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Generation of Authentic Latent Fingerprints Background
Gajda, Adam ; Goldmann, Tomáš (referee) ; Kanich, Ondřej (advisor)
This bachelor's thesis deals with the generation of authentic latent fingerprint backgrounds, through the use of deep learning, more specifically with the help of conditional generative adversarial network and other more conventional methods. This work summarizes the basic theoretical information about biometrics including synthetic fingerprints and a introduction into artificial intelligence. The main model proposed in this thesis has not come into fruition due to lack of unique training data. Other possible reasons were discussed. Thus an alternative way of generating latent fingerprint backgrounds was developed and after visual evaluation of the final results and real data the conclusion was positive.
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Object detection for video surveillance using the SSD approach
Dobranský, Marek ; Lokoč, Jakub (advisor) ; Božovský, Petr (referee)
The surveillance cameras serve various purposes ranging from security to traffic monitoring and marketing. However, with the increasing quantity of utilized cameras, manual video monitoring has become too laborious. In re- cent years, a lot of development in artificial intelligence has been focused on processing the video data automatically and then outputting the desired no- tifications and statistics. This thesis studies the state-of-the-art deep learning models for object detection in a surveillance video and takes an in-depth look at SSD architecture. We aim to enhance the performance of SSD by updating its underlying feature extraction network. We propose to replace the initially used VGG model by a selection of modern ResNet, Xception and NASNet classifica- tion networks. The experiments show that the ResNet50 model offers the best trade-off between speed and precision, while significantly outperforming VGG. With a series of modifications, we improved the Xception model to match the ResNet performance. On top of the architecture-based improvements, we ana- lyze the relationship between SSD and a number of detected classes and their selection. We also designed and implemented a new detector with the use of temporal context provided by the video frames. This detector delivers enhanced precision while...
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Converting prose into poetry using neural networks
Gokirmak, Memduh ; Popel, Martin (advisor) ; Dušek, Ondřej (referee)
Title: Converting Prose into Poetry with Neural Networks Author: Memduh Gokirmak Institute: Institute of Formal and Applied Linguistics Supervisor: Martin Popel, Institute of Formal and Applied Linguistics Abstract: We present here our attempts to create a system that generates poetry based on a sequence of text provided to it by a user. We explore the use of machine translation and language model technologies based on the neural network architecture. We use different types of data across three languages in our research, and employ and develop metrics to track the quality of the output of the systems we develop. We find that combining machine translation techniques to generate training data to this end with fine-tuning of pre-trained language models provides the most satisfactory generated poetry. Keywords: poetry machine translation language models iii
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Efficiency of deep convolutional neural networks on an elementary classification task
Prax, Jan ; Dobrovský, Ladislav (referee) ; Škrabánek, Pavel (advisor)
In this thesis deep convolutional neural networks models and feature descriptor models are compared. Feature descriptors are paired with suitable chosen classifier. These models are a part of machine learning therefore machine learning types are described in this thesis. Further these chosen models are described, and their basics and problems are explained. Hardware and software used for tests is listed and then test results and results summary is listed. Then comparison based on the validation accuracy and training time of these said models is done.
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Image data segmentation using deep neural networks
Hrdý, Martin ; Myška, Vojtěch (referee) ; Kiac, Martin (advisor)
The main aim of this master’s thesis is to get acquainted with the theory of the current segmentation methods, that use deep learning. Segmentation neural network that will be capable of segmenting individual instances of the objects will be proposed and created based on theoretical knowledge. The main focus of the segmentation neural network will be segmentation of electronic components from printed circuit boards.
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Deep Neural Networks in Image Processing
Ihnatchenko, Luka ; Mrázová, Iveta (advisor) ; Pilát, Martin (referee)
The goal of this master thesis was to propose a suitable strategy to detect and classify objects of interest in mammogram images. A part of this goal was to implement an experimentation framework, that will be used for data preparation, model training and comparison. Patch and full-image versions of the dataset were used in the analysis. Initialisation with weights that were pretrained on the images from other domain improved classifier performance. ResNet-34 had better AUC scores on the test set that ResNet-18. Semi-supervised training using entropy minimisation has no significant improvement over the supervised one. The thesis includes the visualisation of the network predictions and the analysis of the knowledge representation of the classier. The achieved results for a patch version of the dataset are comparable to the results of another article that utilised the same test set. For a full-image dataset the results of the training were suboptimal. 1
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Automatic Chord Recognition Using Deep Neural Networks
Nodžák, Petr ; Bidlo, Michal (referee) ; Vašíček, Zdeněk (advisor)
This work deals with automatic chord recognition using neural networks. The problem was separated into two subproblems. The first subproblem aims to experimental finding of most suitable solution for a acoustic model and the second one aims to experimental finding of most suitable solution for a language model. The problem was solved by iterative method. First a suboptimal solution of the first subproblem was found and then the second one. A total of 19 acoustic and 12 language models were made. Ten training datasets was created for acoustic models and three for language models. In total, over 200 models were trained. The best results were achieved on acoustic models represented by convolutional networks together with language models represented by recurent networks with LSTM modules.
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Synthetic Fingerprint Generation Using GAN
Dvořák, Jiří ; Drahanský, Martin (referee) ; Kanich, Ondřej (advisor)
Tato bakalářská práce se zabývá generováním syntetických otisků prstů za pomoci modelu založeném na principu generativních soupeřících sítí. Práce shrnuje základní teoretické informace z biometrie se zaměřením na otisky prstů. Zaobírá se také principem jednoho z populárních generátorů syntetických otisků prstů - nástrojem SFinGe. Práce představuje model postavený na hluboké konvoluční generativní soupeřící síti a představuje několik metod, které vedly ke zlepšení jeho výkonu. Vyhodnocení výsledků bylo provedeno výpočtem "Fréchet Inception Distance mezi vygenerovanými a existujími otisky. Dále byl vygenerován dataset obsahující 100 snímků. Ten byl vyhodnocen nástrojem NFIQ 2.0, který ukázal, že model je schopný generovat otisky prstů kvality srovnatelné s reálnými trénovacími daty.
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Document Quality Enhancement
Trčka, Jan ; Zemčík, Pavel (referee) ; Juránek, Roman (advisor)
The aim of this work is to increase the accuracy of the transcription of text documents. This work is mainly focused on texts printed on degraded materials such as newspapers or old books. To solve this problem, the current method and problems associated with text recognition are analyzed. Based on the acquired knowledge, the implemented method based on GAN network architecture is chosen. Experiments are a performer on these networks in order to find their appropriate size and their learning parameters. Subsequently, testing is performed to compare different learning methods and compare their results. Both training and testing is a performer on an artificial data set. Using implemented trained networks increases the transcription accuracy from 65.61 % for the raw damaged text lines to 93.23 % for lines processed by this network.
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Detection and Recognition of Gun in a Scene
Stuchlík, David ; Goldmann, Tomáš (referee) ; Drahanský, Martin (advisor)
The aim of the diploma thesis is to design an algorithm for detection and recognition of the type of gun in the image. Firstly, the existing methods and techniques for detecting the various objects are briefly introduced in the text of the thesis however, the methods are primarily focused on guns. Next, the basics of neural networks are briefly outlined, followed by an overview of the most common detectors for deep neural networks. The second half of the thesis is devoted to the implementation of an application for generating images based on a 3D model of a gun, the creation of a data file and learning of a neural network. Finally, the results obtained, which clearly indicate that in order to cover a huge variation of real weapons, is necessary to generate a large amount of training data based on many different 3D models, are briefly summarized in the conclusion of the thesis.
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