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Computer Vision

Code: EIC0104     Acronym: VCOM

Classification Keyword
OFICIAL Interaction and Multimedia

Instance: 2014/2015 - 1S Ícone do Moodle

Active? Yes
E-learning page: https://moodle.fe.up.pt/
Responsible unit: Department of Informatics Engineering
Course/CS Responsible: Master in Informatics and Computing Engineering

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
MIEIC 22 Syllabus since 2009/2010 5 - 6 56 162

Teaching - Hours

Recitations: 3,00
Type Teacher Classes Hour
Recitations Totals 1 3,00
Jorge Alves da Silva 1,50
Luís Filipe Pinto de Almeida Teixeira 1,50

Teaching language

Suitable for English-speaking students


Computer vision is a subfield of computer science that focuses on extracting "useful information" from images and videos. The goal of computer vision is to "discover from images what is present in the world, where things are located, what actions are taking place" (Marr, 1982). Examples of "useful information" include, for example, recovering the 3D geometry of objects in an image, detection and identification of human faces and gestures, and tracking moving people or vehicles in a video sequence. Computer vision algorithms have found a wide range of applications in the industrial, military and medical fields, as well as in the ever-growing entertainment field.

This course is an introduction to basic concepts and methods in computer vision. It is mainly suited for MIEIC students who are interested in following research in this area. The covered topics include: image formation, basic image processing and analysis methods, as well as more advanced methods like 3D scene reconstruction, motion analysis, tracking, and object recognition. 

Learning outcomes and competences

Upon completion of this course, students will:

    • understand and be able to explain the basic concepts of computer vision and the fundamental algorithms for manipulation of images and video sequences;


    • have knowledge of existing methods for visual data analysis and be able to apply them in practical situations;


    • acquire skills to use a library, like OpenCV, that implements some of the analyzed algorithms, and to implement novel algorithms described in the literature;


    • be able to analyze and understand selected scientific papers in image processing and analysis, and computer vision.


Working method


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

Approval in the Programming, Algorithms and Data Stuctures, and Algebra courses (or equivalent) is advisable.


Introduction to Computer Vision

Image acquisition

  • intensity images (2D) and distance/position images (3D)
  • geometric and radiometric model of a camera

Processing and analysis of intensity images

  • filtering
  • feature extraction
  • segmentation

Geometric calibration of a camera

  • calibration methods


  • epipolar geometry
  • point correspondence

Motion and tracking

  • motion estimation
  • tracking using linear models


  • feature selection
  • description using local invariant features
  • learning systems

Case studies

Mandatory literature

ULL; Introductory techniques for 3 D computer vision
Gary Bradski, Adrian Kaehler; Learning OpenCV. ISBN: 978-0-596-51613-0

Complementary Bibliography

David A. Forsyth, Jean Ponce; Computer vision. ISBN: 0-13-085198-1
Robert M. Haralick, Linda G. Shapiro; Computer and Robot Vision. ISBN: 0 201 10877 1(vol.1)
Mubarak Shah; Fundamentals of computer vision
Richard Szeliski; Computer vision. ISBN: 978-1-84882-935-0

Teaching methods and learning activities

• Lectures: Presentation and discussion of the course topics, and resolution of exercises.

• Practical assignments: Development of projects where the studied computer vision methods must be applied.



Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation Weight (%)
Exame 50,00
Trabalho laboratorial 50,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Estudo autónomo 70,00
Frequência das aulas 42,00
Trabalho laboratorial 50,00
Total: 162,00

Eligibility for exams

Do not exceed the absence limit and obtain a minimum of 40% in the distributed evaluation classification.

Calculation formula of final grade

Distributed evaluation (DEv) with final exam (ExEv).

DEv - Two projects will be developed during the semester; these projects must be developed both during classes and at home. For the last project a report will be written and an oral presentation will be required. • DEv and ExEv are specified in a 0 to 20 scale.

Projects weight in DEv:

  • Project 1 - 40%
  • Project 2 -60%

• Final Classification = DEv * 0.5 + ExEv * 0.5

Oral examination: whenever needed, according to a decision of the teaching team, students may be submitted to an oral exam. In this situation the final classification will be given by the average of the classification calculated with the previous formula and the classification of the oral exam.

Observations: 1- A minimum of 40% on the ExEv evaluation component is required to be approved in the course. 2- If the teaching team decides not to propose one of the projects, its weight will be redistributed to the other projects.

Examinations or Special Assignments

See DEv, in Evaluation components.

Special assessment (TE, DA, ...)

Students with a special status will be assessed in the same way as ordinary students. They have to do all the assignments and deliver them on the scheduled dates.

Classification improvement

Students can only improve the mark of the distributed evaluation component in the following year. Students can improve the mark of the written exam at the corresponding seasons (according to the rules).

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