Teaching plan for the course unit

 

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General information

 

Course unit name: Computer Vision

Course unit code: 572674

Academic year: 2018-2019

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 learning activities

75

 

-  Lecture with practical component

 

30

 

-  Document study

 

45

 

 

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 text analysis 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. Multiclass and multilabel recognition

*  Multiclass and multilabel recognition

6. Image segmentation

*  Image segmentation

7. Image retrieval

*  Image retrieval

8. Text detection and analysis

*  Text detection and analysis

9. Scene understanding and captioning

*  Scene understanding and captioning

10. Face analysis and affective computing

*  Face analysis and affective computing

11. Behaviour analysis

*  Behaviour analysis

 

 

Teaching methods and general organization

 

Oral presentation of the content in combination with practical sesions associated to the different lectures of the course.

 

 

Official assessment of learning outcomes

 

50% of the final score: reports and code associated to the different practical sessions of the course

50% of the final score: final course exam

 

Examination-based assessment

50% of the final score: reports and code associated to the different practical sessions of the course

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

Conference on Computr Vision and Pattern Recognizion (CVPR) 2016  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