Checking date: 16/05/2022

Course: 2022/2023

Perception Systems
Study: Bachelor in Industrial Electronics and Automation Engineering (223)

Coordinating teacher: ESCALERA HUESO, ARTURO DE LA

Department assigned to the subject: Systems Engineering and Automation Department

Type: Electives
ECTS Credits: 6.0 ECTS


By the end of this content area, students will be able to have: 1. coherent knowledge of their branch of engineering including some at the forefront of the branch in perception systems; 2. the ability to apply their knowledge and understanding of perception systems to identify, formulate and solve engineering problems using established methods; 3. the ability to apply their knowledge and understanding to develop and realise designs to meet defined and specified requirements; 4. an understanding of design methodologies, and an ability to use them. 5. the ability to select and use appropriate equipment, tools and methods; 6. the ability to combine theory and practice to solve problems of perception systems 7. an understanding of applicable techniques and methods in perception systems, and of their limitations;
Skills and learning outcomes
Description of contents: programme
1.- Introduction to Computer Vision. 1.1. Definitions. 1.2. History 1.3. Modules 1.4. Human vision sense 1.5. Applications 2.- Digital images. 2.1. Spatial sampling, grey levels. 2.2. Pixels. 2.3. Arithmetical and logical Operations. 2.4. Colour. 3.- Image Pre-processing. 3.1. Contrast 3.2. Noise reduction 3.3. Image sharpening 3.4 Edge detection. 4.- Segmentation. 4.1. Thresholding and labelling. 4.2. Region growing. 4.3. Split & Merge. 4.4. Mean-Shift 5.- Morphological Transforms and object description. 5.1. Morphological Transforms for binary images 5.2. Morphological Transforms for grey level images 5.3. Region descriptors. 5.4. Shape descriptors. 6.- Object recognition. 6.1. Basic concepts. 6.2. Bayes classifier. 6.3. Clustering. 7. Neural Networks 7.1 Introduction 7.2 Neural networks 7. 3 Loss function, gradient descent and retro-propagation 8. Deep learning. 8.1 Introduction 8.2 Convolutional neural networks 9. Architectures 9.1. Classifiers 9.2 Object detectors 9.3 Semantic segmentation. 10. Additional Techniques. 10.1 Variations to gradient descent. 10.2 Initialization 10.3 Regularization.
Learning activities and methodology
The learning activities and methodology are: - Lectures, classes for resolution of doubts in small groups, student presentations, tutorials and individual work of students; aimed at the acquisition of knowledge (3 ECTS). - Laboratory practices and sections of problems in small groups, individual tutorials and individual work of students, aimed at the acquisition of practical skills related to the syllabus of the subject (3 ECTS).
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • GONZALEZ, R. Digital image processing. Addison-Wesley.
  • Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press. 2016
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Gary Bradski, Adrian Kaehler. Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media. 2008
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

The course syllabus may change due academic events or other reasons.