Digital image
Code  Completion  Credits  Range  Language 

BAB33DZO  Z,ZK  6  2p+2c  Czech 
 Lecturer:
 Tutor:
 Supervisor:
 Department of Cybernetics
 Synopsis:

The subject teaches how to represent the twodimensional image in a computer, how to process it and interpret it. The first part of the subject deals with the image as with the signal without interpretation. Image acquisition, linear and nonlinear preprocessing methods and image compression will be explicated. In the second part, image segmentation and registration methods will be taught. Studied topics will be practiced on practical examples in order to obtain also practical skills.
 Requirements:
 Syllabus of lectures:

1. Digital image processing vs. computer vision. Role of interpretation. Objects in images. Digital image. Concepts.
2. Physical foundation of images. Image acquisition from geometric and radiometric point of view.
3. Brightness and geometric transformations, interpolation.
4. Fourier transform. Derivation of the sampling theorem. Frequency filtration of images. Image restauration.
5. Processing in the spatial domain. Convolution. Correlation. Noise filtration. Homomorphic filtration.
6. Edge detection. Multiscale image processing. Canny detector.
7. Principal component analysis. Wavelets transformation.
8. Color images and processing of color images.
9. Image compression. Video compression.
10. Mathematical morphology.
11. Image segmentation  thresholding, kmeans, EM algorithm.
12. Image segmentation  mean shift, seek for the optimal graph cut.
13. Registration of images and of objects in images.
 Syllabus of tutorials:

1. MATLAB. Homework 1 assignment (image acquisition).
2. Constultations. Solving the homework.
3. Constultations. Solving the homework.
4. Constultations. Solving the homework.
5. Homework 1 handover. Homework 2 assignment (Fourier transformation).
6. Constultations. Solving the homework.
7. Constultations. Solving the homework.
8. Constultations. Solving the homework.
9. Homework 2 handover. Homework 3 assignment (image segmentation).
10. Constultations. Solving the homework.
11. Constultations. Solving the homework.
12. Consultations. Homework 3 handover.
13. Written test. Presentation of several best student homeworks.
 Study Objective:
 Study materials:

1. Šonka M., Hlaváč V., Boyle R.: Image Processing, Analysis and Machine vision, 4th edition, Thomson Learning, Toronto, Canada, 2015, 912p., ISBN10: 1133593607.
2. Svoboda, T., Kybic, J., Hlaváč, V.: Image processing, analysis and machine vision. The MATLAB companion, Thomson Learning, Toronto, Canada, 2007.
 Note:
 Further information:
 No timetable has been prepared for this course
 The course is a part of the following study plans:

 Medical electronics and bioinformatics (compulsory course in the program)