Syllabus for Computer-Assisted Image Analysis I

Learning outcomes

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.

Content

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.

Instruction

Lectures, laboratory work, compulsory assignments.

Assessment

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.