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Computer Vision

Code: CC4016     Acronym: CC4016     Level: 400

Classification Keyword
OFICIAL Computer Science

Instance: 2020/2021 - 1S Ícone do Moodle Ícone  do Teams

Active? Yes
Web Page: http://www.dcc.fc.up.pt/~mcoimbra
Responsible unit: Department of Computer Science
Course/CS Responsible: Master's degree in Computer Science

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
E:BBC 0 PE_Bioinformatics and Computational Biology 1 - 6 42 162
M:BBC 7 The study plan since 2018 2 - 6 42 162
M:CC 21 Study plan since 2014/2015 1 - 6 42 162
M:DS 12 Official Study Plan since 2018_M:DS 1 - 6 42 162
MI:ERS 24 Plano Oficial desde ano letivo 2014 4 - 6 42 162

Teaching Staff - Responsibilities

Teacher Responsibility
Miguel Tavares Coimbra

Teaching - Hours

Theoretical and practical : 3,00
Type Teacher Classes Hour
Theoretical and practical Totals 2 6,00
Miguel Tavares Coimbra 4,50

Teaching language

Suitable for English-speaking students


This module will present generic computer vision topics to the students, namely: image capturing technology, core image and video processing algorithms, basic pattern recognition algorithms, computer vision application fields.

Learning outcomes and competences

At the end of this module, students are expected to:
1. Understand the basic concepts of the human visual system.
2. Get acquainted with image capturing technologies.
3. Learn basic image processing methods.
4. Learn basic video processing methods.
5. Learn basic pattern recognition methods.
6. Discover today’s most popular computer vision application fields.

Working method


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



- Digital image: The human visual system, image formation, digital representation, colour, noise.

- Image processing: Single point manipulation, spatial filters, geometric structure extraction, segmentation.

- Vídeo processing: Optical flow, video compression.

- Pattern recognition: Introduction, knowledge representation, statistical pattern recognition, machine learning.

- Application fields.

Mandatory literature

Gonzalez Rafael C.; Digital image processing. ISBN: 0-13-008519-7

Teaching methods and learning activities

Theoretical-Practical Lectures (TP): Scientific content presentation. Discussion of illustrative examples. Problem solving. Questions and coursework support.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation Weight (%)
Exame 100,00
Total: 100,00

Amount of time allocated to each course unit

designation Time (hours)
Estudo autónomo 32,00
Frequência das aulas 42,00
Trabalho de investigação 88,00
Total: 162,00

Eligibility for exams

Achievement of the minimum grades for all evaluation components

Calculation formula of final grade

Practical Evaluation (PE):

- A course project will be proposed, which must be submitted at the end of the semester.

- The project will be presented to the lecturer at the end of the semester.

Minimum grade: 40% (8 values)


Written Exam (WE)

- Students will have to answer an exam.

Minimum grade: 40% (8 values)


Final Grade (FG):

- The final grade is obtained by the following formula:
--- FG = 0.50 x WE + 0.50 x PE --- FG = WR

Minimum grade: 9.5 values


- Miguel Coimbra
- Francesco Renna
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