Syllabus for Computer-Assisted Image Analysis I
Datoriserad bildanalys I
- 5 credits
- Course code: 1TD396
- Education cycle: Second cycle
Main field(s) of study and in-depth level:
Computer Science A1N,
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Established: 2010-03-18
- Established by:
- Revised: 2021-10-18
- Revised by: The Faculty Board of Science and Technology
- Applies from: Autumn 2022
120 credits including 30 credits in mathematics and 10 credits in computer science, including Scientific Computing I/Introduction to Scientific Computing and Computer Programming I, Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Responsible department: Department of Information Technology
On completion of the course, the student should be able to:
- explain fundamental notions on computerised image analysis, such as digitizing, image enhancement, segmentation and classification of features;
- use methods for image compression, distance computation, frequency analysis, etc.
- use software (MATLAB) for implementing algorithms for solving image analysis problems;
- analyse and outline the steps necessary to solve a realistic image analysis problem;
- give examples of applications in research and industry where image analysis is used.
Representation of images in computers. Image types. Sampling. Image coding and compression.Image processing and image restoration. Point operators. Fundamentals of frequency analysis. Histogram operations. Neighbourhood operators. Mathematical morphology. Segmentation. Shape analysis and feature extraction. Classification and decision theory. Examples of applications from research and industry.
Lectures, laboratory work, compulsory assignments.
Written examination at the end of the course and approved assignments.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.
- Latest syllabus (applies from Autumn 2022)
- Previous syllabus (applies from Spring 2019)
- Previous syllabus (applies from Autumn 2010)
The reading list is missing. For further information, please contact the responsible department.