Lessons and laboratory works are devoted to basic principles of acquisition, storage and processing of digital image data. Methods which can be used in engineering image data processing are emphasizedin in addition to general techniques. Lessons represent and operate with terms like Colour Processing, Image adjustment, 2D Discrete Function, Sampling, Compression, , Image Analysis in the Image and Frequency Area, Reconstruction, Geometric Transformations, Image Registration, Objects Detection, Segmentation and Classification. Methods of discrete mathematics, statistics, Fourier transform, mathematical morphology and others constitute basic tools of image processing. Practical projects are solved in the Matlab system including its Image Processing Toolbox. Lessons and training are held in a computer laboratory during a summer term in the load 1-0-3. Lessons are sealed with classified examination based on individual students projects elaborated during the term.

o Rafael C. Gonzales: Digital Image Processing, 2nd ed., Prentice Hall, New Jersey, 2002

o Bernard Jahne: Image Processing for scientific and Technical Applications, CRC Press, 2004

1. Introduction � basic terms. Connected areas. Image representation methods.

2. Basic colour models and their conversions.

3. Histogram and its use.

4. Gamma correction.

5. Colour palettes.

6. Dithering and halftoning.

7. Binary images use. Working with a mask.

8. Alpha blending.

9. Image as 2D discrete function. Sampling.

10.2D discrete convolution. 2D DFT. Image analysis.

11.Filters and their use - noise reduction, edge detection. Sharpening.

12.Warping and morphing. Geometric image transformations.

13.DCT and JPEG format

14.Image compression, image file formats