Computer Vision

Code:

CC4016

Acronym:

CC4016

Level:

400

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2021/2022 - 1S

Active?	Yes
Web Page:	http://www.dcc.fc.up.pt/~mcoimbra
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Master 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	1	PE_Bioinformatics and Computational Biology	1	-	6	42	162
M:BBC	6	The study plan since 2018	2	-	6	42	162
M:CC	26	Study plan since 2014/2015	1	-	6	42	162
M:DS	18	Official Study Plan since 2018_M:DS	1	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

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

Presencial

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

None

Program

- 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

Special assessment (TE, DA, ...)

Special evaluations can only use the Written Exam (WE) format.

Classification improvement

Only the written exam (WE) component can be improved, which is then combined with the Practical Evaluation (PE) component obtained previously.

Observations

Juri: 
- Miguel Coimbra
- Francesco Renna