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Traffic analysis using on machine learning
Zelený, Ondřej ; Slanina, Martin (referee) ; Frýza, Tomáš (advisor)
Tato práce přibližuje problematiku detekce objektů a jejich klasifikace pro uplatnění k analýze dopravy. V teoretické části přibližuji několik metod a technik pro detekci a klasifikaci objektů. Dále zde představuji nejpoužívanější platformy a programovací jazyky pro implementaci konvolučních neuronových sítí.. V praktické části se pak zabývám implementací vybraného modelu a výběrem hardware pro realizaci systému.
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Deep learning based QRS delineator
Malina, Ondřej ; Ronzhina, Marina (referee) ; Smíšek, Radovan (advisor)
This thesis deals with the issue of automatic measurement of the duration of QRS complexes in ECG signals. Special emphasis is then placed on the possibility of automatic detection of QRS complexes while exciting cardiac tissue with a pacemaker. The content of this work is divided into four logical units, where the first part deals with the heart as an organ. It describes the origin and spread of excitement in the heart, its possible pathologies and their manifestations in ECG recording, it also deals with pacing and measuring ECG recording during simultaneous pacing. The second part of the thesis contains a brief introduction to the topic of machine and deep learning. The third part of the thesis contains a search of current approaches using methods based on deep learning to solve the detection of QRSd. The fourth part deals with the design and implementation of its own model of deep learning, able to detect the beginnings and ends of QRS complexes from ECG recordings. It describes the data preprocessing implemented in the MATLAB programming environment. The actual implementation of the model was performed in the Python using the PyTorch and NumPy moduls.
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Cell segmentation using convolutional neural networks
Hrdličková, Alžběta ; Chmelík, Jiří (referee) ; Vičar, Tomáš (advisor)
This work examines the use of convolutional neural networks with a focus on semantic and instance segmentation of cells from microscopic images. The theoretical part contains a description of deep neural networks and a summary of widely used convolutional architectures for image segmentation. The practical part of the work is devoted to the creation of a convolutional neural network model based on the U-Net architecture. It also contains cell segmentation of predicted images using three methods, namely thresholding, the watershed and the random walker.
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Simulation of Skin Diseases Effect Using GAN
Bak, Adam ; Goldmann, Tomáš (referee) ; Kanich, Ondřej (advisor)
Cieľom tejto diplomovej práce je vygenerovanie datasetu syntetických snímkov odtlačkov prstov, ktoré vykazujú známky kožných ochorení. Práca sa zaoberá poškodením spôsobeným kožnými ochoreniami v odtlačkoch prstov a generovaním syntetických odtlačkov prstov. Odtlačky prstov s prejavom kožných ochorení boli generované s využitím modelu založeného na Wasserstein GAN s penalizáciou gradientu. Na trénovanie GAN modelu bola použitá unikátna databáza odtlačkov prstov s prejavom kožných ochorení vytvorená na FIT VUT. Daný model bol trénovaný na troch typoch kožných ochorení: atopický ekzém, psoriáza a dyshidrotický ekzém. Sieť generátoru z natrénovaného WGAN-GP modelu bola použitá na vygenerovanie datasetov syntetických odtlačkov prstov. Tieto syntetické odtlačky boli porovnané s reálnymi odtlačkami s využitím NFIQ a FiQiVi nástrojov na určenie kvality spoločne s porovnaním rozložení lokácií a orientácii markantov v snímkoch odtlačkov prstov.
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Detection and Classification of Damage in Fingerprint Images Using Neural Nets
Vican, Peter ; Drahanský, Martin (referee) ; Kanich, Ondřej (advisor)
The aim of this diploma thesis is to study and design experimental improvement of the convolutional neural network for disease detection. Another goal is to extend the classifier with a new type of detection. he new type of detection is damage fingerprint by pressure. The experimentally improved convolutional network is implemented by PyTorch. The network detects which part of the fingerprint is damaged and draws this part into the fingerprint. Synthetic fingerprints are used when training the net. Real fingerprints are added to the synthetic fingerprints.
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Pedestrian Detection and Recognition in a Multi-Camera System
Macák, Filip ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
The main purpose of this bachelor's thesis is to create an application for person detection and recognition from scenes captured in a multi-camera system. The output of the application is a video on which the detected persons are highlighted and each person is assigned an identification number through which it can be recognized across the input scenes. Several solutions to the problem of person detection and recognition were examined and the text of this work serves as an overview of these problems. The application is built on PyTorch and Torchreid libraries. A detector with a Faster-RCNN network is used for detection and recognition is based on the OSNet network. The application also includes a simple user interface to facilitate work with the application. The application serves as a demonstration of the state-of-the-art for person detection and recognition.
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Deep Neural Networks for Reinforcement Learning
Košák, Václav ; Dobeš, Petr (referee) ; Hradiš, Michal (advisor)
The paper describes a training environment for training a character how to walk. The environment is implemented in Al Gym by using the PyBullet physical model. Tasks from the environment are solved by using reinforcement learning by the ActorCritic algorithm. Each of the tasks is focused on the fundamental movements of the character. The paper show, which reward functions are used by the algorithm to solve given tasks.
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Detection of single photon-upconversion nanoparticles by luminescence microcopy
Polachová, Natálie ; Kolář, Radim (referee) ; Fohlerová, Zdenka (advisor)
This bachelor thesis deals with the detection of photon-upconversion nanoparticles using the U-net convolutional neural network, by using epiluminescence microscopy. The theoretical part contains an introduction to the issue of photon-upconversion, description and use of photon-upconversion nanoparticles. Furthermore, the thesis deals with the functioning of basic and convolutional neural networks. In the practical part, we prepared samples of nanoparticles with subsequent acquisition of images by epiluminescence microscopy. The convolutional neural network U-net was designed, which further serves for the detection of nanoparticles bz using H-maxima morphological operations. In the end, everything was summarized and statistically evaluated..
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Deep learning model for visual detection and classification general object from industry
Dočkal, Radim ; Honec, Peter (referee) ; Kratochvíla, Lukáš (advisor)
The goal of this bachelor’s thesis is to programme deep learning model for visual detection and classification of general object from industry. The paper is divided into five chapters. First chapter deals with research of the most used architectures of this type. The second chapter deals with choosing the best fitting architecture for usage in industry. In the third chapter is desribed the procedure of creating your own dataset. The fourth chapter then describes the implementation process itself, so that each sub-part of the architecture was sufficiently described and the results are described in the fifht chapter. The summary and recommended procedures for potential implementation in real environment can be found in the conclusion of this paper.
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Image segmentation using machine learning
Matějek, Libor ; Frýza, Tomáš (referee) ; Bravenec, Tomáš (advisor)
This work deals with machine learning and its application in the field of image segmentation and object recognition. The thesis describes the basic terminology related to machine learning and data related to it. It also focuses on the biological nature of the neuron and its technological applications. The basic types of neural networks and the key convolutional neural network for image processing are described. The work also presents the used architectures of convolutional neural networks. Then follow the methods of image preprocessing before the convolutional network R-CNN. Subsequently, some of the datasets suitable for image recognition are analyzed. The implementation is then realized in Python with support for the PyTorch framework from Facebook.
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