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	Application of AdaBoost Wrhel, Vladimír ; Šilhavá, Jana (referee) ; Hradiš, Michal (advisor) Basics of classification and pattern recognitions will be mentioned in this work. We will focus mainly on AdaBoost algorithm, which serves to create a strong classifier function by some weak classifiers. We shall get acquainted with some modifications of AdaBoost. These modifications improve some of AdaBoost attributes. We shall also look into weak classifiers and features applicable to them. We shall especially look into the Haar- likes features. We shall discus possibilities of using the mentioned algorithms and features in facial expression recognition. We shall describe the situation between facial expression databases. We shall draw out a possible implementation of application of facial expression recognition. Detailed record
	Implementation of Image Classifiers in FPGAs Kadlček, Filip ; Puš, Viktor (referee) ; Fučík, Otto (advisor) The thesis deals with image classifiers and their implementation using FPGA technology. There are discussed weak and strong classifiers in the work. As an example of strong classifiers, the AdaBoost algorithm is described. In the case of weak classifiers, basic types of feature classifiers are shown, including Haar and Gabor wavelets. The rest of work is primarily focused on LBP, LRP and LR classifiers, which are well suitable for efficient implementation in FPGAs. With these classifiers is designed pseudo-parallel architecture. Process of classifications is divided on software and hardware parts. The thesis deals with hardware part of classifications. The designed classifier is very fast and produces results of classification every clock cycle. Detailed record
	Application of AdaBoost Wrhel, Vladimír ; Šilhavá, Jana (referee) ; Hradiš, Michal (advisor) Basics of classification and pattern recognitions will be mentioned in this work. We will focus mainly on AdaBoost algorithm, which serves to create a strong classifier function by some weak classifiers. We shall get acquainted with some modifications of AdaBoost. These modifications improve some of AdaBoost attributes. We shall also look into weak classifiers and features applicable to them. We shall especially look into the Haar- likes features. We shall discus possibilities of using the mentioned algorithms and features in facial expression recognition. We shall describe the situation between facial expression databases. We shall draw out a possible implementation of application of facial expression recognition. Detailed record

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