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Urban Element Detection Using Satellite Imagery
Oravec, Dávid ; Herout, Adam (referee) ; Zlámal, Adam (advisor)
Táto práca sa zameriava na správnu detekciu objektov v satelitných snímkach pomocou konvolučných neuronových sietí. Cieľom práce je pomocou natrénovaného modelu detekovať bazény a tenisové ihriská v satelitných snímkach z rôznych miest. Model pracuje s dátami z 10 rôznych miest. Pri vypracovaní bol využitý model neurónovej siete RetinaNet a knižnica Detectron2. Model, ktorý sa podarilo vytrénovať, dokáže detekovať objekty s priemernou presnosťou (AP50) na úrovni 63,402 %. Práca môže byť prínosom v oblasti automatizovania získavania štatistík o povrchu zeme.
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Self-supervised learning in computer vision applications
Vančo, Timotej ; Richter, Miloslav (referee) ; Janáková, Ilona (advisor)
The aim of the diploma thesis is to make research of the self-supervised learning in computer vision applications, then to choose a suitable test task with an extensive data set, apply self-supervised methods and evaluate. The theoretical part of the work is focused on the description of methods in computer vision, a detailed description of neural and convolution networks and an extensive explanation and division of self-supervised methods. Conclusion of the theoretical part is devoted to practical applications of the Self-supervised methods in practice. The practical part of the diploma thesis deals with the description of the creation of code for working with datasets and the application of the SSL methods Rotation, SimCLR, MoCo and BYOL in the role of classification and semantic segmentation. Each application of the method is explained in detail and evaluated for various parameters on the large STL10 dataset. Subsequently, the success of the methods is evaluated for different datasets and the limiting conditions in the classification task are named. The practical part concludes with the application of SSL methods for pre-training the encoder in the application of semantic segmentation with the Cityscapes dataset.
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Semantic segmentation of images using convolutional neural networks
Špila, Filip ; Věchet, Stanislav (referee) ; Krejsa, Jiří (advisor)
Tato práce se zabývá rešerší a implementací vybraných architektur konvolučních neuronových sítí pro segmentaci obrazu. V první části jsou shrnuty základní pojmy z teorie neuronových sítí. Tato část také představuje silné stránky konvolučních sítí v oblasti rozpoznávání obrazových dat. Teoretická část je uzavřena rešerší zaměřenou na konkrétní architekturu používanou na segmentaci scén. Implementace této architektury a jejích variant v Caffe je převzata a upravena pro konkrétní použití v praktické části práce. Nedílnou součástí tohoto procesu jsou kroky potřebné ke správnému nastavení softwarového a hardwarového prostředí. Příslušná kapitola proto poskytuje přesný návod, který ocení zejména noví uživatelé Linuxu. Pro trénování všech variant vybrané sítě je vytvořen vlastní dataset obsahující 2600 obrázků. Je také provedeno několik nastavení původní implementace, zvláště pro účely použití předtrénovaných parametrů. Trénování zahrnuje výběr hyperparametrů, jakými jsou například typ optimalizačního algoritmu a rychlost učení. Na závěr je provedeno vyhodnocení výkonu a výpočtové náročnosti všech natrénovaných sítí na testovacím datasetu.
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A convolutional neural network for image segmentation
Mitrenga, Michal ; Petyovský, Petr (referee) ; Jirsík, Václav (advisor)
The aim of the bachelor thesis is to learn more about the problem of convolutional neural networks and to realize image segmentation. This theme includes the field of computer vision, which is used in systems of artificial intelligence. Special Attention is paid to the image segmentation process. Furthermore, the thesis deals with the basic principles of artificial neural networks, the structure of convolutional neural networks and especially with the description of individual semantic segmentation architectures. The chosen SegNet architecture is used in a practical application along with a pre-learned network. Part of the work is a database of CamVid images, which is used for training. For testing, a custom image database is created. Practical part is focused on CNN training and searching for unsuitable parameters for network learning using SW Matlab.
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Semantic Segmentation in Mountainous Environment
Pelikán, Jakub ; Čadík, Martin (referee) ; Brejcha, Jan (advisor)
Semantic segmentation is one of classic computer vision problems and strong tool for machine processing and understanding of the scene. In this thesis we use semantic segmentation in mountainous environment. The main motivation of this work is to use semantic segmentation for automatic location of geographic position, where the picture was taken. In this thesis we evaluated actual methods of semantic segmentation and we chose three of them that are appropriate for adapting to mountainous environment. We split the dataset with mountainous environment into validation, train and test sets to use for training of chosen semantic segmentation methods. We trained models from chosen methods on mountainous data. We let segments from the best trained models get evaluated in electronic survey by respondents and we evaluated these segments in process of camera orientation estimation. We showed that chosen methods of semantic segmentation are possible to use in mountainous environment. Our models are trained on 11, 5 or 4 mountainous classes and the best of them achieve on 4 class mean IU 57.4%. Models are usable in practise. We show it by their deployment as a part of camera orientation estimation process.
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Computer Aided Recognization and Classification of Coat of Arms
Vídeňský, František ; Kočí, Radek (referee) ; Zbořil, František (advisor)
This master thesis describes the design and development of the system for detection and recognition of whole coat of arms as well as each heraldic parts. In the thesis are presented methods of computer vision for segmentation and detection of an object and selected methods that are the most suitable. Most of the heraldic parts are segmented using a convolution neural networks and the rest using active contours. The Histogram of the gradient method was selected for coats of arms detection in an image. For training and functionality verification is used my own data set. The resulting system can serve as an auxiliary tool used in auxiliary sciences of history.
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