Computer Vision

Code:

MECD09

Acronym:

VC

Keywords
Classification	Keyword
CNAEF	Informatics Sciences

Instance: 2024/2025 - 1S

Active?	Yes
Responsible unit:	Department of Electrical and Computer Engineering
Course/CS Responsible:	Master in Data Science and Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MECD	21	Syllabus	2	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

Computer vision focuses on extracting "useful information" from images and videos. Examples of "useful information" include, for example, 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. This course is an introduction to basic concepts and methods in computer vision. 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 computer vision.

Learning outcomes and competences

Teaching and learning methods aim the knowledge of the contents referred to in the syllabus, reaching the targeted goals and competencies.

The diversity of proposed methodologies aims at enhancing the skills and competencies established, seeking to evidence different levels of analysis, fostering the integration of knowledge. The proposed methods and strategies aim to develop students' knowledge, understanding and skills in computer vision techniques.

The generic skills of teamwork, organization, etc. will be worked on in the group project.

Likewise, the ability to develop computer vision according to existing needs and to apply the most appropriate technological tools, to know, apply and evaluate computer vision will be worked out in the weekly exercises and group project.

The specific skills in computer vision techniques will be worked during the semester in theoretical-practical classes.

 

Working method

Presencial

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

Programming skills.

Program

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

    time filtering

    frequency filtering

    feature extraction

    segmentation

Recognition

    Connected components labeling

    Feature measurement

    Object representation

    feature selection

    description using local invariant features

    learning systems

    data driven representations

Multiview geometry

Motion and tracking

    motion estimation

    tracking using linear models        

Case studies

Mandatory literature

Richard Szeliski; Computer vision. ISBN: 978-1-84882-935-0 (https://catalogo.up.pt:443/F/?func=direct&doc_number=000556119&local_base=UPB01)
David A. Forsyth; Computer vision. ISBN: 0-13-085198-1

Comments from the literature

Prince, S. J. (2012). Computer vision: models, learning, and inference. Cambridge University Press.

 Forsyth, D. A., & Ponce, J. (2003). A modern approach. Computer vision: a modern approach, 88-101.

Haralick, R. M., & Shapiro, L. G. (1992). Computer and robot vision. Addison-wesley.

 (vol.1)

Shah, M. (1997). Fundamentals of computer vision. University of Central Florida.

Szeliski, R. (2010). Computer vision: algorithms and applications. Springer Science & Business Media.

Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library. " O'Reilly Media, Inc.".

Artigos recentes das conferências e revista de referência na área: CVPR, ICCV, ECCV, IEEE TIP, PR, PRL, International Journal of Computer Vision.

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.

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.

The final exam is worth 50% of the final grade. The projects account for the remaining 50%.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Participação presencial	10,00
Apresentação/discussão de um trabalho científico	40,00
Teste	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Apresentação/discussão de um trabalho científico	2,00
Elaboração de projeto	45,00
Estudo autónomo	35,00
Frequência das aulas	42,00
Total:	124,00

Eligibility for exams

-- University of Porto regulations.
--  Completion of the practical work.
-- Minimum grade of 8.00 (eight).

Calculation formula of final grade

CF (100%) = 40%Project_1 + 50% Project_2 + 10% Professor Opinion

IF (TP>=8.00) THEN
       CF =TP
ELSE
       CF=RFC /* reprovado por falta de componente */
END_IF


Final grade = 50% CF + 50% Test

Examinations or Special Assignments

Students who have not carried out the practical component during the academic period will have to carry out an equivalent work.

Special assessment (TE, DA, ...)

UP regulations.

Classification improvement

CF is not possible to be enhanced in the same year.