The master thesis presents an overview of face detection task in color, static images. Face detection term is posed in the context of various branches. Main concepts of face detection and also their relationships are described. Individual approaches are divided into groups and then define in turn. In the thesis is in detail described algorithm AdaBoost, which is selected on the basis of its properties. Especially speed of computation and good detection results are key features. In the scope of this work Viola-Jones detector was implemented. This detector was trained with face pictures from public accessible database. Combination of Viola-Jones detector with simple color detector is described. In the thesis is also presented experiment approach to facial features detection.

The project consists of two distinct levels i.e. separation level and diagnostic level. At the separation level, statistical models of gaussians and color are separately used to classify each pixel as belonging to backgroung or foreground. Adopted method is mixture of gaussians.A mixture of gaussians model is suitable here because the results of the picture tests will not depend on the lens opening, but rather on the colors in the backgroung. A mixture of gaussians model for return data seems reasonable. The achieved results the used method on the real sequences are presented in the thesis. Diagnostic level is identified human body on the scene. Adopted method is ASM(Active Shape Models) with PCA(Principal Component Analysis). ASM are statistical models of the shape of human bodies which iteratively deform to fit to an example of the object in a new image.

This master's thesis deal with problems of classification objects on the basis of atributes get from images. This thesis pertain to a branch of computer vision. Describe possible instruments of classification (e.g. neural networks, decision tree, etc.). Essential part is description objects by means of atributes. They are imputs to classifier. Practical part of this thesis deal with classification of object collection, which can be usually found at home (e.g. scissors, compact disc, sticky, etc.). Analyzed image is preprocessed , segmented by thresholding in HSV color map. Then defects caused by a segmentation are reconstructed by morfological operations. After are determined atribute values, which are imputs to classifier. Classifier has form of decision tree.

This master thesis deals with detection of microsleep on the basis of the changes in power spectrum of EEG signal. The results of time-frequency analysis are input values for the classifikation. Proposed classification method uses fuzzy logic. Four classifiers were designed, which are based on a fuzzy inference systems, that are differ in rule base. The results of fuzzy clustering are used for the design of rule premises membership functions. The two classifiers microsleep detection use only alpha band of the EEG signal’s spectrogram then allows the detection of the relaxation state of a person. Unlike to first and second classifiers, the third classifier is supplemented with rules for the delta band, which makes it possible to distinguish the 3 states: vigilance, relaxation and somnolence. The fourth classifier inference system includes the rules for the whole spectrum band. The method was implemented by computer. The program with a graphical user interface was created.

This diploma evaluates methods for verification of key characteristics of a product using digital image processing techniques. At first, reasons why this work has been done are described followed by a list of all methods that were used in this diploma such as Hough Circle Transform and Flood Fill (Seed Fill) algorithm. Also, a new approach how to compensate non regularly illuminated scene, which is based on surface modeling with Bézier Surfaces, was developed. Moreover, the algorithm was implemented in the C++ programming language and some of the parts were also simulated using the MATLAB environment. The algorithm was evaluated based on the percentage level of recognition of the required parameters. Efficiency of the implementation is also important for the author.

This document describes the three methods for the detection and classification of paralinguistic expressions such as laughing and crying from usual speech by analysis of the audio signal. The database of records was originally designed for this purpose. When analyzing everyday dialogs, music might be included, so the database was extended by four new classes as speech, music, singing with music and usual speech with background music. Feature extraction, feature reduction and classification are common steps in recognizing for all three methods. Difference of the methods is given by classification process in detail. One classification of all six classes at once is proposed in the first method called straight approach. In the second method called decision tree oriented approach we are using five intuitive sub classifiers in the tree structure and the final method uses for classification emotion coupling approach. The best features were reduced by feature evaluation using F-ratio and GMM classifiers were used for the each classification part.

The aim of master’s thesis is a classification of digital modulation type. The interest in modulation classification has been growing for last years. It has several possible roles in both civilian and military applications such as spectrum sensing, signal confirmation, interference identification, monitoring and so on. Modulation classification is an intermediate step between signal detection and successful demodulation. Therefore the known methods are based on different statistics obtained from received signals. These statistics can be derived from continuous time signals and they hold for sampled signals.

This paper is focused on an artificial neural network RCE, especially describing the topology, properties and learning algorithm of the network. This paper describes program uTeachRCE developed for learning the RCE network and program RCEin3D, which is created to visualize the RCE network in 3D space. The RCE network is compared with a multilayer neural network with a learning algorithm backpropagation in the practical application of recognition letters. For a descriptions of the letters were chosen moments invariant to rotation, translation and scaling image.

Tato práce se zabývá využitím EEG dat pro výpočet mozkové konektivity a vytvořením klasifikátoru mentální zátěže. Nejdříve je popsán teoretický základ EEG, následně jsou rozebrány některé metody pro určení mozkové konektivity. Pro výpočet klasifikačních příznaků byla použita data nasnímaná během experimentu, který manipuloval s mentální zátěží ve dvou stupních. V práci je popsán průběh experimentu, zpracování a redukce nasnímaných dat, stejně jako extrakce příznaků z nasnímaných EEG dat pomocí několika metod měření konektivity (korelační funkce, kovariance, koherence a míra fázové soudržnosti) a následná automatická klasifikace třemi způsoby (na základě vzdálenosti od vzoru tvořeného průměrem, metoda nejbližšího souseda a diskriminační alanýza). Dosažené výsledky jsou detailně popsány a diskutovány. Nejlepšího výsledku (úspěšnost 60,64%) bylo dosaženo při použití kovarianční matice určené z dat získaných ze 4 elektrod z různých mozkových oblastí (beta pásmo EEG) při klasifikaci založené na lineární diskriminační funkci.

In this master thesis is described how to design and implement classifier of defects detected during the final stage of production nonwovens. The beginning of the thesis is devoted to the analysis of options for image processing and classification. Followed by the part, where is described process of image segmentation and extraction of feature vector. Description of classifier implementation and table of achieved results of classification on real images of detected defects.