Educational guide
IDENTIFYING DATA 2018_19
Subject ARTIFICIAL VISION Code 00709043
Study programme
GRADO EN INGENIERÍA INFORMÁTICA
Descriptors Credit. Type Year Period
6 Optional Fourth Second
Language
Ingles
Prerequisites
Department ING.ELECTR.DE SIST. Y AUTOMATI
Coordinador
ALEGRE GUTIÉRREZ , ENRIQUE
E-mail ealeg@unileon.es
vgonc@unileon.es
Lecturers
ALEGRE GUTIÉRREZ , ENRIQUE
GONZÁLEZ CASTRO , VICTOR
Web http://
General description The course covers the main concepts related to artificial vision, focusing on some practical applications such as people and object detection, face recognition and similar images and videos detection and retrieval. During the lectures, the principal methods and concepts related to artificial vision are explained. In the lab, first sessions are devoted to learning Python and computer vision, and machine learning libraries are introduced gradually. The course is evaluated based on the results presented by the student during the realisation of a Course Project. The students will choose a subject related to vision, among the offered, and they will implement it using Python.
Tribunales de Revisión
Tribunal titular
Cargo Departamento Profesor
Presidente ING.ELECTR.DE SIST. Y AUTOMATI FOCES MORAN , JOSE MARIA
Secretario ING.ELECTR.DE SIST. Y AUTOMATI FUERTES MARTINEZ , JUAN JOSE
Vocal ING.ELECTR.DE SIST. Y AUTOMATI REGUERA ACEVEDO , PERFECTO
Tribunal suplente
Cargo Departamento Profesor
Presidente ING.ELECTR.DE SIST. Y AUTOMATI RIESCO PELAEZ , FELIX
Secretario ING.ELECTR.DE SIST. Y AUTOMATI ALAIZ RODRIGUEZ , ROCIO
Vocal ING.ELECTR.DE SIST. Y AUTOMATI GARCIA RODRIGUEZ , ISAIAS

Competencias
Code  
A8497
A8499
A8504
A8571
A8602
C2
C4
C5

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 Learning to use Python to program simple machine vision applications Be able to use computer vision techniques learned to solve new problems A8497
A8499
A8504
A8571
A8602
 C2
C4
C5

Contents
Topic Sub-topic
Lectures 1. Introduction to Computer Vision
2. Basic concepts
3. Pre-processing
4. Edges detection
5. Segmentation
6. Corners detection
7. Descriptors
8. Classification
9. Deep Learning
Labs Several practicals, related to the theoretical lessons, oriented to learn how to program in Python and, at the same time, to use this language to solve problems related to computer vision.

Planning
Methodologies  ::  Tests
  Class hours Hours outside the classroom Total hours
Problem solving, classroom exercises 20 0 20
 
Laboratory practicals 75 0 75
 
Lecture 45 0 45
 
10 0 10
 
(*)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 using Excel
Laboratory practicals The practicals are carried out using Python (3.5 or later). Available in the laboratory F3 although it is recommended that each student also install it on her/his personal computer. A first lab about how to install and use of the environment will be conducted. Several labs aimed at learning basic Python combined with computer vision and machine learning concepts will be carried out later. Finally, some specific examples related with image classification will be the subject of the practicals. These examples will help students doing their final projects. Using Python 3.5 from Anaconda installation environment (https://www.continuum.io/downloads). the Spyder will be used IDE included in the installation.
Lecture Theoretical sessions in the classroom using slides.

Personalized attention
 
Lecture
Problem solving, classroom exercises
Laboratory practicals
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
The subject will be evaluated by the implementation and presentation of a project that will be divided into the next 5 phases. Each of them will be graded up to 20 points.


1. The student will perform a theoretical study of the subject, presenting a report that will deliver and will be evaluated. This report will contain a description of the problem, an explanation of its interest and a review of published work. This study will be delivered in mid-March.

2. In a second stage, the student will perform a review of possible available implementations, mainly in Python, related to the selected topic. It will be a critical review that will conclude with conclusions about the possibilities and limitations of the implementations of the revised methods. The study will be delivered by the end of March.

3. As a result of the previous document, the student will select one of the reviewed implementations, or perform a self-implementation if he/she wishes, and evaluate the method with a known dataset or with a self-constructed dataset. With this implementation, the student will evaluate the method studied and will test, in a practical way, how it works.

4. The student will prepare and make a brief presentation during the last weeks of class. The presentation is part of the evaluation, and all students are asked to attend this class.

5. After the presentation, a validation test will be conducted where each student will have to answer various questions related to the subject studied. 		90
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.
Furthermore, students will be asked to carried out online courses in a MOOC Platform, Datacamp, if it is available to the course. Each course completed will give to the student up to 5 extra points. In that case, the previous 5 activities for the evaluation will have 15 points each one, instead of 20.		 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.
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