, and understand how they can be used to extract temporal patterns and detect events.
  • Understand image and video generation using deep neural networks, and how they can be used for image and video synthesis from different deep learning models.
  • Develop skills to design and implement computer vision solutions in a wide variety of applications, and have a thorough understanding of the challenges and limitations associated with computer vision.

 

 

General

Code: 43512
Lecturer responsible:
Azorín López, Jorge
Credits ECTS: 4,50
Theoretical credits: 0,90
Practical credits: 0,90
Distance-base hours: 2,70

Departments involved

  • Dept: Computer Science and Technology
    Area: COMPUTER ARCHITECTURE
    Theoretical credits: 0,9
    Practical credits: 0,9
    This Dept. is responsible for the course.
    This Dept. is responsible for the final mark record.

Study programmes where this course is taught