Main field(s) of study and in-depth level:
Computer Science A1N,
Fail (U), 3, 4, 5.
The Faculty Board of Science and Technology
120 credits including 30 credits in mathematics and 15 credits in computer science, including Scientific Computing I and Computer Programming II or the equivalent. Mathematical Statistics is recommended.
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.