Competencies and objectives

 

Course context for academic year 2024-25

La visión artificial es un área de la inteligencia artificial cuyo objetivo principal es hacer posible que la máquinas interpreten imágenes y videos. Gracias a la posiblidad de analizar entradas visuales, tiene aplicación en multitud de campos como la medicina, la industria, la seguridad, etc. En la actualidad, el uso de técnicas avanzadas de aprendizaje profundo y la capacidad de procesar grandes cantidades de datos han llevado a importantes avances en la visión artificial.

En esta asignatura se profundizará en las técnicas de procesamiento y análisis de imágenes y videos mediante el uso de herramientas avanzadas de visión artificial. En cuanto a las técnicas de análisis de secuencias de imágenes y video, permitirá a los estudiantes entender cómo se pueden extraer patrones temporales de datos visuales y cómo se pueden aplicar a aplicaciones como la vigilancia, el seguimiento de objetos y la detección de eventos anómalos. Además, se estudiarán técnicas de procesamiento y análisis de datos tridimensionales que pueden ser utilizados en aplicaciones como la robótica, la realidad virtual y aumentada, la inspección industrial, la ingeniería biomédica. Por último, se estudiará la generación de imágenes y video utilizando redes neuronales profundas, lo que permitirá a los estudiantes entender cómo se pueden utilizar estas técnicas para sintetizar imágenes y video a partir de modelos de aprendizaje profundo.

Se espera que al final de la asignatura, los estudiantes hayan adquirido habilidades en el uso de técnicas avanzadas de visión artificial y sean capaces de aplicarlas para resolver problemas en distintos campos. También se espera que los estudiantes comprendan las limitaciones y desafíos en el uso de estas técnicas, y estén al tanto de las últimas tendencias y desarrollos en el campo de la visión artificial.

 

 

Course content (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2024-25

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 2024-25

No data

 

 

General

Code: 43512
Lecturer responsible:
AZORIN LOPEZ, JORGE
Credits ECTS: 4,50
Theoretical credits: 0,90
Practical credits: 0,90
Distance-base hours: 2,70

Departments involved

  • Dept: INFORMATION TECHNOLOGY AND COMPUTING
    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