BE4M33DZO - Digital Image

This course presents an overview of basic methods for digital image processing. It deals with practical techniques that have an interesting theoretical basis but are not difficult to implement. Seemingly abstract concepts from mathematical analysis, probability theory, or optimization come to life through visually engaging applications. The course focuses on fundamental principles (signal sampling and reconstruction, monadic operations, histogram, Fourier transform, convolution, linear and non-linear filtering) and more advanced editing techniques, including image stitching, deformation, registration, and segmentation. Students will practice the selected topics through six implementation tasks, which will help them learn the theoretical knowledge from the lectures and use it to solve practical problems.

Students are expected to know topics from calculus, linear algebra, statistics and probability to the extent taught at CTU in Prague, FEE. Basic programming skills are also expected, especially in MATLAB.

Lecturer: prof. Ing. Daniel Sýkora, Ph.D. (CTU in Prague, FEE, Department of Computer Graphics and Interaction, Room: KN:E-422)

Place and Time: Wednesday 09:15am-10:45am, Lecture Hall KN:E-301

Extent: 2 hours weekly

Lab Assistants:

• Ing. Jan Čech, Ph.D. (CTU in Prague, FEE, Department of Cybernetics, Room: KN:G-2)
• Mgr. Ondřej Drbohlav, Ph.D. (CTU in Prague, FEE, Department of Cybernetics, Room: KN:G-2)
• Ing. Vojtěch Pánek (CTU in Prague, CIIRC, Room: JP:B-634a)
• prof. Ing. Daniel Sýkora, Ph.D. (CTU in Prague, FEE, Department of Computer Graphics and Interaction, Room: KN:E-422)

Place and Time: Tuesday 2:30pm-7:30pm, Lab Rooms KN:E-230 and KN:E-132

Extent: 2 hours weekly

In total, it is possible to obtain 60 points from all assignments. In order to pass labs, student have to obtain at least 30 points.

The exam has two parts, written and oral. In the written part, the student will work through two randomly selected questions covering the subject matter within 30 minutes:

• Monadic operations (simple modifications of pixel intensity, histogram equalization and mapping)
• Fourier transform (formulation, properties, sampling theorem)
• Convolution (formulation, properties, speed-up methods, applications)
• Image deblurring (formulation, solution using Wiener filter)
• Bilateral filter (formulation, speed-up methods, applications)
• Image editing in the gradient domain (formulation, solution, applications)
• Image deformation (1, 2, 3, 4-point)
• Image deformation (N-point, basic formulation, extensions)
• Image registration (translation, solution methods)
• Image registration (translation, rotation, and scaling using Fourier-Mellin)
• Image registration (N-point)
• Image segmentation (formulation, solution using maximum flow, applications)
• Image segmentation (formulation, solution using random walker, applications)

Written part is then followed by an oral examination where the examiner goes through the written preparation with the student and asks supplementary questions. The aim of these questions is to find out to what extent the student understands the material. Rather than encyclopedic knowledge, what is important is the depth of understanding of the topics discussed and the ability to implement them in practice. A maximum of 20 points can be obtained for each question. Thus, in total 40 points can be obtained for the exam. To pass the exam, a minimum of 20 points is required in total.

The final grade is determined by the sum of the points obtained from the labs (maximum 60) and from the exam (maximum 40):

• He J., Kim C.-S., Kuo C.-C. J.: Interactive Segmentation Techniques: Algorithms and Performance Evaluation, Springer, 2014.
• Radke R. J.: Computer Vision for Visual Effects, Cambridge University Press, 2012.
• Goshtasby A. A.: Image Registration: Principles, Tools and Methods, Springer, 2012.
• Paris S., Kornprobst P., Tumblin J., Durand F.: Bilateral Filtering: Theory and Applications, Now Publishers, 2009.
• Gonzalez R. C., Woods R. E.: Digital Image Processing (3rd Edition), Prentice Hall, 2008.
• Šonka M., Hlaváč V., Boyle R.: Image Processing, Analysis and Machine vision (3rd Edition), Thomson Learning, 2007.
• Pratt W.: Digital Image Processing (3rd Edition), John Wiley, 2004.