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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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Image segmentation on GPU
Bravenec, Tomáš ; Mego, Roman (referee) ; Frýza, Tomáš (advisor)
Bachelor thesis is focused on using graphical processing units for parallel data processing, specifically on image processing. Main focus of this thesis is determining time difference in image processing using graphical processing unit and classic approach on processor. Another focus is accessing webcam and processing of captured frames.
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Traffic sign detection in real time
Sicha, Marek ; Přinosil, Jiří (referee) ; Bravenec, Tomáš (advisor)
The bachelor's thesis focuses on the detection and classification of traffic signs in images and video sequences. The goal of the work is also the possibility to perform detection and classification on a single board computer. Neural networks and the Python programming language were chosen to solve the problem. Object detection and classification are solved separately, so two neural networks were used. A convolutional neural network was chosen for classification and a detector from the EfficientDet family was chosen for detection. The overall architecture was tested on a single board Nvidia Jetson Nano computer.
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Exploiting Wireless Communications for Localization: Beyond Fingerprinting
Bravenec, Tomáš ; Sanchez, Maria Cristina Rodriguez (referee) ; Crivello, Antonino (referee) ; Moreira, Adriano (referee) ; Orti, Enrique Quintana (referee) ; Oliver, Sergi Trilles (referee) ; Frýza, Tomáš (advisor)
The field of Location-based Services (LBS) has experienced significant growth over the past decade, driven by increasing interest in fitness tracking, robotics, and eHealth. This dissertation focuses on evaluating privacy measures in Indoor Positioning Systems (IPS), particularly in the context of ubiquitous Wi-Fi networks. It addresses non-cooperative user tracking through the exploitation of unencrypted Wi-Fi management frames, which contain enough information for device fingerprinting despite MAC address randomization. The research also explores an algorithm to estimate room occupancy based on passive Wi-Fi frame sniffing and Received Signal Strength Indicator (RSSI) measurements. Such room occupancy detection has implications for energy regulations in smart buildings. Furthermore, the thesis investigates methods to reduce computational requirements of machine learning and positioning algorithms through optimizing neural networks and employing interpolation techniques for IPS based on RSSI fingerprinting. The work contributes datasets, analysis scripts, and firmware to improve reproducibility and supports advancements in the LBS field.
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Computer vision and hand gestures detection and fingers tracking
Bravenec, Tomáš ; Wyrzykowski, Roman (referee) ; Frýza, Tomáš (advisor)
Diplomová práce je zaměřena na detekci a rozpoznání gest rukou a prstů ve statických obrazech i video sekvencích. Práce obsahuje shrnutí několika různých přístupů k samotné detekci a také jejich výhody i nevýhody. V práci je též obsažena realizace multiplatformní aplikace napsané v Pythonu s použitím knihoven OpenCV a PyTorch, která dokáže zobrazit vybraný obraz nebo přehrát video se zvýrazněním rozpoznaných gest.
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Multi-Class Weather Classification From Single Images With Convolutional Neural Networks On Embedded Hardware
Bravenec, Tomáš
The paper is focused on creating a lightweight machine learning solution for classificationof weather conditions from input images, that can process the input data in real time on embeddeddevices. The approach to the classification uses deep convolutional neural networks architecture withfocus on lightweight design and fast inference, while providing high accuracy results. The focus oncreating lightweight convolutional neural network architecture capable of classification of weatherconditions also enables usage of the network in real time applications at the edge.
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Traffic sign detection in real time
Sicha, Marek ; Přinosil, Jiří (referee) ; Bravenec, Tomáš (advisor)
The bachelor's thesis focuses on the detection and classification of traffic signs in images and video sequences. The goal of the work is also the possibility to perform detection and classification on a single board computer. Neural networks and the Python programming language were chosen to solve the problem. Object detection and classification are solved separately, so two neural networks were used. A convolutional neural network was chosen for classification and a detector from the EfficientDet family was chosen for detection. The overall architecture was tested on a single board Nvidia Jetson Nano computer.
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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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Image Segmentation On CPU/GPU
Bravenec, Tomáš
This article is focused on using graphical processing units for parallel data processing, specifically on image processing. Main focus of this thesis is determining time difference in image processing using graphical processing unit and classic approach on processor. Another focus is accessing webcam and processing of captured frames.
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Hand Detection In Static Images, Video Sequences And Real Time Camera Feed
Bravenec, Tomáš
The goal of this project is to create a computer vision system capable of hand detection in static images and in video sequence either from existing recording or real time feed from connected camera. Algorithms commonly used for hand detection are mostly dependent on simple background and are very dependent on the lightning changes. To mostly eliminate these issues this project uses deep convolutional neural network trained for hand detection.
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