Teaching plan for the course unit

 

 

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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

75

 

-  Lecture

Online

 

14

 

-  Lecture with practical component

Online

 

14

 

-  Document study

Online

 

42

 

-  Student presentation and discussion

Face-to-face

 

5

 

 

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

Book

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

Journal

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