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Object clasification based on its topology change using image processing
Zbavitel, Tomáš ; Věchet, Stanislav (referee) ; Krejsa, Jiří (advisor)
The aim of the present work is to select a suitable object classification method for the recognition of one-handed finger alphabet characters. For this purpose, a sufficiently robust dataset has been created and is included in this work. The creation of the dataset is necessary for training the convolutional neural network. Further more, a suitable topology for data classification was found. The whole work is implemented using Python and the open-source library Keras was used.
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Classification of UAV hyperspectral images using deep learning methods
Řádová, Martina ; Potůčková, Markéta (advisor) ; Kupková, Lucie (referee)
Diploma thesis "Classification of UAV hyperspectral images using deep learning methods" focuses on the classification methods, namely convolutional neural networks (CNN), of hyperspectral (HS) images. Based on a thorough literature review, a comprehensive overview on CNN utilisation in remote sensing is assembled as a basis for identifying suitable methods for the specific task of this thesis. Two methods with an open solution in programming language Python were selected - Capsule Network and U-Net. The main aim of this work is to verify the suitability of chosen methods for the classification of hyperspestral images. The images were acquired by sensors with high spatial resolution carried by a UAV over Krkonoše Mts. tundra. Important step was to prepare input HS data (54 bands, 9cm) to have suitable form for entering the network. Not all the required results were achieved due to the complexity of the Capsule Network architecture. The U-Net method was used in purpose of comparing and verifying the results. Accuracies retrieved from the U-Net overcome results achieved by traditionally used machine learning methods (SVM, ML, RF, etc). Overall accuracy for U-Net was higher than 90% where other mentioned methods did not get over 88%. Especially classes block fields and dwarf pine achieved higher...
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Anticurtaining - Image Filter for Electron Microscopy
Dvořák, Martin ; Dobeš, Petr (referee) ; Zemčík, Pavel (advisor)
Tomographic analysis produces 3D images of examined material in nanoscale by focus ion beam (FIB). This thesis presents new approach to elimination of the curtain effect by machine learning method. Convolution neuron network is proposed for elimination of damaged imagine by the supervised learning technique. Designed network deals with features of damaged image, which are caused by wavelet transformation. The outcome is visually clear image. This thesis also designs creation of synthetic data set for training the neuron network which are created by simulating physical process of the creation of the real image. The simulation is made of creation of examined material by milling which is done by FIB and by process displaying of the surface by electron microscope (SEM). This newly created approach works precisely with real images. The qualitative evaluation of results is done by amateurs and experts of this problematic. It is done by anonymously comparing this solution to another method of eliminating curtaining effect. Solution presents new and promising approach to elimination of curtaining effect and contributes to a better procedure of dealing with images which are created during material analysis.
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Detect the Use of Retouch Filters in a Face Image
Kraváček, Adam ; Drahanský, Martin (referee) ; Goldmann, Tomáš (advisor)
These days, altering images via filters is one of the easiest ways of enhancing its properties. Social networks like Instagram or Snapchat, focused primarily on image sharing, offer their users the option to apply filters on their images, which alter their colours to make them look better. If someone was to extract images from these platforms, many of these images would have a filter applied. This thesis explains the principles of these filters and focuses on detection of filters on facial images. Several approaches to detecting filters are being experimented with. Detection by analysis of histograms and detection by convolutional neural network achieve the best results and so are implemented in a program with a simple user interface. They achieved a success rate of 94,44% (histogram) and 99,10% (CNN). This thesis also investigates the impact of filters on facial recognition, where the impact varies depending on the filter used. Some filters have a significant impact on the rate of successful identifications, whereas others have little impact.In general, however, it can be said that the changes introduced by the application of filters are not negligible.
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Typing Using Brain Signals
Wagner, Lukáš ; Malinka, Kamil (referee) ; Tinka, Jan (advisor)
This bachelor thesis focusses on the implementation of a brain-computer interface, programmed in Python language, that would enable to communicate using EEG. The thesis investigates and evaluates existing brain-computer interface technologies for this purpose. The thesis also explores the use of machine learning applied to the technology, in particular neural networks, which have proven to be one of the most accurate methods of EEG signal processing. Following that, 3 different systems are proposed and implemented, each on different paradigm of visually evoking EEG potential changes. These systems were tested with different signal classification approaches. Unfortunately, none of the systems proved to be useful in communication.
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Visual Localization of Chess Pieces
Hampl, Tomáš ; Špaňhel, Jakub (referee) ; Hradiš, Michal (advisor)
The main goal of this thesis was to analyze state of the chess game and to locate chess pieces on the chessboard. Chessboard recognition is based on locating lines in image using Hough transform and PClines. The figures were detected by models of convolutional neural networks - YOLOv3, YOLOv4 and YOLOv4 tiny. Evaluation was perfomed on our data set. Chessboard detection achieves accuracy of 97%, complete localization of the state reaches 74,5% and piece localization 96%.
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Depth-Based Determination of a 3D Hand Position
Ondris, Ladislav ; Tinka, Jan (referee) ; Drahanský, Martin (advisor)
Cílem této práce je určení kostry ruky z hloubkového obrazu a jeho následné využití k rozpoznání statického gesta. Na vstupu je hloubkový obrázek, ve kterém je nejprve detekována ruka pomocí neuronové sítě Tiny YOLOv3. Následně je obrázek zbaven pozadí a z takto předzpracovaného obrázku je určena kostra ruky v podobě 21 klíčových bodů neuronovou sítí JGR-P2O. K rozpoznání gesta z klíčových bodů ruky byla navržena technika, která porovná kostru na vstupu s uživatelem definovanými gesty. Funkcionalita systému byla otestována na vytvořeném datasetu s více než čtyřmi tisíci obrázky.
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PVC detection in ECG
Imramovská, Klára ; Hejč, Jakub (referee) ; Ronzhina, Marina (advisor)
The thesis deals with problems of automatic detection of premature ventricular contractions in ECG records. One detection method which uses a convolutional neural network and LSTM units is implemented in the Python language. Cardiac cycles extracted from one-lead ECG were used for detection. F1 score for binary classification (PVC and normal beat) on the test dataset reached 96,41 % and 81,76 % for three-class classification (PVC, normal beat and other arrhythmias). Lastly, the accuracy of the classification is evaluated and discussed, the achieved results for binary classification are comparable to the results of methods described in different papers.
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Object detection in video using neural networks and Android application
Mikulec, Vojtěch ; Kiac, Martin (referee) ; Myška, Vojtěch (advisor)
This master’s thesis deals with the implementation of functional solution for classifying road users using mobile device with Android operating system. The goal is to create Android application which classifies vehicles in real time using rear-facing camera and saves timestamps of classification. Testing is performed mostly with own, diversely modificated dataset. Five models are trained and their performance is measured in dependence on hardware. The best classification performance is from pretrained MobileNet model where transfer learning with 6 classes of own dataset is used – 62,33 %. The results are summarized and a method for faster and more accurate traffic analysis is proposed.
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