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# MOAX

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Course: Methods of Image Analysis

Department/Abbreviation: KEF/MOAX

Year: 2021

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

Annotation: Introduction, relation of digital image processing to other related disciplines, overview, types and classification of digital image processing.

Course review:
1. Relationship of digital image processing to related disciplines. Overview, Types and distribution 2. The image sensor types, features , camera interface (analog, digital) , transmission image, image formats. Digital analyzer NIS-Elements. 3. Digital image-standard (NIS-Elements). Image Digitization-Sampling and quantum - Shannon s theorem, the image matrix, image files, formats the image data. Image capture, archiving, analysis, editing scanned images NIS- Elements. 4. Image Processing - adjacency, area, distance, route, contiguous zone, the inner and outer boundaries of the area, surface area and perimeter. Moments and center of gravity field recovery histogramu for image edge detection in images, methods for detecting and highlighting edges, examples NIS -Elements 5th Descriptive statistics - image histogram , cum . Histogram , the basic operation of the image matrix , brightness correction , transformation of the luminance scale , histogram equalization , logical ope - ration with paintings , blending images , filters , convolution matrix. Demonstration using NIS -Elements , Image Processing Toolbox. 6. Mathematical morphology , principles and applications for image pre -processing - noise reduction, and thinning skeletizace on the principle of mathematical morphology . Point set, structural element , dilation and erosion , opening and closing operations . Demo NIS -Elements , Video and Image Processing Blockset 7. NIS-Elements. Refractive index measurement, visualization , holograms, rekonstrkce image holo - gram of a series of images , assigning colors using interference theory . 8. The spectral 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. NIS -Elements - video analysis, spectroscopic data processing , Video and Image Processing Blockset . 9th Recognition methods . a) description of the image representation and description of boundaries Freeman chain code sections of lines , differential chain code and physical analysis of the geometric shape of the object ( NIS -Elements ) , analysis of geometric shapes (area , center of gravity , main, central moments , exam - ples measuring length, area and angles using different measurement methods using NIS- Elements. b ) Feature Recognition. Flags description of planar objects - general prin - py , image moments , correlation, recognition of 2 - D objects . Discriminant function, Criterion minimum distance, minimum errors , parametric estimation methods, cluster analysis . c ) Structural methods , choice of primitives , a description of formal languages ??, grammars , automata, syntactic analysis. d ) Neural Networks. Using neural networks for image classifier . Applica - tions , practical demonstration .