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Intelligent manipulation of laboratory objects using the ABB YuMi robot
Nevřiva, Václav ; Parák, Roman (referee) ; Matoušek, Radomil (advisor)
The aim of the master thesis is to design a laboratory station and a control program operated by a collaborative robot IRB 14000 YuMi using an integrated effector camera to identify laboratory objects and control the progress of the task. In the introductory part, collaborative robots are briefly introduced, the IRB 14000 on which the task is implemented and the RobotStudio development environment together with the IntegratedVision extension are described in more detail. The following chapters describe the laboratory task itself, its solution and testing of the designed program.
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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%.
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Image segmentation using machine learning
Matějek, Libor ; Frýza, Tomáš (referee) ; Bravenec, Tomáš (advisor)
This work deals with machine learning and its application in the field of image segmentation and object recognition. The thesis describes the basic terminology related to machine learning and data related to it. It also focuses on the biological nature of the neuron and its technological applications. The basic types of neural networks and the key convolutional neural network for image processing are described. The work also presents the used architectures of convolutional neural networks. Then follow the methods of image preprocessing before the convolutional network R-CNN. Subsequently, some of the datasets suitable for image recognition are analyzed. The implementation is then realized in Python with support for the PyTorch framework from Facebook.
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WaldBoost on GPU
Polok, Lukáš ; Mikolov, Tomáš (referee) ; Hradiš, Michal (advisor)
Image recognition and machine vision in general is quickly evolving field, due boom of cheap and powerful computation technologies. Image recognition has many different applications in wide spectrum of industries, ranging from communications trough security to entertainment. Algorithms for image recognition are still evolving and are often quite computationaly demanding. That is why some of authors deal with implementing the algorithms on specialized hardware accelerators. This work describes implementation of image recognition using the WaldBoost algorithm on the graphic accelerator (GPU) platform.
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Support for Sagrada Game on Mobile Phone with OS Android
Trněný, Jan ; Švec, Tomáš (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to create support application for board game Sagrada on mobile devices with OS Android. The solution consist of detection and recognition of the game pattern card and dices on board using the OpenCV library. Subsequently, support is provided, in any state of the game, for points calculation and rules checking for any number of players. These functions allow management of data for multiple users on one mobile device and help with faster rules checking and points calculation. Functions also help for beginners in understanding the game.
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Multi-Dimensional Language Models and Their Applications in Visual Arts
Dohnal, Marek ; Tomko, Martin (referee) ; Meduna, Alexandr (advisor)
This thesis is concerned with application of formal models, namely four-way and cellular automata, in the field of visual arts. New models were designed in order to recognize, modify, and transform an input grid comprised of tiles. These models are implemented in an application accepting an input grid, which is colourized and transformed in the serial art style of Victor Vasarely. The result of this work is a synthesis of an input grid and a colour reference into a video visualisation of the transformations carried out by the newly designed cellular automaton.
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Image segmentation using machine learning
Matějek, Libor ; Frýza, Tomáš (referee) ; Bravenec, Tomáš (advisor)
This work deals with machine learning and its application in the field of image segmentation and object recognition. The thesis describes the basic terminology related to machine learning and data related to it. It also focuses on the biological nature of the neuron and its technological applications. The basic types of neural networks and the key convolutional neural network for image processing are described. The work also presents the used architectures of convolutional neural networks. Then follow the methods of image preprocessing before the convolutional network R-CNN. Subsequently, some of the datasets suitable for image recognition are analyzed. The implementation is then realized in Python with support for the PyTorch framework from Facebook.
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Intelligent manipulation of laboratory objects using the ABB YuMi robot
Nevřiva, Václav ; Parák, Roman (referee) ; Matoušek, Radomil (advisor)
The aim of the master thesis is to design a laboratory station and a control program operated by a collaborative robot IRB 14000 YuMi using an integrated effector camera to identify laboratory objects and control the progress of the task. In the introductory part, collaborative robots are briefly introduced, the IRB 14000 on which the task is implemented and the RobotStudio development environment together with the IntegratedVision extension are described in more detail. The following chapters describe the laboratory task itself, its solution and testing of the designed program.
|
|
|
Support for Sagrada Game on Mobile Phone with OS Android
Trněný, Jan ; Švec, Tomáš (referee) ; Smrž, Pavel (advisor)
The aim of this thesis is to create support application for board game Sagrada on mobile devices with OS Android. The solution consist of detection and recognition of the game pattern card and dices on board using the OpenCV library. Subsequently, support is provided, in any state of the game, for points calculation and rules checking for any number of players. These functions allow management of data for multiple users on one mobile device and help with faster rules checking and points calculation. Functions also help for beginners in understanding the game.
|
|
|
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%.
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