Code: | M4094 | Acronym: | M4094 | Level: | 400 |
Keywords | |
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Classification | Keyword |
OFICIAL | Mathematics |
Active? | Yes |
Responsible unit: | Department of Mathematics |
Course/CS Responsible: | Master in Bioinformatics and Computational Biology |
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 | 1 | The study plan since 2018 | 1 | - | 6 | 42 | 162 |
M:DS | 1 | Official Study Plan since 2018_M:DS | 2 | - | 6 | 42 | 162 |
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 orientation focus on the understanding of concepts and methods, and its effective use in synthetic and experimental data analysis. The course makes an extensive use of advance computational tools (MATLAB).
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 (Matlab - Image processing Toolbox).
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.
1. Basic concepts of Digital Image Processing. Application examples.
2. Image Processing in Matlab.
3. Point operations, spatial filtering, noise removal.
4. Color representation models.
5. Image segmentation.
6. Morphological operators.
7. Multi-spectral image classification.
8. Geometric correction and image alignment.
9. Image processing in the frequency domain.
10. Representation and object recognition.
11. Transforms (Hough, Radon, etc.)
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 (Matlab - Image processing Toolbox).
designation | Weight (%) |
---|---|
Trabalho laboratorial | 50,00 |
Trabalho prático ou de projeto | 50,00 |
Total: | 100,00 |
designation | Time (hours) |
---|---|
Elaboração de projeto | 40,00 |
Estudo autónomo | 21,00 |
Frequência das aulas | 56,00 |
Trabalho escrito | 15,00 |
Trabalho laboratorial | 30,00 |
Total: | 162,00 |
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 course final mark will be based on the practical assignment (50%) and the individual project (50%), with both componentes having to reach a minimum level of 40% (8 marks on a 0-20 scale).
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The classification component based on the fixed practical assignments is not subject to mark improvement.
Course juri:
Prof. André Marçal
Prof. Teresa Mendonça