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Moving Persons Detection and Tracking
Johanová, Daniela ; Zahrádka, Jiří (referee) ; Španěl, Michal (advisor)
This bachelor's thesis deals with the person detection using RGB-D Microsoft Kinect sensor. Human body detector is based on the method Combohod which uses both color and depth information from Kinect sensor. The aim of the thesis was to create a person detector whose functionality is demonstrated by proposing a statistical application that collects statistical information about the people who passed the shop window. At the end of the thesis the experiments with the detector under varios different conditions are described.
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Document Classification
Marek, Tomáš ; Škoda, Petr (referee) ; Otrusina, Lubomír (advisor)
This thesis deals with a document classification, especially with a text classification method. Main goal of this thesis is to analyze two arbitrary document classification algorithms to describe them and to create an implementation of those algorithms. Chosen algorithms are Bayes classifier and classifier based on support vector machines (SVM) which were analyzed and implemented in the practical part of this thesis. One of the main goals of this thesis is to create and choose optimal text features, which are describing the input text best and thus lead to the best classification results. At the end of this thesis there is a bunch of tests showing comparison of efficiency of the chosen classifiers under various conditions.
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Moving Persons Detection and Tracking
Johanová, Daniela ; Zahrádka, Jiří (referee) ; Španěl, Michal (advisor)
This bachelor's thesis deals with the person detection using RGB-D Microsoft Kinect sensor. Human body detector is based on the method Combohod which uses both color and depth information from Kinect sensor. The aim of the thesis was to create a person detector whose functionality is demonstrated by proposing a statistical application that collects statistical information about the people who passed the shop window. At the end of the thesis the experiments with the detector under varios different conditions are described.
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Document Classification
Marek, Tomáš ; Škoda, Petr (referee) ; Otrusina, Lubomír (advisor)
This thesis deals with a document classification, especially with a text classification method. Main goal of this thesis is to analyze two arbitrary document classification algorithms to describe them and to create an implementation of those algorithms. Chosen algorithms are Bayes classifier and classifier based on support vector machines (SVM) which were analyzed and implemented in the practical part of this thesis. One of the main goals of this thesis is to create and choose optimal text features, which are describing the input text best and thus lead to the best classification results. At the end of this thesis there is a bunch of tests showing comparison of efficiency of the chosen classifiers under various conditions.
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