National Repository of Grey Literature 217 records found  1 - 10nextend  jump to record: Search took 0.01 seconds. 
Polygonal Mesh Segmentation
Švancár, Matúš ; Kodym, Oldřich (referee) ; Španěl, Michal (advisor)
This bachelor thesis analyzes and approaches the issue of segmentation of polygonal models. It presents a design of an interactive method inspired by the method described in the Interactive Mesh Segmentation Based on Feature Preserving Harmonic Field. The method uses graph-cut and is implemented as a web application. The application supports .obj and .stl file formats, allows the user to load a model, draw sketches representing foreground and background on the surface of the model, and to start segmentation. Once completed, the user can download the resulting models or continue segmenting with one of them.
Cloud Solution for 3D Models Processing
Klemens, Jakub ; Veľas, Martin (referee) ; Španěl, Michal (advisor)
This thesis deals with the possibilities of processing 3D models in cloud applications. A C++ library called Cloud3D has been designed and implemented. The resulting library is used to quickly create client-server applications. The library is divided into three separate parts: Client, Service Provider and Load Balancer. The service provider runs in the cloud and provides 3D model processing services to client applications. The biggest advantage of Cloud3D is the ease of creating new applications with its help. Other benefits include scalability, assured implementation of look-a-side Load Balancer, and security ensured by the use of SSL certification. 
Deep Learning for Medical Image Analysis
Bíl, Tomáš ; Kodym, Oldřich (referee) ; Španěl, Michal (advisor)
The goal of this thesis is developing convolutional neural network which is able to classify if x-ray images are suitable for cephalometry analysis. Four networks were created and trained on a dataset for this purpose. Two of them are VGG type, one is based on UNet and one is Resnet. The dataset was generated from ct scan images. VGG network with four blocks has got the best results.  Measured accuracy performed on test dataset is 97%.
Material Artefact Generation
Rončka, Martin ; Španěl, Michal (referee) ; Kodym, Oldřich (advisor)
Ne vždy je jednoduché získání dostatečně velké a kvalitní datové sady s obrázky zřetelných artefaktů, ať už kvůli nedostatku ze strany zdroje dat nebo složitosti tvorby anotací. To platí například pro radiologii, nebo také strojírenství. Abychom mohli využít moderní uznávané metody strojového učení které se využívají pro klasifikaci, segmentaci a detekci defektů, je potřeba aby byla datová sada dostatečně velká a vyvážená. Pro malé datové sady čelíme problémům jako je přeučení a slabost dat, které způsobují nesprávnou klasifikaci na úkor málo reprezentovaných tříd. Tato práce se zabývá prozkoumáváním využití generativních sítí pro rozšíření a vyvážení datové sady o nové vygenerované obrázky. Za použití sítí typu Conditional Generative Adversarial Networks (CGAN) a heuristického generátoru anotací jsme schopni generovat velké množství nových snímků součástek s defekty. Pro experimenty s generováním byla použita datová sada závitů. Dále byly použity dvě další datové sady keramiky a snímků z MRI (BraTS). Nad těmito dvěma datovými sadami je provedeno zhodnocení vlivu generovaných dat na učení a zhodnocení přínosu pro zlepšení klasifikace a segmentace.
Landmark Detection in Medical Images Using Deep Neural Networks
Škandera, Juraj ; Španěl, Michal (referee) ; Kodym, Oldřich (advisor)
This thesis deals with detection of anatomical landmarks from cephalometric X-ray images using convolutional neural networks. Program works with public available dataset, which consists of side X-ray images of skull. There are two architectures of convolutional neural networks proposed in this thesis.  The best architecture achieves accuracy of 73.22% for detection within 5 mm. Program is created in Python language with use of Tensorflow framework.
Deep Learning for Medical Image Analysis
Dronzeková, Michaela ; Kodym, Oldřich (referee) ; Španěl, Michal (advisor)
The purpose of this thesis is to use convolutional neural networks for X-ray image classification of human body. Four different architectures of neural networks have been created. They were trained and tested on three tasks: classification of front and lateral chest, classification of X-ray images into several different categories and classification of diseases in chest X-ray. ResNet and SEResNet architectures achieved the best results. SEResNet scored 99,49% accuracy in the first task, ResNet achieved 94,97% accuracy in the second task and SEResNet reached 31,53% in the third task with F1 measure as metrics for evaluating results.
Watermarking 3D Models
Stehlík, Václav ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This bachelor's thesis is dedicated to embedding and extraction of watermarks to/from 3D models represented by polygonal mesh. The goal is to choose a suitable method for watermark embedding and extraction to/from a 3D model for the purpose of implementing a tool of such capabilities. The resulting solution uses methods proposed in article A Novel Blind Robust Digital Watermarking on 3D Meshes . Used methods create two watermarks. 'OTC' watermark that changes vertices of the model and 'Zero' watermark that does not change the model at all. These watermarks provide robustness against attacks and modification such as translation, scaling, rotation, simplification, random noise, remeshing and vertex reordering. Used methods also preserve imperceivability of watermark thanks to minimal changes of the model. Methods are blind and do not require original model. The tool is implemented in a form of simple web application which allows user to embed and extract watermark to/from files in stl format. This work includes testing and evaluation of success and usability of the tool against various attacks on chosen test set of models.
Restoration of X-Ray Images with Geometric Blur
Sokol, Juraj ; Španěl, Michal (referee) ; Hradiš, Michal (advisor)
This thesis aims to compare various image restoration methods on x-ray images. These methods use point spread function to remove blur introduced in images. These methods are experimentally compared.
Searching for Similar 3D Models
Šťáva, Zdeněk ; Veľas, Martin (referee) ; Španěl, Michal (advisor)
This paper deals with searching similar 3D models in a database containing up to thousands of models. It focuses in particular on the comparison of existing descriptors used to describe 3D models and the subsequent evaluation of similarity between models. In particular, the descriptors Rotation Invariant Spherical Harmonics and 3D Zernike Descriptor are compared. It also describes the use of libraries to extract these descriptors and to design of various experiments with these libraries over several object databases. It examines the effect of scale, translation, deformation and rotation of different 3D models on the resulting descriptor and the overall accuracy of both selected methods. These results compare.
User Interface for ARTable and Microsoft Hololens
Bambušek, Daniel ; Španěl, Michal (referee) ; Kapinus, Michal (advisor)
Tato práce se zaměřuje na použitelnost brýlí Microsoft HoloLens pro rozšířenou realitu v prototypu pracoviště pro spolupráci člověka s robotem - "ARTable". Použití brýlí je demonstrováno vytvořeným uživatelským rozhraním, které pomáhá uživatelům lépe a rychleji porozumět systému ARTable. Umožňuje prostorově vizualizovat naučené programy, aniž by bylo nutné spouštět samotného robota. Uživatel je veden 3D animací jednotlivých programů a hlasem zařízení, což mu pomůže získat jasnou představu o tom, co by se stalo, pokud by program spustil přímo na robotovi. Implementované řešení také umožňuje interaktivně provést uživatele celým procesem programování robota. Použití brýlí umožňuje mimo jiné zobrazit cenné prostorové informace, například vidění robota, tedy zvýraznit ty objekty, které jsou robotem detekovány.

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