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Deep Learning for Image Recognition
Kozel, Michal ; Španěl, Michal (referee) ; Hradiš, Michal (advisor)
Neural networks are currently state-of-the-art technology for speech, image and other recognition tasks. This thesis describes basis properties of neural networks and their learning. The aim of this thesis was to extend Caffe framework with new learning methods and compare their performance on Cifar10 dataset. Namely RMSPROP and normalized SGD
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Support for Codenames Game on Mobile Phone with OS Android
Grossmann, Jan ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
This bachelor's thesis deals with creation of an application for support for the Codenames game on mobile phone with Android operating system. Application helps user with game strategy and simplify selection of the clue. First I discuss existing solutions and their imperfections. Based on this experience, I analyze designed solution and then, the very implementation with usage of Java programming language, involving storing data with database system or optical recognition. Finally, I undertake user testing, which I also describe in detail.
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Altitude Estimation from an Image
Vašíček, Jan ; Kolář, Martin (referee) ; Čadík, Martin (advisor)
This thesis is concerned with the automatic altitude estimation from a single landscape photograph. I solved this task using convolutional neural networks. There was no suitable training dataset available having information about image altitude, thus I had to create a new one. To estimate human performance in altitude estimation task, an experiment was conducted counting 100 subjects. The goal of this experiment was to measure the accuracy of the human estimate of camera altitude from an image. The measured average estimation error of subjects was 879 m. An automatic system based on convolutional neural networks outperforms humans with an average elevation error 712 m. The proposed system can be used in more complex scenario like the visual camera geo-localization.
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Monitoring Pedestrian by Drone
Dušek, Vladimír ; Goldmann, Tomáš (referee) ; Drahanský, Martin (advisor)
This thesis is focused on monitoring people in a video footage captured by drone. People are detected by trained model of detector RetinaNet. A feature vector is extracted for each detected person using color histograms. Identification of people is realized by comparing their feature vectors with respect to their distance in the frame. In the end the trajectories of all people are visualized in a panorama image. Accuracy of the trained RetinaNet detector on difficult validation data is 58.6 %. Error rate is partially reduced by the way of algorithm design for trajectory visualisation. It's not necessary to successfully detect person on every frame for correct visualization of its trajectories. At the same time, static objects which are detected as person but are not moving are not consider as people and are not visualized at all. There is a lot of algorithms dealing with people detection however only a few approaches are focused on detection people from an aerial footage.
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Automatic Forms Validation with RPA
Rákoczy, Filip ; Heriban, Pavel (referee) ; Dobrovský, Ladislav (advisor)
Následující text se zabývá otázkou automatizovaného testování uživatelských rozhraní aplikací pro Windows. Konkrétně tato práce navrhuje samostatné softwarové řešení napsané v jazyce Python, které nahrazuje existující software SikuliX pro testování uživatelských rozhraní elektronových mikroskopů v Thermofisher Scientific. Tato práce diskutuje o výhodách a implementačnách detailech tohoto nového řešení a jsou navrženy další možné způsoby rozšíření tohoto softwaru. Výsledky jsou testovány na různých aplikacích, včetně aplikace pro řízení mikroskopu xT od společnosti Thermofisher scientific.
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Mobile Application for Automatic Recording of Chess Games
Jiruška, Adam ; Bobák, Petr (referee) ; Čadík, Martin (advisor)
This thesis is focused on making application for mobile devices, which records progress of chess game. This is achieved by image recognition on input from camera. Chess figures are classified by neural network. Usage of application is during training or real matches to record games and then for analyzing these games. For analyzing, my application offers record in standard algebraic notation. User can also add notes to every game.
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Automatic Trafic Scene Analysis Using Image Processing
Válek, Lukáš ; Špaňhel, Jakub (referee) ; Zemčík, Pavel (advisor)
Tato práce se zabývá problematikou analýzy scény pomocí metod počítačového vidění. Cílem této práce je vytvořit systém schopný automaticky detekovat anomálie nacházející se ve video záznamech. Práce se zabývá systémy pro detekci a sledování objektů v obraze, tvorbou grafického uživatelského rozhraní a algoritmem pro detekci porušení uživatelem definovaných pravidel. Výsledkem práce je webová aplikace, která uživateli umožňuje správu videozáznamů, definování pravidel pro scénu, zahájení detekce anomálií a zobrazení výsledků analýzy. Systém pracuje v reálném čase, upozorňuje uživatele o dokončení operace a uchovává výsledky analýzy pro další zpracovaní.
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Weather Estimation Based on Images of Clouds
Kukaň, Tomáš ; Goldmann, Tomáš (referee) ; Orság, Filip (advisor)
The main purpose of this thesis is a creation of a simple mobile application that would be able to give weather predictions based on a cloud photo through the usage of convolu- tional neural networks. I have analyzed all types of clouds and joined them with weather prediction. Then there are the results of experiments with different neural networks archi- tectures and different datasets. In the end of this thesis I have described the creation of the Android application as well as the problems I had to solve.
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Monitoring Pedestrian by Drone
Dušek, Vladimír ; Goldmann, Tomáš (referee) ; Drahanský, Martin (advisor)
This thesis is focused on monitoring people in a video footage captured by drone. People are detected by trained model of detector RetinaNet. A feature vector is extracted for each detected person using color histograms. Identification of people is realized by comparing their feature vectors with respect to their distance in the frame. In the end the trajectories of all people are visualized in a panorama image. Accuracy of the trained RetinaNet detector on difficult validation data is 58.6 %. Error rate is partially reduced by the way of algorithm design for trajectory visualisation. It's not necessary to successfully detect person on every frame for correct visualization of its trajectories. At the same time, static objects which are detected as person but are not moving are not consider as people and are not visualized at all. There is a lot of algorithms dealing with people detection however only a few approaches are focused on detection people from an aerial footage.
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