- Department of Cybernetics
First, the subject teaches how to process two-dimensional image as a signal without interpretation. Image acquisition, linear and nonlinear preprocessing methods and image compression will be studied. Second, image segmentation and registration methods will be taught. Studied topics will be practised on practical examples in order to obtain also practical skills.
It is expected that the student is familiar with calculus, linear algebra, probability and statistics to the depth taught at FEL CVUT.
- Syllabus of lectures:
1. Digital image processing vs. computer vision. Objects in images. Digital image. Distance transform. Brightness histogram.
2. Physical foundation of images. Image acquisition from geometric and radiometric point of view.
3. Processing in the spatial domain. Convolution. Correlation. Noise filtration. Linear and nonlinear methods.
4. Fourier transform. Derivation of the sampling theorem. Frequency filtration of images. Image restauration.
5. Brightness and geometric transformations, interpolation. Registration I.
6. Edge detection. Multiscale image processing.
7. Color images and processing of color images.
8. Segmentation I.
9. Segmentation II.
10. Registration II.
11. Image compression.
12. Mathematical morphology.
- 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.D.: Image processing, analysis and machine vision. 3. vydání, Thomson Learning, Toronto, Canada, 2007.
2. Svoboda, T., Kybic, J., Hlaváč, V.: Image processing, analysis and
machine vision. The MATLAB companion, Thomson Learning, Toronto, Canada, 2007.
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans: