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Detection of cells in confocal microscopy images
Hubálek, Michal ; Štursa, Dominik (referee) ; Škrabánek, Pavel (advisor)
The goal of the thesis was to create an application that automatically detects healthy cardiomyocytes from images captured by a confocal microscope. The thesis was created based on the specific needs of researchers from the Slovak Academy of Sciences.The application will facilitate and increase the efficiency of their research,because until now they have to evaluate the images and search for suitable cells manually. The RetinaNet convolutional neural network is used for detection and has been implemented in a user-friendly desktop application. The application also automatically records and stores coordinates of detected cells which can be used for capturing cells in higher image quality. Another advantage of the developed application is its versatility, which allows to train detection on other data, making it applicable to other projects. The result of this work is a functional, standalone and intuitive application that is ready to be used by researchers.
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A convolutional neural network for image segmentation
Mitrenga, Michal ; Petyovský, Petr (referee) ; Jirsík, Václav (advisor)
The aim of the bachelor thesis is to learn more about the problem of convolutional neural networks and to realize image segmentation. This theme includes the field of computer vision, which is used in systems of artificial intelligence. Special Attention is paid to the image segmentation process. Furthermore, the thesis deals with the basic principles of artificial neural networks, the structure of convolutional neural networks and especially with the description of individual semantic segmentation architectures. The chosen SegNet architecture is used in a practical application along with a pre-learned network. Part of the work is a database of CamVid images, which is used for training. For testing, a custom image database is created. Practical part is focused on CNN training and searching for unsuitable parameters for network learning using SW Matlab.
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Advanced scoring of sleep data
Jagošová, Petra ; Novotná, Petra (referee) ; Ronzhina, Marina (advisor)
The master´s thesis is focused on advanced scoring of sleep data, which was performed using deep neural network. Heart rate data and the movement information were used for scoring measured using an Apple Watch smartwatch. After appropriate pre-processing, this data serves as input parameters to the designed networks. The goal of the LSTM network was to classify data into either two groups for sleep and wake or into three groups for wake, Non-REM and REM. The best results were achieved by network doing classification of sleep vs. wake using the accelerometer. The statistical evaluation of this best-designed network reached the values of sensitivity 71,06 %, specificity 57,05 %, accuracy 70,01 % and F1 score 81,42 %.
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Detection of foreign objects in X-ray chest images using machine learning methods
Matoušková, Barbora ; Kolář, Radim (referee) ; Chmelík, Jiří (advisor)
Foreign objects in Chest X-ray (CXR) cause complications during automatic image processing. To prevent errors caused by these foreign objects, it is necessary to automatically find them and ommit them in the analysis. These are mainly buttons, jewellery, implants, wires and tubes. At the same time, finding pacemakers and other placed devices can help with automatic processing. The aim of this work was to design a method for the detection of foreign objects in CXR. For this task, Faster R-CNN method with a pre-trained ResNet50 network for feature extraction was chosen which was trained on 4 000 images and lately tested on 1 000 images from a publicly available database. After finding the optimal learning parameters, it was managed to train the network, which achieves 75% precision, 77% recall and 76% F1 score. However, a certain part of the error is formed by non-uniform annotations of objects in the data because not all annotated foreign objects are located in the lung area, as stated in the description.
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Improving Bots Playing Starcraft II Game in PySC2 Environment
Krušina, Jan ; Škoda, Petr (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to create an automated system for playing a real-time strategy game Starcraft II. Learning from replays via supervised learning and reinforcement learning techniques are used for improving bot's behavior. The proposed system should be capable of playing the whole game utilizing PySC2 framework for machine learning. Performance of the bot is evaluated against the built-in scripted AI in the game.
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Algorithmic Solution for Determining the Age of a Person Based on 2D Photography Using Artificial Intelligence
Bednář, Pavel ; Goldmann, Tomáš (referee) ; Drahanský, Martin (advisor)
Automated person's age estimation from a facial image is one of the challenges in the field of artificial intelligence and machine learning. Age estimation is often a non-trivial complexity for a person, unlike other biological characteristics such as determining gender or race. Information about an individual's age is very important for certain situations. For example, when committing an offense or crime, the amount of the sentence is co-determined by age. This information can also be used in the analysis of customers of a commercial entity and the subsequent adjustment of the offer. The aim of this work is to be able to extract his age from a photograph of a human face. The algorithm consists of two modules. If the first module says that the person is under 14 years old, the image will go to the second module. Furthermore, another version of the algorithm is proposed with an added module focused on selected facial features. In all modules transformations are performed on the image and their results are averaged. Finally, the algorithm is evaluated on standard datasets for age estimation and compared with state-of-the-art methods in this area.
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Recurrent Neural Network for Text Classification
Myška, Vojtěch ; Kolařík, Martin (referee) ; Povoda, Lukáš (advisor)
Thesis deals with the proposal of the neural networks for classification of positive and negative texts. Development took place in the Python programming language. Design of deep neural network models was performed using the Keras high-level API and the TensorFlow numerical computation library. The computations were performed using GPU with support of the CUDA architecture. The final outcome of the thesis is linguistically independent neural network model for classifying texts at character level reaching up to 93,64% accuracy. Training and testing data were provided by multilingual and Yelp databases. The simulations were performed on 1200000 English, 12000 Czech, German and Spanish texts.
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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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Automatic Humor Evaluation
Katrňák, Josef ; Ondřej, Karel (referee) ; Dočekal, Martin (advisor)
The aim of this thesis is to create a system for automatic humor evaluation. The system allow to predict humor and category for english input. The main essence is to create a classifier and train the model with the created datasets to get the best possible results. The classifier architecture is based on neural networks. The system also includes a web user interface for communication with the user. The result is a web application linked to a classifier that allows user input to be evaluated and user feedback to be provided.
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Convolutional neural networks for identification of axial 2D slices in CT data
Vavřinová, Pavlína ; Harabiš, Vratislav (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the classification of axial 2D slices in CT patient’s data into six categories. The sphere of convolutional neural networks was used for this purpose. For a better understanding of this issue, the basics of neural networks and then the principles of deep learning including convolutional neural networks are explained at first. The AlexNet network was specifically selected for the intention of this identification, and it was tested on the created data set after being adaptated. The overall classification success rate was 86% ,after the final adjustments, a slight improvement was achieved and the identification success rate was 87%.
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