Image Processing and Analysis
Code  Completion  Credits  Range  Language 

17AMBZAO  KZ  2  1P+1C  English 
 Lecturer:
 Tutor:
 Supervisor:
 Department of Biomedical Informatics
 Synopsis:

Digital image processing × image analysis × computer vision. Interpretation, its significance for images. Objects in images. Distance transform (DT). Brightness histogram. Image acquisition from the geometric and radiometric point of view. Fourier transform . Sampling theorem. Filtering in frequency domain. PCA. Brightness scale transformation, geometric transformations, interpolation. Image registration. Image processing in spatial domain. Convolution, correlation. Noise filtering. Edge detection. Linear and nonlinear methods. Mathematical morphology. Image compression. Color images. Texture. Segmentation of objects in images. Objects description in images and their recognition.
 Requirements:

It is possible to obtain 100 points in total. Fulfillment of the exercises during the practices corresponds to maximum 40 points. The written exam test assures 30 points maximally. Classification <50 F, 5059 E, 6069 D, 7079 C, 8089 B, 90100 A
 Syllabus of lectures:

1.Digital image processing × image analysis × computer vision. Interpretation, its significance for images. Objects in images.
2.Distance transform (DT). Brightness histogram. Image acquisition from the geometric and radiometric point of view.
3.Fourier transform. Sampling theorem.
4.Filtering in frequency domain.
5.PCA.
6.Brightness scale transformation.
7.Geometric transformations, interpolation.
8.Image registration.
9.Image processing in spatial domain. Convolution, correlation.
10.Noise filtering. Edge detection. Linear and nonlinear methods.
11.Mathematical morphology.
12.Image compression. Color images. Texture.
13.Segmentation of objects in images.
14.Objects description in images and their recognition.
 Syllabus of tutorials:

1.Grayscale mathematical morphology, dilation, erosion
2.Top Hat transform, distance transform.
3.Fourier transform.
4.Filtering in frequency domain.
5.Principal Component Analysis (PCA).
6.Brightness scale transformation.
7.Geometric transformations, interpolation.
8.Image registration.
9.Image processing in spatial domain. Convolution, correlation.
10.Noise filtering. Edge detection. Linear and nonlinear methods.
11.Image compression. Color images.
12.Huffman coding, Discrete cosine transform (DCT).
13.Segmentation of objects in images.
14.Summary of subject topics.
 Study Objective:

The goal of the subject is to introduce the basic principles of image processing and analysis. We link to the student knowledge from the signal theory.
 Study materials:

[1] Šonka M., Hlaváč V., Boyle R.: Image, processing, analysis and machine vision, Cengage Learning;, Canada, 4th edition, 2014, 912 pages, ISBN13: 9781133593607.
 Note:
 Further information:
 No timetable has been prepared for this course
 The course is a part of the following study plans: