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Image Completion Using Depth Maps
Valeš, Ondřej ; Brejcha, Jan (referee) ; Čadík, Martin (advisor)
The aim of this thesis is to design and implement aplication for dataset driven scene completion utilizing data from similar dataset images and test the posibility of generating data used in reconstruction directly from depth map. For scene matching in dataset novel modification of GIST descriptor including depth data is used. Furthermore, depth maps can be used to split reconstructed image into parts with similar depth, simplifying reconstruction process. Part of this thesis is also computing GIST descriptors for datset images and implementation of tools for searching datset for similar images using depth maps.
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Detection and Recognition of License Plates
Tykva, Jiří ; Zemčík, Pavel (referee) ; Juránek, Roman (advisor)
Cílem této bakalářské práce je návrh, implementace a testování systému, který v reálném čase pomocí neuronových sítí bude detekovat a rozpoznávat registrační značky vozidel. Nasbíraná data budou ukládána do databáze. Architektura systému je rozdělena do tří hlavních částí. První část řeší detekci registrační značky v obraze pomocí TensorFlow Object Detection API. Detektor dosahuje přesnosti 98.15 % AP při rychlosti kolem 14 fps. Druhá část se zabývá sledováním značek ve videu pomocí algoritmu SORT. Třetí část systému se věnuje holistickému rozpoznávání textu registrační značky a dosahuje až 0.6% chybovosti při rozpoznávání jednotlivých znaků a 2% chybovosti při rozpoznávání celého textu. Výsledný systém lze použít například pro policejní oddělení za účelem sledování kradených vozidel či automatického vybírání dálničních poplatků.
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Face Detection, Invariant to Rotation
Bureš, Václav ; Herout, Adam (referee) ; Beran, Vítězslav (advisor)
This bachelor thesis focuses on the detection of type uniform objects (concretely faces) in an image. Furthermore the thesis concentrates on the detection of objects in various rotations. The thesis covers a brief overview of methods available, such as Logical Binary Patterns, Histogram Of Gradients, Eigen Faces and more closely specified AdaBoost. Next, freely available datasets are presented, with a descripiton of their chosen characteristics. At the end of the thesis, experiments using AdaBoost algorythm and their evaluation are described.
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Image Super-Resolution Using Deep Learning
Mojžiš, Tomáš ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
The aim of this thesis is to create a deep neural net capable of super-resolution on images acquired by electron microscopes. The thesis consists of two parts - finding appropriate data and creating a dataset for the super-resolution task and designing a neural net architecture capable of solving the super-resolution task. Within the thesis, two datasets comprised of images acquired by electron microscopes were created. The datasets differ in the approach to data augmentation. They allow to train a neural network which fulfills the super-resolution task. To solve this task, two U-Net based and one GAN based architecture were trained. The resolution of images was upscaled by a factor of two and four. The best artificially upscaled images were created by neural network Real-ESRGAN. The values of metrics were not higher than the tested interpolation method, but the images seem more visually pleasing especially when they were upscaled four times. Thanks to this thesis, two datasets were created allowing to train other possible neural network architectures to improve the quality of the artificially upscaled images. The neural networks trained in this thesis can be utilized in the process of acquiring higher quality data from low resolution electron microscope images.
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Pedestrian Attribute Analysis
Studená, Zuzana ; Špaňhel, Jakub (referee) ; Hradiš, Michal (advisor)
This work deals with obtaining pedestrian information, which are captured by static, external cameras located in public, outdoor or indoor spaces. The aim is to obtain as much information as possible. Information such as gender, age and type of clothing, accessories, fashion style, or overall personality are obtained using using convolutional neural networks. One part of the work consists of creating a new dataset that captures pedestrians and includes information about the person's sex, age, and fashion style. Another part of the thesis is the design and implementation of convolutional neural networks, which classify the mentioned pedestrian characteristics. Neural networks evaluate pedestrian input images in PETA, FashionStyle14 and BUT Pedestrian Attributes datasets. Experiments performed over the PETA and FashionStyle datasets compare my results to various convolutional neural networks described in publications. Further experiments are shown on created BUT data set of pedestrian attributes.
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Pedestrians Detection in Traffic Environment by Machine Learning
Tilgner, Martin ; Klečka, Jan (referee) ; Horák, Karel (advisor)
Tato práce se zabývá detekcí chodců pomocí konvolučních neuronových sítí z pohledu autonomního vozidla. A to zejména jejich otestováním ve smyslu nalezení vhodné praxe tvorby datasetu pro machine learning modely. V práci bylo natrénováno celkem deset machine learning modelů meta architektur Faster R-CNN s ResNet 101 jako feature extraktorem a SSDLite s feature extraktorem MobileNet_v2. Tyto modely byly natrénovány na datasetech o různých velikostech. Nejlépší výsledky byly dosaženy na datasetu o velikosti 5000 snímků. Kromě těchto modelů byl vytvořen nový dataset zaměřující se na chodce v noci. Dále byla vytvořena knihovna Python funkcí pro práci s datasety a automatickou tvorbu datasetu.
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Basics of Pedestrians Detection in Image by Machine Learning
Lučanský, Peter ; Klečka, Jan (referee) ; Horák, Karel (advisor)
Táto Bakalárska práce sa zaoberá významnou problematikou v oblasti počítačového videnia, ktorou je detekcia osôb/chodcov v obraze, za pomoci metod strojového učenia, spolu s jej možným využitím, vývojom a vysvetlením princípov. Taktiež sa zaoberá testovaním dnes najlepšieho dostupného algoritmu, pričom sa porovnávajú faktory ktoré vplívajú na kvalitu jeho činnosti. Na začiatku je problematika stručne popísaná, potom sa prejde k podrobným popisom dosiahnutých pokrokov. V nasledujúcej časti sú popísané dostupné datasety, ktoré by sa dali použiť pri tréningu detekčného algoritmu. V poslednom rade sú vykonané trénovacie procesy za rozličných podmienok, pričom sú jednotlivé výsledky porovnávané.
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Using machine learning for quality control in industrial applications
Gaško, Viktor ; Dobrovský, Ladislav (referee) ; Parák, Roman (advisor)
Goal of this bachelor´s thesis is to get acquainted with issue of quality control in industrial applications with focus on deep learning. For this and similar issues was created several libraries which have a purpose of simplifying these issues. Main task is to create program for quality control with help of programming language Python and framework Tensorflow. This program will be comprised of three neural network, from which one will identify the approximate position of the part, second its color, and third will check the correctness of its production.
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Analyzer of web application
Vašíček, Tomáš ; Lieskovan, Tomáš (referee) ; Martinásek, Zdeněk (advisor)
This work focuses on the identification of technologies in web applications. The main goal is to develop a software tool for basic analysis of web applications that aims to identify the application's operating system, web server, and programming language. This work further aims to allow the developed tool to be integrated into the Penterep platform as an extension module. The theoretical part of this work is an introduction into the area of web application analysis with emphasis on describing and manually demonstrating the existing identification methods. The practical part presents the design, implementation and testing of the developed tool. This part also includes a description of an experimental environment that was built using the Docker platform.
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Artificial Intelligence in architecture
Rubáč, František ; Mačuda, Michal (referee) ; Vítek, Jiří (advisor)
The thesis primarily deals with the topic of AI and its use in architecture. In the thesis I first discuss AI in a broader context, then I present the different possibilities of using AI in architecture. I describe the theoretical use of AI and accompany this description with practical examples. I then focus on organic architecture and on generating organic facades and buildings. Towards the end of the thesis I will show the results of my work on the generated outputs and models.
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