Image Data Processing
- Garant předmětu:
- Zoltán Szabó
- Zoltán Szabó
- Pavla Suchánková, Zoltán Szabó
- Department of Biomedical Informatics
The aim of the course is to provide basic knowledge about the principles of the digital image processing process (algorithms - implementation and realization). This goal also includes the issue of digitization and basic methods of image data analysis.
Basic knowledge of working in the Matlab software environment
- Syllabus of lectures:
1. Introduction - optics of the eye, Spectral sensitivity of the human eye, Luminance sensitivity of the eye, Contrast sensitivity of the eye, Spatial resolution of the eye, Temporal resolution of the eye, General scheme of the imaging process
2. Halftonning and Dithering, Histogram, Point Operations, Brightness Modification, Contrast Modification, Logarithmic Brightness Transformation, Gamma Correction, Thresholding, Adaptive Thresholding, Bit-Plane Slicing
3. Histogram equalization, Colors, High / True Color, Color model, RGB (A), additive model, CMY - subtractive model, RGB to grayscale conversion, HSV model, HLS model, XYZ chromatic diagram, (CIE) , Gamut
4. General scheme of imaging process, Filtration (selection or sampling) property of Dirac pulse, Linear imaging system, Convolution and correlation in continuous and discrete region
5. Edge finding, Edge, edge point, Category of edge detectors, Gradient image function, Discrete approximation of derivation, Sensitivity of derivative to noise, convolution masks, Laplacian image function, LoG operator, DoG,
6. 2D Fourier transform, Continuous signal sampling, Aliasing, Antialiasing filter, frequency domain filtering
7. Image compression, Redundancy and irrelevance, RLE - run length coding, Huffman coding, 2D DFT, JPEG, BOC vs. ROC
- Syllabus of tutorials:
1. Specifics of image information processing in Matlab - Image Processing Toolbox, possibilities of HW cooperation with Matlab in image processing, overview of functions, basic image operations, freely distributable tutorial MIPS
2. Calculation of basic image parameters (mean brightness, min. And max. Brightness, matrix size, number of gray levels, noise types, signal-to-noise ratio SNR, PSNR, root mean square deviation of two images, etc.), image formats in Matlab, experiment on the perception of brightness and contrast
3. Arithmetic and logical operations over the image, conversion characteristics (LUT) - types and implementations
4. Histogram, expansion and alignment of the histogram
5. 2D Fourier transform, 2D convolution (different types of masks), filtering (averaging, median, etc.)
6. Segmentation and geometric transformations
7. Compression of image data and calculation of selected image quality indicators
- Study Objective:
Understanding of image processing methods as a generalization of methods for signal processing. Gaining practical experience with digital image processing.
- Study materials:
 Rafael C.Gonzales, Paul Wintz: Digital Image Processing, 2002.
 Šonka M., Hlaváč V., Boyle R.: Image Processing, Analysis and Machine vision, 3rd edition, Thomson Learning, Toronto, Canada, 2007
 Svoboda T., Kybic J., Hlaváč V.: Image Processing, Analysis and Machine Vision - A MATLAB Companion. Thomson, Toronto, Canada, 1 edition, 2007.
- Time-table for winter semester 2023/2024:
Lab. umělé inteli. a bioinfor.
Wed Thu Fri
- Time-table for summer semester 2023/2024:
- Time-table is not available yet
- The course is a part of the following study plans:
- Biomedical Technology (compulsory elective course)