Image Processing and Analysis

Code:

M4094

Acronym:

M4094

Level:

400

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2022/2023 - 2S

Active?	Yes
Responsible unit:	Department of Mathematics
Course/CS Responsible:	Master in Bioinformatics and Computational Biology

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	0	The study plan since 2018	1	-	6	42	162
M:DS	6	Official Study Plan since 2018_M:DS	2	-	6	42	162
M:ECAD	0	Study plan since 2021/2022	2	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

The course presents the main concepts and techniques of digital image processing and analysis. The main goal is that in the end of the course the students will be able to plan and implement algorithms for information extraction from images.

The course focus on the understanding of concepts and methods, and its effective use in synthetic and experimental data analysis. The course makes an intensive use of advance computational tools (MATLAB).

Learning outcomes and competences

The classes are all of type theoretical-practical (TP). Some classes are used to present concepts and methods, illustrated with a variety of examples, whereas others are used to for the resolution of problems and projects, with a strong computational component in a laboratorial environment using MATLAB.

The practical assignment contain a variety of proposed exercises, using both synthetic and experimental data. These exercises cover the range of topics and methods presented in the course, and have various levels of difficulty. The level of autonomy expected from the student is also varied (increasing), which should lead to the achievement of the course objectives in the end of the semester.

Working method

Presencial

Program

1. Basic concepts of Digital Image Processing. Application examples. Image Processing in MATLAB.
2. Point operations
3. Spatial filtering, noise reduction.
4. Color image processing.
5. Image segmentation.
6. Morphological operators.
7. Geometric correction and image alignment.
8. Representation and object recognition.
9. Multi-spectral image classification.
10. Image processing in the frequency domain.
11. Transforms (Hough, Radon, etc.)

Mandatory literature

000091303. ISBN: 0-13-008519-7
000091878. ISBN: 978-0-13-168728-8
000093668. ISBN: 0-495-08252-X

Complementary Bibliography

Richard Szeliski; Computer Vision: Algorithms and Applications, Springer, 2010 (available at http://szeliski.org/Book/)

Teaching methods and learning activities

The classes are all of type TP (Theory / Practical). Some classes are used to present concepts and methods, illustrated with a variety of examples, whereas others are used to for the resolution of problems and small projects, with a strong computational component in a laboratorial environment using MATLAB.

Software

MATLAB

keywords

Technological sciences > Technology > Computer technology > Image processing
Physical sciences > Mathematics > Applied mathematics

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Trabalho prático ou de projeto	50,00
Apresentação/discussão de um trabalho científico	20,00
Trabalho escrito	30,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Elaboração de projeto	35,00
Estudo autónomo	21,00
Frequência das aulas	56,00
Trabalho escrito	15,00
Trabalho laboratorial	30,00
Apresentação/discussão de um trabalho científico	5,00
Total:	162,00

Eligibility for exams

The practical assignments are compulsory, with corresponding submission of programs / scripts / reports required within the fixed schedules, and with a minimum level of 40% (8 marks on a 0-20 scale).
The students might have to answer questions regarding the practical assignments made, during the lectures or in an oral exam.

Calculation formula of final grade

The course final mark will be based on the practical assignment (50%) and on an original mini-project (50%), with both components having to reach a minimum level of 40% (8 marks on a 0-20 scale).

Examinations or Special Assignments

n.a.

 

Internship work/project

n.a.

Special assessment (TE, DA, ...)

n.a.

 

Classification improvement

The practical assignments are not eligible for classification improvement.

Observations

Course juri:

Prof. André Marçal

Prof. Ana Paula Rocha