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Automatická vizuální podpora pro Q-řazení
Kán, Dávid ; Hradiš, Michal (referee) ; Vaško, Marek (advisor)
This bachelor thesis deals with the integration of Q-sorting and computer vision methods for object detection. The goal of the work is to create a program that, with the help of~visual support, will facilitate the process and at the same time prevent errors in Q-sorting. Furthermore, the work deals with the creation of~a suitable data set for training the model and for experiments, which takes into account the way the cards are laid out and the~environment. The implemented program takes the form of a console application and is written using the Python programming language. The program uses YOLOv8 to detect objects and uses Pero OCR to retrieve text from cards. Using the created test set, experiments were performed on the trained model and the program was tested.
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Automatická kontrola dopravního značení
Čechmánek, Roman ; Klíma, Ondřej (referee) ; Musil, Petr (advisor)
The aim of this work is to create a cost-effective tool capable that would be able to automate the process of traffic sign control. This includes working with records of drives on land communications, created using inexpensive recording devices such as GoPro action cameras or certain dashcams. The control is based on the system localized traffic signs and historical traffic sign mapping data. The result of the work is a system whose input consists of drive records and historical data, and whose output is two files containing information about the inspection results. The first of these is a GEOJSON file, suitable for further processing of the collected data, and an HTML file that provides a simple user interface visualizing the inspection results on an interactive web map.
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Mobile Application for Scanning Nonograms and Solving It
Zobaník, Michal ; Pánek, Richard (referee) ; Dyk, Tomáš (advisor)
The goal of this bachelor thesis is to create mobile application for scanning nonograms from newspapers or magazines and allows its solving. The thesis describes the design of the application, its functionality and important parts of implementation. Image processing methods are used for detection of the nonogram. Number recognition is realized by created and trained convolutional network. Scanned nonograms are solved by using several logical rules and backtracking.
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Enhancing Reliability and Benchmarking Performance of Agar Plate Handling Algorithms for Laboratory Automation Robots
Kalivodová, Tereza ; Nohel, Michal (referee) ; Mézl, Martin (advisor)
Tato bakalářská práce zkoumá problematiku vzorkové přípravy v oblasti mikrobiologie a lékařské diagnostiky s důrazem na automatizovaný robotický systém MBT Pathfinder, vyvinutý firmou \bruker. S využitím digitálních obrazových technik a konvolučních neuronových sítí se práce zaměřuje na zdokonalení algoritmu pro identifikaci pozice mikrobiálních kolonií v systému MBT Pathfinder. Praktická část práce prezentuje inovativní přístupy k optimalizaci kritických kroků vzorkové přípravy s cílem eliminovat chyby a zvýšit efektivitu procesu. Výsledky této práce mohou posílit spolehlivost mikrobiologických analýz v oblasti lékařské diagnostiky a mikrobiologického výzkumu.
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Road and path segmentation in images for autonomous driving scenario
Janíček, Ondřej ; Cihlář, Miloš (referee) ; Svědiroh, Stanislav (advisor)
This bachelor's thesis deals with the topic of segmentation of roads and paths for the purposes of autonomous driving. In the theoretical part, it deals with computer vision, simple segmentation methods, and practical solutions to the problem using convolutional neural networks and classical methods. In the practical part, the work deals with the collection of test data, the selection of a suitable programming language, and the selection of suitable libraries. Subsequently, the procedure for programming our own solution will be presented. Here it starts with pre-processing to convert the image into a grayscale image and filtering the noise, then finding the edges in the image using the Canny edge detector, followed by the definition of the region of interest, with the subsequent Hough transform to detect the straight lines in the image, and in the last stage, filtering the horizontal lines and averaging the remaining lines. At the end of the thesis, the results of the presented solution are compared with respect to robustness and computational complexity.
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Application of deep learning in sleep apnea detection
Láznička, Jakub ; Šaclová, Lucie (referee) ; Králík, Martin (advisor)
The master thesis focuses on the use of deep learning methods for the detection of sleep apnea, a sleep disorder characterized by repeated episodes of cessation or significant reduction in airway flow during sleep. The study investigates the effectiveness of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) models in the automatic detection of different types of sleep apnea using polysomnographic recordings. The datasets used in this work are from the MESA database, which have been specially prepared and modified for deep learning. The best performing models achieved F1-scores of 0.87 and 0.83, showing that deep learning can provide accurate tools for sleep apnea diagnosis, representing a potential improvement in clinical practice. The paper also discusses the possibilities of integrating these models into clinical diagnostic processes and outlines directions for future research in this area.
