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Mathematics for Technical Visualization (MATLD0131), 5 op
Basic information
| Course name: | Mathematics for Technical Visualization Mathematics for Technical Visualization |
| Course Winha code: | MATLD0131 |
| Kurre acronym: | MathTV |
| Credits: | 5 |
| Type and level of course: | Basic studies |
| Year of study, semester or study period: | 4.year |
| Implementation: | Autumn semester, 1.period, 2.period |
| Semester: | 0607 |
| Language of tuition: | English |
| Teacher: | Jaakko Pitkänen |
| Final assessment: | Grading scale (0-5) |
Descriptions
Prerequisites
Engineering Mathematics and Mathematics for Multimedia
Course contents (core content level)
Applied statistics in image processing: distributions, conditional probabilities, central limit theorem. Basics of information theory, redundancy, compression ratio. Mathematics of colour, colour spaces and colour processing. Fourier analysis of signals esp. digital images: frequency domain processing; sampling principles; amplitude and phase spectrum of a signal. Filtering in frequency domain; image enhancement in frequency domain: e.g. removing noise from a digital image.
Course contents (additional)
Boosting masks; Laplacian of a signal in edge enhancement. Bayer filter pattern of a colour image. Basics of data compression methods: reducing redundancy of data.
Core content level learning outcomes (knowledge and understanding)
After completing the course the student will know different ways to represent the results of technical activity visually. Will be able to tell data and information apart. Will understand basic concepts in statistics, information theory and in colour processing. Will know what kind of information is related to a signal in frequency domain. Will know some principles how to extract information from data.
Core content level learning outcomes (skills)
After completing the course the student will be able to enhance images in frequency domain by a computer. Will be able to carry out colour image processing by a computer.
Recommended reading
Teaching and learning strategies
Teaching methods and student workload
Lectures
Individual research, reading
Exam
Project
Laboratory assignments
Assessment weighting and grading
Two examinations with approval (at least 40% of the maximum), laboratory exercises and a final project.