Dades generals

Curs acadèmic:
2017
Descripció:
This course deals with all the steps needed to develop an image recognition/classification system. Well-know processes such as image segmentation, image characterization and image classification will be covered in detail.
Crèdits:
6

Grups

Grup A

Durada:
Semestral, 2n semestre
Professorat:
XAVIER LLADO BARDERA
Idioma de les classes:
Anglès (100%)

Continguts

1. Course presentation

2. Characterization

3. Segmentation

4. Classification

5. Scene classification & description

6. Applications

Activitats

Tipus d’activitat Hores amb professor Hores sense professor Total
Classes expositives 24 3 27
Elaboració de treballs 16 54 70
Exposició dels estudiants 4 16 20
Lectura / comentari de textos 0 16 16
Resolució d'exercicis 8 12 20
Total 52 101 153

Bibliografia

  • González, Rafael C., Woods, Richard E., Eddins, Steven L. (cop. 2004). Digital image processing using Matlab. Upper Saddle River: Prentice Hall. Catàleg
  • Editor: Robert B. Fisher (2007). CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Comp. Recuperat 01/01/2007, a http://homepages.inf.ed.ac.uk/rbf/CVonline/
  • Forsyth, David A., Ponce, Jean (cop. 2003). Computer vision : a modern approach. Upper Saddle River: Prentice Hall. Catàleg
  • Duda, Richard O., Hart, Peter E., Stork, David G. (cop. 2001). Pattern classification (2nd ed.). New York [etc.]: John Wiley & Sons. Catàleg

Avaluació i qualificació

Activitats d'avaluació:

Descripció de l'activitat Avaluació de l'activitat %
P1 Practical session: Texture characterization with MATLAB 70% strategy & results + 30% document
P2 Practical session: Image segmentation with MATLAB 70% strategy & results + 30% document
P3 Practical session: Image classification with MATLAB 70% strategy & results + 30% document
Lecture prepared by VIBOT students 50% document + 50% presentation and interaction
Project design & development 70% strategy and results + 30% technical report

Qualificació

The evaluation is based on three different activities: 30% from P1&P2&P3 + 40 % from Final PROJECT + 30% by evaluating the lectures given by the students.

Criteris específics de la nota «No Presentat»:
NP will be considered when the students do not submit any of the evaluation activities (P1, P2, P3, Final project, and Lecture activity)

Observacions

Mentoring will be held in the office 015 of building P-IV.