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Anticurtaining - Image Filter for Electron Microscopy
Dvořák, Martin ; Dobeš, Petr (referee) ; Zemčík, Pavel (advisor)
Tomographic analysis produces 3D images of examined material in nanoscale by focus ion beam (FIB). This thesis presents new approach to elimination of the curtain effect by machine learning method. Convolution neuron network is proposed for elimination of damaged imagine by the supervised learning technique. Designed network deals with features of damaged image, which are caused by wavelet transformation. The outcome is visually clear image. This thesis also designs creation of synthetic data set for training the neuron network which are created by simulating physical process of the creation of the real image. The simulation is made of creation of examined material by milling which is done by FIB and by process displaying of the surface by electron microscope (SEM). This newly created approach works precisely with real images. The qualitative evaluation of results is done by amateurs and experts of this problematic. It is done by anonymously comparing this solution to another method of eliminating curtaining effect. Solution presents new and promising approach to elimination of curtaining effect and contributes to a better procedure of dealing with images which are created during material analysis.
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Radar Signal Processing and Fusion of Information
Reich, Bořek ; Maršík, Lukáš (referee) ; Zemčík, Pavel (advisor)
This bachelor's thesis focuses on fusion of millimetr-wave radar and camera. It proposes appropriate procedure and usage of these sensors for object detection. Object detection in this bachelor's thesis is focused on people and provides additional information about detected person. It proposes convolution neural network as means of person detection and fusion of mmWave radar and camera data. When person is detected, distance of person from sensors is found in mmWave radar point cloud. Testing is performed on input data from both sensors in different situations, in poorly lit, unkwonwn scenes, with unknown people etc. Distance measuring is validated with reference data.
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Word2vec Models with Added Context Information
Šůstek, Martin ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This thesis is concerned with the explanation of the word2vec models. Even though word2vec was introduced recently (2013), many researchers have already tried to extend, understand or at least use the model because it provides surprisingly rich semantic information. This information is encoded in N-dim vector representation and can be recall by performing some operations over the algebra. As an addition, I suggest a model modifications in order to obtain different word representation. To achieve that, I use public picture datasets. This thesis also includes parts dedicated to word2vec extension based on convolution neural network.
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Trainable image segmentation using deep neural networks
Majtán, Martin ; Burget, Radim (referee) ; Harár, Pavol (advisor)
Diploma thesis is aimed to trainable image segmentation using deep neural networks. In the paper is explained the principle of digital image processing and image segmentation. In the paper is also explained the principle of artificial neural network, model of artificial neuron, training and activation of artificial neural network. In practical part of the paper is created an algorithm of sliding window to generate sub-images from image from magnetic rezonance. Generated sub-images are used to train, test and validate of the model of neural network. In practical part of the paper si created the model of the artificial neural network, which is used to trainable image segmentation. Model of the neural network is created using the Deeplearning4j library and it is optimized to parallel training using Spark library.
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Using advanced segmentation methods for images from TEM microscopes
Mocko, Štefan ; Chmelík, Jiří (referee) ; Potočňák, Tomáš (advisor)
Tato magisterská práce se zabývá využitím konvolučních neuronových sítí pro segmentační účely v oblasti transmisní elektronové mikroskopie. Také popisuje zvolenou topologii neuronové sítě - U-NET, použíté augmentační techniky a programové prostředí. Firma Thermo Fisher Scientific (dříve FEI Czech Republic s.r.o) poskytla obrazová data pro účely této práce. Získané segmentační výsledky jsou prezentovány ve formě křivek (ROC, PRC) a ve formě numerických hodnot (ARI, DSC, Chybová matice). Zvolená UNET topologie dosáhla excelentních výsledků v oblasti pixelové segmentace. S největší pravděpodobností, budou tyto výsledky sloužit jako odrazový můstek pro interní firemní výzkum.
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Captcha Code Recognition
Pazderka, Radek ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This bachelor thesis is dedicated to design and implementation of application , which's purpose is to recognize text CAPTCHA codes . It describes image processing algorithms , segmentation algorithms and character classification . Two different aproaches were used for classification . Convolution neural network LeNet and histogram classificator , which uses Pearson's correlation coefficient . Chosen classificators were tested on different CAPTCHA codes while finding out the success rate of recognition .
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Artificial intelligence for application services classification in network communication
Jelínek, Michael ; Fujdiak, Radek (referee) ; Blažek, Petr (advisor)
The master thesis focuses on the selection of a suitable algorithm for the classification of selected network traffic services and its implementation. The theoretical part describes the available classification approaches together with commonly used algorithms and selected network services. The practical part focuses on the preparation and preprocessing of the dataset, selection and optimization of the classification algorithm and verifying the classification capabilities of the algorithm in the various scenarios of the dataset.
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Computer Vision for Monitoring of 3D Printing
Heinz, Mikuláš ; Hradiš, Michal (referee) ; Smrž, Pavel (advisor)
This thesis deals with the automatic detection of errors that can occur during time-consuming 3D printing. It uses computer vision and artificial intelligence to achieve this. The main result is a system that uses Raspberry Pi and a connected camera to periodically record the printing process and sends the images to the user's computer for detection. On this computer, the image is analysed by a convolutional neural network model and information about found error is sent to the user via a SMTP protocol. The solution also includes a dataset with 385 images of 3D printing errors sorted by type.
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Neuroevolution Principles and Applications
Herec, Jan ; Strnadel, Josef (referee) ; Bidlo, Michal (advisor)
The theoretical part of this work deals with evolutionary algorithms (EA), neural networks (NN) and their synthesis in the form of neuroevolution. From a practical point of view, the aim of the work is to show the application of neuroevolution on two different tasks. The first task is the evolutionary design of the convolutional neural network (CNN) architecture that would be able to classify handwritten digits (from the MNIST dataset) with a high accurancy. The second task is the evolutionary optimization of neurocontroller for a simulated Falcon 9 rocket landing. Both tasks are computationally demanding and therefore have been solved on a supercomputer. As a part of the first task, it was possible to design such architectures which, when properly trained, achieve an accuracy of 99.49%. It turned out that it is possible to automate the design of high-quality architectures with the use of neuroevolution. Within the second task, the neuro-controller weights have been optimized so that, for defined initial conditions, the model of the Falcon booster can successfully land. Neuroevolution succeeded in both tasks.
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Detection of Traffic Signs and Lights
Chocholatý, Tomáš ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The thesis focuses on traffic sign detection and traffic lights detection in view with utilization convolution neural network. The goal is create suitable detector for detection and classification traffic sign in real traffic. For training of convolution neural network were created appropriate datasets, that contains synthetic and real dataset. For synthetic dataset was create generator, that can simulated different deformation of traffic signs. Evaluation is done by own program for quantitative evaluation. The detection rate successfully detected signs is 89\% over own test dataset. The results allow to find out importance of representation real or synthetic dataset in training dataset and influence individual deformations synthetic dataset for final detection quality.
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