Lecturer(s)


Bartl Eduard, RNDr. Ph.D.

Trnečková Markéta, Mgr.

Krupka Michal, doc. RNDr. Ph.D.

Procházka Pavel, Mgr.

Course content

1. Image representation. Sampling and quantization. Fourier transform. Shannon sampling theorem. Alias, antialiasing. 2. Human illumination and color perception. Color models. 3. Raster image representation. Image compression. Image formats. 4. Image enhancement in the spatial domain. Lookup table. Linear, logarithmic and exponential transformation, gamma correction. Thresholding, adaptive thresholding. Histogram processing. Histogram equalization. 5. Algorithms for drawing straight lines and circles. DDA algorithm. Bresenham algorithm. 6. Areafilling algorithms. Scanline filling algorithm. Seed filling algorithm. 7. Clipping algorithms. CohenSutherland algorithms. CyrusBeck algorithms. 8. Algorithms for drawing curves.

Learning activities and teaching methods

Lecture, Monologic Lecture(Interpretation, Training), Demonstration

Learning outcomes

The students become familiar with basic concepts of computer graphics.
2. Comprehension. Understand basic concepts of computer graphics.

Prerequisites

unspecified

Assessment methods and criteria

Oral exam, Written exam
Active participation in class. Completion of assigned homeworks. Passing the oral (or written) exam.

Recommended literature


R. C. Gonzalez, R. E. Woods. (2002). Digital Image Processing. Pearson Prentice Hall, New Jersey.

W. Burger, M. J. Burge. (2008). Digital Image Processing: An Algorithmic Introduction Using Java.

W. K. Pratt. (2001). Digital image processing, Third edition. WilleyInterscience, New York.

Žára, J., Beneš, B., Sochor, J., Felkel, P. (2004). Moderní počítačová grafika, 2. vyd. Brno, Computer Press.
