Vision-Based Systems

Code:

EEC0100

Acronym:

SBVI

Keywords
Classification	Keyword
OFICIAL	Automation, Control & Manufacturing Syst.
OFICIAL	Basic Sciences for Electrotechnology

Instance: 2018/2019 - 1S

Active?	Yes
Web Page:	http://www.fe.up.pt/si/conteudos_adm.conteudos_list?pct_pag_id=1639&pct_parametros=p_ano_lectivo=2009/2010-y-p_cad_codigo=EEC0100-y-p_periodo=1S
Responsible unit:	Department of Electrical and Computer Engineering
Course/CS Responsible:	Master in Electrical and Computers Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIEEC	49	Syllabus	4	-	6	56	162

Teaching language

Suitable for English-speaking students

Objectives

The main objectives to attain are the acquisition of knowledge on automatic vision systems, not only on the elements that constitute their modular structure, but also on the technology used in their implementation.

Learning outcomes and competences

1. Acquisition of knowledge on automatic vision systems, not only on the elements that constitute their modular structure, but also on the technology used in their implementation (CDIO Syllabus 1.3, 1.4, 2.1, 2.3).
2. Demonstration of skills on the design, projection and implementation of automatic vision systems (CDIO Syllabus 4.2, 4.3, 4.4, 4.5).
3. Development of organisational skills, as well as autonomous work and bibliography research (CDIO Syllabus 2.4, 2.5, 3.3).
4. Demonstration of skills on teamwork (CDIO Syllabus 2.5, 3.1).
5. Development and demonstration of skills on written and oral communication (CDIO Syllabus 3.2)

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Knowledge of Signal Processing and software MatLab.

Program

1. Introduction. General structure of a vision-based system.
2. Acquisition and formation of digital images.
3. Preprocessing. Image operators and their application in image enhancement.
4. Mathematical morphology.
5. Image segmentation.
6. Image analysis.
7. Image recognition and classification.

Mandatory literature

Gonzalez, Rafael C; Digital image processing. ISBN: 0-20-118075-8
Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins; Digital image processing using Matlab. ISBN: 0-13-008519-7

Complementary Bibliography

Steger C., Ulrich M. Wiedemann C; Machine Vision Algorithms and Applications, Wiley, 2008. ISBN: 978-3-527-40734-7
Milan Sonka, Vaclav Hlavac, Roger Boyle; Image processing, analysis and machine vision. ISBN: 978-0-495-24428-7
E. R. Davies; Machine Vision: Theory, Algorithms, Practabilities, Morgam Kaufmann, 205. ISBN: 0-12-206093-8

Teaching methods and learning activities

Theoretical classes: presentation of themes, practical demonstration and case studies.

Theoretical-practical classes:
Problem solving, experimentation and testing using Matlab (Image Processing Toolbox), development of solutions in Matlab.

Practical assignment outside classes:
Students have to develop a project (3/4 students) on a subject proposed by the teachers. 
Students will also have to write a report and to orally present their project.

Software

MatLab - The Mathworks

keywords

Technological sciences > Engineering > Electrical engineering

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	70,00
Trabalho prático ou de projeto	30,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	40,00
Estudo autónomo	66,00
Frequência das aulas	56,00
Total:	162,00

Eligibility for exams

Assessment components:
- Practical assignment (3/4 students) and report;
- Oral presentation of the practical assignment;
- Final Exam.

Students have to do the practical assignment, as well as the oral presentation and report, to be admitted to exams.
The grade of the continuous assessment component will be based on professors’ opinion regarding students’ performance during the preparation of the project, as well as the quality of the report and presentation.
Students without a continuous assigment grade are not admitted to the exam. Students with a countinuous assignment grade obtained in a previous year should contact the teacher in order to be admitted to the final exam.


Practical assignment – CDIO Syllabus assessed: 1.3, 1.4, 2.1, 2.3, 2.4, 2.5, 3.1, 3.3, 4.2, 4.3, 4.4, 4.5.
Oral Presentation – CDIO Syllabus assessed: 3.1, 3.3, 4.2, 4.3, 4.4, 4.5.
Final Exam- CDIO Syllabus assessed: 1.3, 1.4, 2.1, 2.3, 4.2, 4.3, 4.4, 4.5.

Calculation formula of final grade

Final Mark will be based on the mark of the continuous assessment component (AD) and on the final exam (NE). The same formula apply for recurso season and classification improvement.

Final grade=0.3*AD + 0.7*NE

Examinations or Special Assignments

The above mentioned project.

Internship work/project

The theme of the group work (project) will be defined by the end of October.
The work report and the developed code must be submitted until December 14th.
The presentation of the work will be carried out during the periods of the classes of December 21st, with a calendar to be defined. Presentation slides must be submitted by the end of December 20th.

 

Special assessment (TE, DA, ...)

The only students with a special status are working-students. They have to do a practical assignment on the themes above mentioned. However, its length may be shorter.
Working-students can ask professors for a theme at the beginning of the semester.

Classification improvement

Students cannot improve the grade of the continuous assessment component. However, students can improve the gradeof the exam.