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A modern approach to measuring antibiotic susceptibility of microbial cultures using machine learning
Lepík, Jakub ; Burget, Radim (referee) ; Čičatka, Michal (advisor)
The bachelor's thesis focuses on antibiotic susceptibility testing (AST), specifically enhancing and automating the assessment of the disk diffusion method using machine learning and object detection architectures. Thanks to the TensorFlow development platform and extensive dataset, on which custom detection models like EfficientDet were trained, processing a wide range of input data is enabled. This brings the possibility of using mobile devices alongside traditional laboratory equipment when evaluating this method. By employing additional image processing techniques and the OpenCV library, a custom algorithm for measuring the size of inhibitory zones was developed, which, along with the detection models, is integrated within the application module developed by Bruker Daltonics GmbH & Co. KG. This module, created using the ASP.NET platform, is a precise and valuable tool for assisting personnel in microbiological laboratories.
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Forensic method for recognizing the authenticity of artworks using multispectral analysis
Lánský, David ; Mezina, Anzhelika (referee) ; Burget, Radim (advisor)
Detecting forgeries is crucial for protecting the art market and preserving the authenticity of artworks. This thesis focuses on forgery detection using convolutional neural networks (CNNs). The main goal was to develop advanced methods capable of identifying anomalies, and thus potential forgeries, in images with their X-ray photographs. During this research, U-net architectures and binary semantic segmentation techniques were applied, enabling successful anomaly detection. The main contribution of this work is 112 models of four different U-net and U-net++ architectures, which effectively highlight anomalies through the method of binary semantic segmentation. The models were trained on a set of images with their synthetically created X-ray images and artificially generated anomalies. In this way, the models can detect lead spots, nails, layers of hidden paintings, and other defects, while also being able to ignore insignificant elements, such as picture frames and overexposed X-ray images. The testing of the models occurred in two phases. In the first phase, they were evaluated using the IoU metric on a set of 400 synthetically generated data, where in the best cases, they achieved up to 83.5 % IoU. In the second phase, they were evaluated subjectively on images with real X-rays and natural anomalies. This approach combines traditional X-ray techniques with modern computer vision, revealing deviations that might be overlooked during standard visual inspection. By bridging these technologies, this work opens new possibilities for the protection of art collections and provides a solid foundation for further research in the field of art forgery detection using artificial intelligence.
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Optimization of control using reinforcement learning on the Robocode platform
Pastušek, Václav ; Myška, Vojtěch (referee) ; Burget, Radim (advisor)
This master's thesis focuses on optimizing the control of a tank robot in the Robocode environment using reinforcement learning. The complexity of this problem falls into the EXPSPACE class, presenting a challenge that cannot be underestimated. The theoretical part of the thesis meticulously examines the Robocode platform, concepts of reinforcement learning, and relevant algorithms, while the practical part focuses on optimizing the agent, implementing reinforcement learning algorithms, and creating a user-friendly interface for easy training and testing of models. A total of 64 models were trained and tested as part of the thesis, with their data and parameters compared and presented in accompanying databases and graphs. The best results in terms of average hits per episode were achieved by models labeled v0.8.0 and v1.0.0. The first model exhibited a certain ability to evade shots, while the second model showed more successful hits.
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Feature extraction from image data
Uher, Václav ; Beneš, Radek (referee) ; Burget, Radim (advisor)
Image processing is one area of signal analysis. This thesis is involved in feature extraction from image data and its implementation using Java programming language. The main contribution of this thesis lies in develop features extractors and their implementation in the program RapidMiner. The result is a robust tool for image analysis. The functionality of each operator is tested on mammogram images. A function model was developed for the removal of artifacts from the mammography images. The success rate of removal is comparable with other similar works. Furthermore, learning algorithms were compared on example detection of ventricle in ultrasound image.
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Enhancement of image quality for security forces
Varga, Adam ; Galáž, Zoltán (referee) ; Burget, Radim (advisor)
This bachelor thesis deals with image quality enhancement for security forces. Image quality enhancement in this case means increasing the resolution of image data by using super-resolution techniques using models of deep convolutional neural networks. The thesis in its theoretical part describes the principles of the operation of this technique and in its practical part is presented the work with selected state-of-the-art models in the area of super-resolution.
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Decentralized communication tool with anonymity guarantee
Legéň, Michal ; Burget, Radim (referee) ; Malý, Jan (advisor)
Anonymity on the internet is becoming a actuall issue nowadays. There are several tools, that can be used to monitor user's activity and it can lead to lose privacy of users. The aim of this master's thesis is to describe different ways of working anonymous systems, especially the method called Onion Routing. The introduction of this work is devoted to the description of this method together with asymmetric cryptosystem RSA. The second part belongs to basics of socket programming and to the implementation of anonymous system in programming language C++. The final part is focussed on analysis of system in terms of security and time complexity. The conditions of anonymity and decentralization are accomplished. There is no presence of central server in the system and the management is handled by signalling messages.
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Multidimensional Data Analysis and Analytic View Processing
Foltýnová, Veronika ; Burget, Radim (referee) ; Škorpil, Vladislav (advisor)
This thesis deals with the analysis and display of multidimensional data. In the theoretical part, the issue of data mining, its tasks and techniques, and a brief explanation of the terms Business Intelligence and data warehouse are presented. The issue of databases is also described in this thesis. Subsequently, the options for displaying multidimensional data are described. At the end of the theoretical part is briefly explained the problems of optical networks and especially the terms Gigabit passive optical network and its frame, because the data from the frames of this network will be displayed by an application. In the practical part, you can find creating a source database and an application to create a OLAP cube and display multidimensional data. This application is based on the theoretical knowledge of multidimensional databases and OLAP technology.
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Hough's transform for circle detection
Kazík, Martin ; Burget, Radim (referee) ; Říha, Kamil (advisor)
The thesis is focused on the implementation of Hough transform algorithm for circle recognition. Algorithm is implemented in C++ language using open source library OpenCv. As a development environment was chosen Microsoft Visual Studio 2008. At first there is general description of classical Hough transform for line and circle recognition. Then thesis contains description of particular steps of Hough transform algorithm and description of OpenCv functions witch are used in these steps. There is a detail description of functions for converting image to grayscale, smoothing image by Gaussian filter and Canny edge detector for edge detecting in smoothed image. Efficiency and speed of algorithm is increased by using function for finding possible centers. This function using the fact that line perpendicular to the chord of circle and going thought its middle point at the same time have to cross the center of the circle. Results of particular stages of algorithm (converting to grayscale, smoothing by Gaussian filter, edge detection, creating of possible centers accumulator and drawing circles) are presented on ultrasonic image of collagen arterial substitute. In the second part of the thesis the algorithm is used for detection of artery in frames of video captured by ultrasound. There is a description of automatic method for evaluating of success of artery detection. Success of detection is analyzed by changing values of important algorithm parameters. From series of tests there are defined ideal parameters of algorithm for artery detection in the video.
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