Teaching plan for the course unit



Close imatge de maquetació




General information


Course unit name: Computer Vision

Course unit code: 572674

Academic year: 2021-2022

Coordinator: Sergio Escalera Guerrero

Department: Department of Mathematics and Computer Science

Credits: 3

Single program: S



Estimated learning time

Total number of hours 75


Face-to-face and/or online activities



-  Lecture





-  Lecture with practical component





-  Document study





-  Student presentation and discussion






Competences to be gained during study


CE6 - That students can apply in an effective way analytics and predictive machine learning tools


CE7 - That students can understand, develop, and update analytics and exploratory algorithms to work with data





Learning objectives


Referring to knowledge

Know the basics of image processing


Be able to extract discriminative features from images


Learn pattern recognition methods from image features


Know the state of the art methodologies to segment and recognize objects in images


Know the basics of affective computing from a computer vision perspective


Know the basic on human behavior analysis



Teaching blocks


1. Introduction to computer vision

*  Introduction to computer vision

2. Image processing principles

*  Image processing principles

3. High level features

*  High level features

4. Object recognition

*  Object recognition

5. Deep Learning and Convolutional Neural Networks

*  Deep Learning and Convolutional Neural Networks

6. Deep detection and segmentation

*  Deep detection and segmentation

7. Human poses

*  Human poses

8. Face analysis and affective computing

*  Face analysis and affective computing

9. Behaviour analysis

*  Behaviour analysis



Teaching methods and general organization


In-class lectures describing the content of the different course modules, and classes with practical sessions. The course will also have student presentations related to recent published computer vision works.



Official assessment of learning outcomes


40% of the final score: practical deliveries and paper report

40% of the final score: student presentation

20% of the final score: final course exam


Examination-based assessment

50% of the final score: practical deliveries and paper report

50% of the final score: final course exam



Reading and study resources

Consulteu la disponibilitat a CERCABIB


Goodfellow, Ian ; Bengio, Yoshua ; Courville, Aaron. Deep learning book. MIT  Enllaç

Edició electrònica d’accés lliure  Enllaç

Russ, John C. ; Brent Neal, F. The image processing handbook. Boca Raton : CRC Press, 2016.  Enllaç


Corneanu, Ciprian A. ; Oliu, Marc ; Cohn, Jeffrey F. ; Escalera, Sergio. Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, n. 82016.

Accés consorciat per als usuaris de la UB  Enllaç

Web page

Conference on Computr Vision and Pattern Recognizion (CVPR) 2020

https://openaccess.thecvf.com/CVPR2020  Enllaç