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Course: Digital Image Processing

Department/Abbreviation: KEF/DZO

Year: 2020

Guarantee: 'doc. Ing. Luděk Bartoněk, Ph.D.'

Annotation: In this course, the students become acquainted with usages of computers in measurements and analysis of image information acquired from one-dimensional and two-dimensional CCD sensors. The first part of the course is devoted to a study of relation of digital image processing to other related disciplines, to digitalization of analog signals and to description of typical CCD sensors. The second part of the course is then focused on processing of image matrix and its geometrical, statistical and spectral evaluations. The attention is also concentrated on methods of recognition (symptomatic and structural) and analysis of geometrical shapes in the image.

Course review:
1. Relationship of digital image processing to other related disciplines , overview , types, distribution. 2. Video signals. A/D - Tv signals, types of color formats, CCD detectors (1D-2D sto-ry ), scanner, grabber. 3.Digitální image. Image matrix, image files , image analyzer DIPS . 4. Processing image files. Descriptive statistics ? histogram , cum . Histogram The basic operation of the image matrix (Add , Subtract , Difference , Multiply , Divide , Lin . Combination, then ... If probabilistic image operations , logic operations with images (Lu - kaszewiczova logic) , blending images , filters , convolution matrix . 5.Spektrální image processing . Discrete basis functions , general disktrétní Fourier transform ( DFT), Fast Fourier Transform (FFT) to reduce the time , frequency , algorithm , program , inverse DFT. 2D FFT functions in the analysis of the image matrix. 6. Methods measurement and recognition of objects in the image. a) Analysis of the geometry and the physical shape of the object ( DIPS ) . Coordinate measuring . Measuring distances and angles. Geometric transformations of scale. Analysis of geometric shapes (area, center of gravity, moments (main , central), Legendre ellipse , elongation , dispersion , ex - tension , perimeter, shape factor , orientation . b) Feature Recognition . Discriminant function , the criterion of minimum distances of the minimum error parameter estimation method, the cluster analysis . Structural methods, choice of primitives , a description of formal languages ??, grammars , automata, syntactic analysis . c ) Neural Networks . Applications practical demonstration.