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Convolutional neural networks for identification of axial 2D slices in CT data
Vavřinová, Pavlína ; Harabiš, Vratislav (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the classification of axial 2D slices in CT patient’s data into six categories. The sphere of convolutional neural networks was used for this purpose. For a better understanding of this issue, the basics of neural networks and then the principles of deep learning including convolutional neural networks are explained at first. The AlexNet network was specifically selected for the intention of this identification, and it was tested on the created data set after being adaptated. The overall classification success rate was 86% ,after the final adjustments, a slight improvement was achieved and the identification success rate was 87%.
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Reinforcement learning for solving game algorithms
Daňhelová, Jana ; Uher, Václav (referee) ; Kolařík, Martin (advisor)
The bachelor thesis Reinforcement learning for solving game algorithms is divided into two distinct parts. The theoretical part describes and compares the fundamental methods of reinforcement learning with special attention to the methods of active learning – Q-learning and deep learning. In the practical part the deep q-learning technique is chosen for testing and applied to the case of the Snake game. The results are presented in the form of program written in Python programming language, which consists of the game environment created in PyGame, the model of convolutional neural network designed in Keras and agent playing the game. As an output of the program there are several types of datasets in CSV format. The gained data containing the values of parameters like number of epochs, accuracy, loss or the amount of the reward can later be used for further processing.
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Protection of sensitive data contained in images
Mezina, Anzhelika ; Rajnoha, Martin (referee) ; Burget, Radim (advisor)
Tato bakalářská práce je zaměřena na využití hlubokého učení v bezpečnostním problému úniku citlivých informací ve formě obrazových dat. Pokusem o vyřešení tohoto problému bylo použití Single Shot Multibox Detectoru (SSD) a plně propojené sítě, poslední je mnohem rychlejší než jiné metody a může být použitá v praxi, kde je potřeba velmi rychlé analýzy příchozí a odchozí informace, například analýzy provozu sítě. V první části práce jsou popsané metody, které mohou být použité pro detekci klíčových slov. Druhá část obsahuje popis experimentu a dosažených výsledků pro dva modely neuronových sítí: Single Shot Multibox Detector a plně propojené sítě. Druhý model dosahuje uspokojivých vlastností jak z pohledu času zpracování tak i přesnosti a lze jej použít v praxi.
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Detection of specific anatomical structures in CT data via convolutional neural networks
Kozlová, Dominika ; Jan, Jiří (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the issue of detection of anatomical structures in medical images using convolutional neural networks (CNN). At first there are described methods of machine learning, convolutional neural networks and selected methods for detection using CNN. In this work was created a database of annotated CT images of ten anatomical structures (head, heart, aorta, left and right lung, spine, liver, left and right kidney, spleen). A method for detecting these structures was designed, that contains two approaches of region proposals from image, CNN and postprocessing to obtain the detection result. The designed algorithm was implemented in the Python programming language using the TensorFlow library. Obtained results of validation of the network and the detection results are presented and discussed in the last chapter.
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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 for image segmentation. This theme encompasses the whole field of computer vision. Particular 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. Part of the thesis is an example of practical applications of image segmentation. An important part is the SYNTHIA database of images, where its properties are shown. At the end of the thesis, the terms and requirements for hardware performance and software needed for good network performance are described in more detail. The Keras framework has already been used, which already includes functions for working with neural networks.
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Number Plate Recognition using Deep Learning Techniques
Dobrovský, Ladislav ; Dvořák, Jiří (referee) ; Matoušek, Radomil (advisor)
Focus of this thesis is research of deep convolutional artificial neural networks and their usage as solution to automatic license plate recognition problem. After summary of theory and current trends the further direction of development has been chosen. Thesis investigates several types of convolutional neural networks and complex architecture of system of collaborating applications. In practical part is described implementation and results of experiments with assessment of networks’ suitability for practical use.
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Superresulution of photography using deep neural network
Holub, Jiří ; Přinosil, Jiří (referee) ; Burget, Radim (advisor)
This diploma thesis deals with image super-resolution with conservation of good quality. Firstly, there are described state of the art methods dealing with this problem, as well as principles of neural networks with focus on convolutional ones. Finally, there is described a few models of convolutional neural network for image super-resolution to double size, which have been trained, tested and compared on newly created database with pictures of people.
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Recurrent Neural Network for Text Classification
Myška, Vojtěch ; Kolařík, Martin (referee) ; Povoda, Lukáš (advisor)
Thesis deals with the proposal of the neural networks for classification of positive and negative texts. Development took place in the Python programming language. Design of deep neural network models was performed using the Keras high-level API and the TensorFlow numerical computation library. The computations were performed using GPU with support of the CUDA architecture. The final outcome of the thesis is linguistically independent neural network model for classifying texts at character level reaching up to 93,64% accuracy. Training and testing data were provided by multilingual and Yelp databases. The simulations were performed on 1200000 English, 12000 Czech, German and Spanish texts.
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Image based flower recognition
Jedlička, František ; Kříž, Petr (referee) ; Přinosil, Jiří (advisor)
This paper is focus on flowers recognition in an image and class classification. Theoretical part is focus on problematics of deep convolutional neural networks. The practical part if focuse on created flowers database, with which it is further worked on. The database conteins it total 13000 plant pictures of 26 spicies as cornflower, violet, gerbera, cha- momile, cornflower, liverwort, hawkweed, clover, carnation, lily of the valley, marguerite daisy, pansy, poppy, marigold, daffodil, dandelion, teasel, forget-me-not, rose, anemone, daisy, sunflower, snowdrop, ragwort, tulip and celandine. Next is in the paper described used neural network model Inception v3 for class classification. The resulting accuracy has been achieved 92%.
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Visual Question Answering
Hajič, Jakub ; Straka, Milan (advisor) ; Lokoč, Jakub (referee)
Visual Question Answering (VQA) is a recently proposed multimodal task in the general area of machine learning. The input to this task consists of a single image and an associated natural language question, and the output is the answer to that question. In this thesis we propose two incremental modifications to an existing model which won the VQA Challenge in 2016 using multimodal compact bilinear pooling (MCB), a novel way of combining modalities. First, we added the language attention mechanism, and on top of that we introduce an image attention mechanism focusing on objects detected in the image ("region attention"). We also experiment with ways of combining these in a single end- to-end model. The thesis describes the MCB model and our extensions and their two different implementations, and evaluates them on the original VQA challenge dataset for direct comparison with the original work. 1
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