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Algorithm for Facial Image Quality Estimation
Husár, Tomáš ; Sakin, Martin (referee) ; Goldmann, Tomáš (advisor)
The precision of face recognition algorithms is heavily influenced by the quality of input images. The aim of the work is to evaluate the quality of face images using a convolutional neural network. The data on which the testing was carried out were created by various degradations of photos from the CelebA dataset. The resulting application determines the quality of images based on the predicted probabilities of individual degradations.
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Genetic Programming with Memory for Symbolic Regression
Jůza, Tadeáš ; Bidlo, Michal (referee) ; Sekanina, Lukáš (advisor)
The purpose of this thesis is to evaluate the possibility of extending genetic programming with memory for solving symbolic regression problems. Furthermore, a set of problems for testing the quality of such solutions is developed. The thesis proposes a practical application of such an extension to reduce the energy consumption of loading weights of convolutional neural networks. Instead of retrieving all the weights of the network from external memory, only a small percentage of the weights is retrieved and the remaining ones are generated using the evolved expression. This method was primarily evaluated on reducing the set of weights of convolutional layers of a small convolutional neural network classifying the MNIST dataset. Furthermore, the possibility of generating weights was also tested on other convolutional neural networks solving more complex classification problems. The proposed method has delivered interesting tradeoffs between the classification accuracy and weight memory size.
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Analýza vývoje rostlin pomocí umělé inteligence
Hežel, Hugo ; Juránková, Markéta (referee) ; Bažout, David (advisor)
This paper discusses the issues of plant growth monitoring, artificial neural networks, convolutional neural networks and also pays attention to their construction. We presented the design of two architecturally different convolutional neural network models for plant growth recognition. We tested and compared these models, and the model with the more complex architecture yielded only slightly better results than the model with the minimalist architecture.
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COVID-19 disease classification based on analysis of chest X-rays
Šteflík, Dominik ; Kiac, Martin (referee) ; Myška, Vojtěch (advisor)
This diploma thesis addresses the development and evaluation of artificial intelligence algorithms for classifying COVID-19 disease from chest X-ray images. Given the severity and impact of the COVID-19 pandemic on the global population, the ability to rapidly and accurately diagnose diseases from radiographic images has become critical. This study synthesizes current advancements in image processing and deep learning to evaluate the application of several novel classification methods in practice. Using a dataset obtained from a Czech medical environment, these methods are analyzed and validated in order to examine their effectiveness and accuracy in real life scenarios. The methods chosen for this study, COVID-Net, DarkCovidNet, and CoroNet, were selected due to their availability, widespread use and proven effectiveness in the field. The core of the thesis is the design of a convolutional neural network tailored to extract and learn from the subtle features present in X-ray images indicative of COVID-19. This initiative confronted significant challenges posed by variable acquisition parameters of X-ray images, which can substantially affect diagnostic accuracy. The uniformity of these parameters is crucial for reliable analysis, underscoring the importance of rigorous preprocessing techniques. In response, advanced normalization, contrast adjustment, and augmentation procedures were implemented to standardize the input data. The convolutional network itself employs a series of convolutional, pooling, and fully connected layers, optimized to handle the nuanced variations present in medical imaging data. Notably, the network architecture incorporates an attention mechanism, implemented through a Squeeze-and-Excitation block, to dynamically adjust the importance of different channels in the input image. By integrating these elements, the network model is trained to focus on significant features within the X-ray images, allowing it to distinguish subtle indicators of COVID-19 effectively. Furthermore, this work discusses the potential of integrating these AI-driven diagnostic tools into existing healthcare infrastructures to enhance early detection and treatment of COVID-19. The findings indicate that leveraging artificial intelligence in medical imaging can substantially aid in managing and controlling disease outbreaks, ultimately contributing to better health outcomes.
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