The main theme of this master's thesis is a description of text mining. This document is specialized to English texts and their automatic data preprocessing. The main part of this thesis analyses various stemming algorithms (Lovins, Porter and Paice/Husk). Stemming is a procedure for automatic conflating semantically related terms together via the use of rule sets. Next part of this thesis describes design of an application for various types of stemming algorithms. Application is based on the Java platform with using of graphic library Swing and MVC architecture. Next chapter contains description of implementation of the application and stemming algorithms. In the last part of this master's thesis experiments with stemming algorithms and comparing the algorithm from viewpoint to the results of classification the text are described.

This thesis deals with a face detection on mobile phones. It especially focuses on Windows Mobile platform. The introduction is therefore devoted to this operating system and alternatives of working with the camera. The next part of the text refers to general problems of the face detection in the image considering the weak performance of the target device. Another part of this thesis is a description of the acquisition of images from the camera using DirectShow multimedia framework and creation of a custom transformation filter for the face detection. Achieved results are summarized in the conclusion. It takes a form of tests examining different mobile devices. All difficulties arising during Windows Mobile developing are also mentioned.

Tato práce se zabývá a analyzuje možnosti, kterými je možno zjistit geografickou polohu ze snímků nebo videa pouze za použití vizuální informace z obrazu.    Výsledkem práce jsou dvě rozdílné metody geo-lokalizace. První z nich pracuje na principu hledání časů východu a západu Slunce. Hlavní výhodou této metody je její univerzálnost. Funguje s jakoukoliv kamerou umístěnou v externích prostorech a nevyžaduje přítomnost žádných specifických objektů ve scéně. Pro správný výsledek je třeba alespoň celodenní záznam z kamery. Výsledky jsou uspokojivé za každého počasí. Druhá metoda pracuje na základě analýzy stínů ve scéně. Správnou pozici je možno určit, s poměrně velkou přesností, pouze na základě dvou snímků pořízených v různém čase. Tato metoda vyžaduje přítomnost dvou objektů v obraze, které vrhají stín. Přesnosti výsledků navržených metod jsou vyhodnoceny a porovnány. Z výsledků vyplývá, že obě metody lze úspěšně použít pro odhad geografické polohy. Dále byla v rámci práce pořízena rozsáhlá datová sada obrazových sekvencí z volně přístupných webových kamer.

V rámci této práce byl navržen a implementován systém pro analýzu dopravy z videa. Tento system umožňuje detekovat, sledovat a klasifikovat automobily. Systém je schopný detekovat pruhy z pohybu projíždějících automobilů a také je možné určit, zdali daný automobil jede v protisměru. Rychlost projíždějících automobilů je také měřena. Pro funkčnost systému není vyžadován žadný manuální vstup nebo kalibrace kamery, jelikož kamera je plně automacky zkalibrována pomocí úběžníků. Navržený systém pracuje s velkou přesností detekce, sledování a klasifikace automobilů a také rychlost automobilů je měřena s~malou chybou. Systém je schopný pracovat v reálném čase a je aktuálně využíván pro nepřetržité online sledování dopravy. Největším přínosem této práce je plně automatické měření rychlostí projíždějích vozidel.

This thesis deals with an automatic counting of cylinders placed on the back of a truck using images taken by a camera mounted above the car. To achieve this goal, an SVM classifier based on HOG image descriptors has been trained to detect the cylinders. Further, a tracking method based on optical flow estimation has been designed to track the cylinders through image sequences. The result of the thesis is an application that counts bottles with precision 93,08 % placed on the truck and visualizes results of the detection.

My thesis is dealing with the Learnable Evolution Model (LEM), a new evolutionary method of optimization, which employs a classification algorithm. The optimization process is guided by a characteristics of differences between groups of high and low performance solutions in the population. In this thesis I introduce new variants of LEM using classification algorithm AdaBoost or SVM. The qualities of proposed LEM variants were validated in a series of experiments in static and dynamic enviroment. The results have shown that the metod has better results with smaller group sizes. When compared to the Estimation of Distribution Algorithm, the LEM variants achieve comparable or better values faster. However, the LEM variant which combined the AdaBoost approach with the SVM approach had the best overall performance.

This paper describes the use of meta-learning in the area of data mining. It describes the problems and tasks of data mining where meta-learning can be applied, with a focus on classification. It provides an overview of meta-learning techniques and their possible application in data mining, especially  model selection. It describes design and implementation of meta-learning system to support classification tasks in data mining. The system uses statistics and information theory to characterize data sets stored in the meta-knowledge base. The meta-classifier is created from the base and predicts the most suitable model for the new data set. The conclusion discusses results of the experiments with more than 20 data sets representing clasification tasks from different areas and suggests possible extensions of the project.

The goal of the master thesis project was to develop an application for tracking of actual events in the surrounding area of the users. This application should allow the users to view events, create new events and add comments to existing ones. Beyond the implementation of developed application, this project deals with an analysis of the presented problem. The analysis includes a comparison with existing solutions and search for available technologies and frameworks applicable for implementation. Another part inside this work is description of the theory in behind of data classification that is internally used for event and comment analysis. This work also includes a design of appliction including design of user interface, software architecture, database, communication protocol and data classifiers. The main part of this project, the implementation, is described aftewards. At the end of this work, there is a summary of the whole process and also there are given some ideas about enhancing the application in the future.

This master thesis deals with the use of gestures in user interfaces. The goal of this thesis is to create library for hand tracking and gesture recognition in real time. For hand tracking was choosen algorithm Flock of Features. Classification of gestures is done by using algorithm DTW. This thesis also contains stage design, design and implementation of a system that uses this library. Within the tests was tested control of various application using this library.

The content of this master thesis project is about design and implementation of intelligent client application for Music Player Daemon (MPD), which searches and presents the metadata related to played content. The actual design precedes the theoretical analysis, which includes analysis of agent systems, methods of data classification, web communication protocols and languages for describing HTML document. At the same time is analyzed the MPD server and communication protocol used by clients application. Furthermore, this work describes the current client applications that presents metadata. In the last chapters of the thesis describes the design and implementation of intelligent client. It describes the methods of solution the implementation and solution of problems. Lastest chapters describes the testing result.