Introduction to Image Analysis
Course, Master's level, 1MD110
Autumn 2023 Autumn 2023, Uppsala, 33%, On-campus, Swedish Only available as part of a programme
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 28 August 2023–14 January 2024
- Language of instruction
- Swedish
- Entry requirements
-
120 credits including 30 credits mathematics and 30 credits computer science. Basic programming, statistics and probability theory, linear algebra, and calculus. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Application deadline
- 17 April 2023
- Application code
- UU-11620
Admitted or on the waiting list?
Autumn 2024 Autumn 2024, Uppsala, 33%, On-campus, English Only available as part of a programme
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 2 September 2024–19 January 2025
- Language of instruction
- English
- Entry requirements
-
120 credits including 30 credits mathematics and 30 credits computer science. Basic programming, statistics and probability theory, linear algebra, and calculus. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Application deadline
- 15 April 2024
- Application code
- UU-11620
Admitted or on the waiting list?
- Registration period
- 26 July 2024–9 September 2024
- Information on registration
About the course
Methodology for solving image analysis problems. An overview of the basic components included in a typical image analysis system. Representation of images in a computer. Image types. Colour Theory. Sampling and interpolation. Image encoding and compression. Image enhancement and image restoration. Basic frequency analysis. Histogram operations. Point and neighbourhood operations. Segmentation and edge detection in images. Image registration and motion analysis. Computer vision. Mathematical morphology, discrete geometry and combinatorial optimisation. Shape analysis and feature extraction. Classification and decision theory. Experimental design and evaluation. Examples of applications in research and industry. Opportunities and limitations of computerised image analysis.
This course cannot be included in the same degree as 1TD396 Computer Assisted Image Analysis I, 1TD398 Computer Assisted Image Analysis II, and 1MD160 Computer Assisted Image Analysis.
Reading list
No reading list found.