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Utilization of Robotic Operating System (ROS) for control of collaborative robot UR3
Juříček, Martin ; Matoušek, Radomil (referee) ; Parák, Roman (advisor)
The aim of the bachelor's thesis is to create a control program, its subsequent testing and verification of functionality for the collaborative robot UR3 from the company Universal Robots. The control program is written in python and integrates control options through the Robotic Operating System, where a defined point can be reached using pre-simulated trajectories of Q-learning, SARSA, Deep Q-learning, Deep SARSA, or using only the MoveIT framework. The thesis deals with a cross-section of the topics of collaborative robotics, Robotic Operating System, Gazebo simulation environment, feedback and deep feedback learning. Finally, the design and implementation of the control program with partial parts is described.
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Holistic License Plate Recognition Based on Convolution Neural Networks
Morbitzer, Dušan ; Juránek, Roman (referee) ; Špaňhel, Jakub (advisor)
The goal of this work is to create a model of neural network for holistic recognition of license plates, focused on accuracy and shortening of the learning process. The model was implemented as a union of convolutional neural network for extraction of deep features of a plate and Bidirectional LSTM with CTC. The trained model was compared to another implementation using a holistic approach, that was trained on the same dataset. My design of the network achieved better results in recognition on a dataset, which is different from the training one, with an error rate of 8.3 %.
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Detection and Classification of Damage in Fingerprint Images Using Neural Nets
Vican, Peter ; Rydlo, Štěpán (referee) ; Kanich, Ondřej (advisor)
The aim of the diploma thesis is to study and propose improvement of the current convolutional neural network for the classification and detection of fingerprint disease. An improvement of the current convolutional neural network is the change of library for the algorithm of learning, detecting and classifying fingerprint damage. Other improvements are to change the convolutional neural network model and a change in the activation function. At the same time, preprocessing using the Gabor filter will be added. Another change is in the area of thresholding. Next, there will be a change in general-purpose algorithms that will simplify the work for expanding database creation, the learning process itself, the classification and detection process, and the network testing process. At the same time, this network will be expanded with a new prediction and classification. Specifically the damage caused by eczema, psoriasis, pressure and moisture. The improved convolutional neural network is implemented by PyTorch. The network detects which part of the fingerprint is damaged and draws this part into the fingerprint. At the same time, the type of disease or imprint damage is classified during detection. Synthetic fingerprints are used in network training and are supplemented by real fingerprints.
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Mobile App for Recognition of Leukocoria in an Image of Human Face
Hřebíček, Pavel ; Kodym, Oldřich (referee) ; Herout, Adam (advisor)
The goal of this thesis is to design and implement a multiplatform multilingual mobile application for detecting leukocoria in an image of human face for iOS and Android platforms. Leukocoria is a whitish light of the pupil, which can be seen on the photo when the flash is used. Early detection of this symptom can save human eyesight. The application itself allows to analyze a user's photo and detect the presence of leukocoria. The goal of the application is to analyze eyes of the human, from which the mobile application name - Eye Check is derived. React Native framework was used to create a multiplatform mobile application. The Dlib library was chosen for human face and eye detection, the OpenCV library for working with the photo. The convolutional neural network was used to classify the eyes for the possible presence of leukocoria. Client-Server communication is solved using the REST architecture. The result is a mobile application that detects leukocoria and allerts the user to visit his doctor if leukocoria is detected.
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Estimation of the Position of the Feet in a Image
Havlíček, Lukáš ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
This thesis deals with the design and implementation of solution for estimating the position of the feet in the image, focusing on cases where the feet are covered in the image. The area of anthropometry, neural networks, solution design and implementation and subsequent experiments are described here. The solution uses own convolutional network, implemented using the Keras interface with the creation of own dataset with a focus on covered legs. In the experiments, other points on the body are used to increase accuracy.
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Food classification using deep neural networks
Kuvik, Michal ; Přinosil, Jiří (referee) ; Burget, Radim (advisor)
The aim of this thesis is to study problems of deep convolutional neural networks and the connected classification of images and to experiment with the architecture of particular network with the aim to get the most accurate results on the selected dataset. The thesis is divided into two parts, the first part theoretically outlines the properties and structure of neural networks and briefly introduces selected networks. The second part deals with experiments with this network, such as the impact of data augmentation, batch size and the impact of dropout layers on the accuracy of the network. Subsequently, all results are compared and discussed with the best result achieved an accuracy of 86, 44% on test data.
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Image similarity measuring using deep learning
Štarha, Dominik ; Šeda, Pavel (referee) ; Rajnoha, Martin (advisor)
This master´s thesis deals with the reseach of technologies using deep learning method, being able to use when processing image data. Specific focus of the work is to evaluate the suitability and effectiveness of deep learning when comparing two image input data. The first – theoretical – part consists of the introduction to neural networks and deep learning. Also, it contains a description of available methods, their benefits and principles, used for processing image data. The second - practical - part of the thesis contains a proposal a appropriate model of Siamese networks to solve the problem of comparing two input image data and evaluating their similarity. The output of this work is an evaluation of several possible model configurations and highlighting the best-performing model parameters.
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Artificial intelligence on nVIDIA Jetson platform
Batelka, Lukáš ; Kozovský, Matúš (referee) ; Blaha, Petr (advisor)
The aim of this bachelor thesis is to design, train and implement an artificial neural network in an NVIDIA Jetson Nano embedded device. The first part of the thesis describes the current state of the art of implementing artificial intelligence in embedded devices. The following section describes the tools for developing artificial neural networks and the possibilities of implementing them in a Jetson Nano device. These tools are further used in the thesis to create and train an artificial neural network to detect a fault in preprocessed measurement data on a synchronous electric motor. Finally, the optimization of the trained neural network is described. The achieved results are summarized in the conclusion of the paper.
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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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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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