Educational guide
IDENTIFYING DATA 2021_22
Subject ARTIFICIAL VISION Code 00709043
Study programme
0709 - GRADO EN INGENIERÍA INFORMÁTICA
Descriptors Credit. Type Year Period
6 Optional Fourth Second
Language
Castellano
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  
A18131
B5614
B5619
B5623
B5625
B5627
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 A18131
B5614
B5619
B5623
B5625
C2
C5
A18131
B5614
B5623
C4
C5
A18131
B5614
B5619
B5623
B5625
C2
C4
C5
A18131
B5614
B5619
B5623
B5625
B5627
C4
C5

Contents
Topic Sub-topic
Block I. Lectures Lesson 1: INTRODUCTION TO ARTIFICIAL VISION
What is artificial vision. Its use Advantages and limitations.

Lesson 2: BASIC CONCEPTS
Image concepts, resolution, models and colour modes.

Lesson 3: PREPROCESSING
Main operations to pre-process images (arithmetic, logic, morphological).

Lesson 4: EDGE DETECTION
Edge concept. Edge detection methods. High pass filters for edge detection.

Lesson 5: SEGMENTATION
Segmentation concept. Use of filters as a form of segmentation. Thresholding segmentation methods. Region-oriented segmentation.


Lesson 6: CORNER DETECTION
Corner concept. Moravec detector. Harris Detector.


Lesson 7: DESCRIPTORS
Descriptor concept. Shape descriptors. Texture descriptors. Descriptors specified in local inventorial characteristics.


Lesson 8: MACHINE LEARNING
Supervised learning. Main supervised learning methods. Grouping Evaluation methods.


Lesson 9: DEEP LEARNING
Fundamental parts of neural networks. Cost function. Neural network training. Optimization
Block II. Labs Several practicals 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. Also, exercises could be left in Agora, with solutions, most of the times also with examples solved in 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 3.6 from Anaconda installation environment (https://www.continuum.io/downloads). the Spyder will be used IDE included in the installation. The practices will be carried out following the scripts left in Agora. Doubts will be resolved by consulting the teacher. Your solution will be delivered in a task enabled for it in Agora.
Lecture Theoretical sessions in the classroom using slides Presentations or documents corresponding to the materials of each lesson will be left in Agora and a forum for doubts will be set up. Some lessons will be accompanied by videos related to the concepts presented, some recorded by the teachers and others from internet resources that the teachers consider especially appropriate. Some lessons will be accompanied by a questionnaire with questions, which can be both theoretical and practical, whose deliveries will be evaluable. Datacamp platform courses, or similar ones, could be used to reinforce some of the lessons taught, with some courses being optional and others being mandatory.

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.

Doubts could be solved as well using remote meetings with the instructors using videoconference.

Assessment
  Description Qualification
55 points (out of 100)

The subject will be evaluated by the implementation and presentation of a project that will be divided into the next four phases.

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.

2. 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.

3. The student will prepare and make a brief presentation with both theoretical and practical parts. The presentation is part of the evaluation and it will be graded by the instructor.

4. After the presentation, a validation test will be conducted where each student will have to answer various questions related to the subject studied.

IN THE CASE ONLINE TEACHING IS NECESSARY:
The presentations of the theoretical and practical parts of the project will be made to the teacher through videoconference.

The validation exam will be oral, also in front of the teacher. There will be an individual written test that the teacher will validate to verify if the student understands the concepts on the syllabus related to the Course Project delivered.

55
Others 30 points (out of 100):
The tasks delivered by the students will be evaluated. The student must submit all the tasks indicated as mandatory that will be evaluated by the teachers.
To pass the course, at least 50% of the mandatory tasks have to be correctly delivered.


15 points (out of 100):
Participation in the course as a function of the intervention and student attitude in lectures, delivery of voluntary activities, help in the review of materials and their interaction with the teacher. The delivery of voluntary tasks will be specially taken into account.
45
 
Other comments and second call

Delayed deliveries will be subject of a penalty in the mark.

Students who do not pass the continuous assessment, could deliver what they lack in the period of the first ordinary call.

Optionally, students could also pass the course in the second call, taking a theoretical and practical exam that will cover all the materials taught during the course.

IN THE CASE ONLINE TEACHING IS NECESSARY:

To pass in the second call, it will be compulsory to present the Course Project.

At least 50% of the compulsory activities must be handed in and passed to pass the course.

In the second call, in addition to the project, there will be an oral exam for the students, via videoconference.


Sources of information
Access to Recommended Bibliography in the Catalog ULE

Basic
E.R. Davis. Computer Vision: Principles, Algorithms, Applications, Learning. Academic Press. 2017.
Francois Chollet. Deep Learning with Python. Manning. 2018 (2021 edition in preparation: https://www.manning.com/books/deep-learning-with-python-second- edition#toc)

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