Educational guide
IDENTIFYING DATA 2018_19
Subject COMPUTER VISION AND LEARNING Code 00715007
Study programme
MASTER UNIV. INGENIERIA INFORMATICA
Descriptors Credit. Type Year Period
4.5 Compulsory First Second
Language
Ingles
Prerequisites
Department ING.ELECTR.DE SIST. Y AUTOMATI
Coordinador
ALEGRE GUTIÉRREZ , ENRIQUE
E-mail ealeg@unileon.es
ralar@unileon.es
vgonc@unileon.es
Lecturers
ALAIZ RODRÍGUEZ , ROCÍO
ALEGRE GUTIÉRREZ , ENRIQUE
GONZÁLEZ CASTRO , VICTOR
Web http://
General description This course covers fundamental topics in artificial vision with a focus on object recognition. During the course, the main ideas related with color, segmentation, image features, shape, texture and object recognition will be explained. During labs sessions some of the methods viewed in class will be programmed using MATLAB. A Final Project will be carried out allowing to students to explore in depth a chosen topic.
Tribunales de Revisión
Tribunal titular
Cargo Departamento Profesor
Presidente ING.MECANICA,INFORMAT.AEROESP. MATELLAN OLIVERA , VICENTE
Secretario ING.ELECTR.DE SIST. Y AUTOMATI FOCES MORAN , JOSE MARIA
Vocal ING.ELECTR.DE SIST. Y AUTOMATI GARCIA RODRIGUEZ , ISAIAS
Tribunal suplente
Cargo Departamento Profesor
Presidente ING.MECANICA,INFORMAT.AEROESP. FERNANDEZ LLAMAS , CAMINO
Secretario ING.MECANICA,INFORMAT.AEROESP. RODRIGUEZ DE SOTO , ADOLFO
Vocal ING.ELECTR.DE SIST. Y AUTOMATI RODRIGUEZ SEDANO , FRANCISCO JESUS

Competencies
Type A Code Competences Specific
  A13233
  A13249
  A13260
  A13261
Type B Code Competences Transversal
  B3064
  B3065
  B3066
  B3068
Type C Code Competences Nuclear
  C1
  C3

Learning aims
Competences
Understand the main concepts related to computer vision and digital image processing Understand and know how to program description techniques and object recognition based on machine vision Learn to use Matlab to program simple computer vision applications Be able to use computer vision techniques and machine learned to solve new problems A13233
A13249
A13260
A13261
B3064
B3065
B3066
B3068
C1
C3

Contents
Topic Sub-topic
Lectures 1. Computer Vision concepts and common operations.

2. Image descriptors: features and methods

3. Image classification

4. Learning: classical Machine Learning and deep learning methods
Labs Several labs sessions covering the concepts related with lectures:

1. Programming in Python
2. Feature extraction from images and preprocessing.
3. Image Classification
4. Classical machine learning
5. Deep learning

Planning
Methodologies  ::  Tests
  Class hours Hours outside the classroom Total hours
Problem solving, classroom exercises 12 0 12
 
Laboratory practicals 40.5 0 40.5
Assignments 25 0 25
 
Lecture 31 0 31
 
Extended-answer tests 4 0 4
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies   ::  
  Description
Problem solving, classroom exercises Exercises done on the blackboard and through slides and examples
Laboratory practicals Guided labs based on programming, used to evaluate and to learn more in deep some methods and techniques discussed in class.
Assignments A specific subject will be studied by each student. A general explanation of the chosen subject will be carried out and a specific study, mainly based in a research paper, will be performed.
Lecture Theoretical sessions in the classroom using slides.

Personalized attention
 
Lecture
Problem solving, classroom exercises
Laboratory practicals
Assignments
Description
Inquiries may be made to the instructor in person or on-line.

The online inquieries could be carried out using a general forum, or specific forums for each activity.

Appointments with the instructor will be made using the Agora.

Assessment
  Description Qualification
Assignments The student will choose a topic proposed by the teachers and will have to study it and defend it. Material prepared and the defence conducted will be assessed.
The subject could have a practical part.

In addition, to pass the course, is necessary to submit correctly all requested deliveries. Some practices will require to pass a validation exam.
50
Extended-answer tests The subject is assessed by a final exam and by submitted homeworks and its corresponding validation examination.

The final examination consists in questions about the theoretical concepts and exercises viewed during the lectures and about practicals carried out in the Labs.
40
Others Participation in the course as a function of the intervention and student attitude in lectures, help in the review of materials and their interaction with the teacher.
10
 
Other comments and second call
<div>Delayed deliveries will suffer a penalty in the note.</div><div><br /></div><div><br /></div><div>Students who do not pass any of the two assignments (homework) or the Final Project in the continuous evaluation, could deliver what they lack in the period of the first ordinary call.</div><div><br /></div><div><br /></div><div>To pass the course on the second ordinary call, the student must submit the two assignments due and the final project. In the second call the grade will be subject of penalty.</div><div><br /></div>

Sources of information
Access to Recommended Bibliography in the Catalog ULE

Basic
Richard Szeliski. Computer Vision: Algorithms and Applications, Springer, 2011
(pdf available online: http://szeliski.org/Book/)

Pattern Classification (2nd Edition), by R.O. Duda, P.E. Hart, and D.G. Stork, Wiley-Interscience, 2000.

Alegre, E., Sánchez, L., Fernández, R.A. y Mostaza, J.C. (2003). Procesamiento Digital de Imagen: Fundamentos y Prácticas con Matlab. Secretariado de Publicaciones y Medios Audiovisuales de la Universidad de León.


González, R. C. y Woods, R. E. (2008). Digital Image Processing (Third Edition). Prentice Hall.

Introduction to Machine Learning. 2nd Edition. Ethem Alpaydin. The MIT Press, 2010.

Principles of Data Mining, Max Bramer, Springer-Verlag, 2007,

Combining Pattern Classifiers: Methods and Algorithms, L. Kuncheva, Wiley, Second Edition, 2014

Complementary

Pajares, G y de la Cruz, J.M. (2001). Visión por Computador. Ra-ma.
González, J. (2000). Visión por Computador. Paraninfo.
González, R. C. y Woods, R. E. (1996). Tratamiento digital de imágenes. Addison-Wesley /Diaz de Santos.

Shapiro, L. & Stockman G. (2001). Computer Vision. Prentice-Hall.

Parker, J.R. (1997). Algorithms for image processing and computer vision. John Wiley & Sons, Inc.
Trucco, E. & Verri, A. (1998). Introductory Techniques for 3-D Computer Vision. Prentice-Hall.
Maravall, D. (1993). Reconocimiento de formas y visión artificial. Ra-ma
Davies, E.R. (1996). Machine Vision: Theory, Alforithms, Practicalities. Academic Press.


Recommendations


Subjects that it is recommended to have taken before
INTRODUCTION TO INTELLIGENT SYSTEMS / 00709016
 
Other comments
The "Artificial Vision" course, in 8th semester, Grado en Ingeniero en Informatica, is also a recommended subject. Any course about Computer Vision or Machine Learning will help to the student to follow this subject. Programming skills, better in Matlab, are recommended.