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Methods of deep learning in image processing tasks
Polášková, Lenka ; Marcoň, Petr (referee) ; Mikulka, Jan (advisor)
The clue of learning to recognize objects using neural network lies in imitation of animal neural network's behavior. In spite the details of how brain works is not known yet, the teams consisting of scientists from various medical or technical professions are trying to search for them. Thanks to giants like Geoffrey Hinton science made a big progress in this domain. The convolutional networks which are based on animal model of optical system can be advantageously used for image segmentation and therefore they ware chosen for segmentation of tumor and edema from images of magnetic resonance. The models of artificial neural networks used in this work had achieved the 41\% of success in edema segmentation and 79\% in segmentation of tumor from brain issue.
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Retinal biometry with low resolution images
Smrčková, Markéta ; Odstrčilík, Jan (referee) ; Kolář, Radim (advisor)
This thesis attempts to find an alternative method for biometric identification using retinal images. First part is focused on the introduction to biometrics, human eye anatomy and methods used for retinal biometry. The essence of neural networks and deep learning methods is described as it will be used practically. In the last part of the thesis a chosen identification algorithm and its implementation is described and the results are presented.
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Deep Neural Network for Detection of Atrial Fibrillation
Budíková, Barbora ; Ronzhina, Marina (referee) ; Hejč, Jakub (advisor)
Atrial fibrillation is an arrhythmia commonly detected from ECG using its specific characteristics. An early detection of this arrhythmia is a key to prevention of more serious conditions. Nowadays, atrial fibrillation detection is being implemented more often using deep learning. This work presents detection of atrial fibrillation from 12lead ECG using deep convolutional network. In the first section, there is a theoretical context of this work, then there is a description of proposed algorithm. Detection is implemented by a program in Python in two variations and their accuracy is rated by Accuracy and F1 measure. Results of the work are being discussed, mutually compared and compared to other similar publications.
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Masked face detection
Malý, Ondřej ; Kříž, Petr (referee) ; Přinosil, Jiří (advisor)
The aim of this work is to study and test current methods for face detection on veiled faces and evaluate the results. In the first chapter, five selected methods are theoretically analyzed and in the second chapter the individual methods are evaluated, both for the Wider Face file and for the actual set of photos with veiled faces. Subsequently, the Dlib CNN method is improved for better detection of veiled faces and reprogrammed to detect the degree of veil from the tested image
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Counting Vehicles in Static Images
Zemánek, Ondřej ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
Tato práce se zaměřuje na problém počítání vozidel v statickém obraze bez znalosti geometrických vlastností scény. V rámci řešení bylo implementováno a natrénováno 5 architektur konvolučních neuronových sítí. Také byl pořízen rozsáhlý dataset s 19 310 snímky pořízených z 12pohledů a zachycujících 7 různých scén. Použité konvoluční sítě mapují vstupní vzorek na mapu hustoty vozidel, ze které lze získat jejich počet a lokalizaci v kontextu vstupního snímku. Hlavním přínosem této práce je porovnání a aplikace dosavadních nejlepších řešení pro počítání objektů v obraze. Většina z těchto architektur byla navržena pro počítání lidí v obraze, proto musely být uzpůsobeny pro potřeby počítání vozidel v statickém obraze. Natrénované modely jsou vyhodnoceny GAME metrikou na TRANCOS datasetu a na velkém spojeném datasetu. Dosažené výsledky všech modelů jsou následně popsány a porovnány.
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Image Compression with Neural Networks
Teuer, Lukáš ; Sochor, Jakub (referee) ; Hradiš, Michal (advisor)
This document describes image compression using different types of neural networks. Features of neural networks like convolutional and recurrent networks are also discussed here. The document contains detailed description of various neural network architectures and their inner workings. In addition, experiments are carried out on various neural network structures and parameters in order to find the most appropriate properties for image compression. Also, there are proposed new concepts for image compression using neural networks that are also immediately tested. Finally, a network of the best concepts and parts discovered during experimentation is designed.
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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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Image classification using deep learning
Hřebíček, Zdeněk ; Přinosil, Jiří (referee) ; Mašek, Jan (advisor)
This thesis deals with image object detection and its classification into classes. Classification is provided by models of framework for deep learning BVLC/Caffe. Object detection is provided by AlpacaDB/selectivesearch and belltailjp/selective_search_py algorithms. One of results of this thesis is modification and usage of deep convolutional neural network AlexNet in BVLC/Caffe framework. This model was trained with precision 51,75% for classification into 1 000 classes. Then it was modified and trained for classification into 20 classes with precision 75.50%. Contribution of this thesis is implementation of graphical interface for object detction and their classification into classes, which is implemented as aplication based on web server in Python language. Aplication integrates object detection algorithms mentioned abowe with classification with help of BVLC/Caffe. Resulting aplication can be used for both object detection (and classification) and for fast verification of any classification model of BVLC/Caffe. This aplication was published on server GitHub under license Apache 2.0 so it can be further implemented and used.
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Energy protocol recognition using artificial intelligence
Racka, Jan ; Holasová, Eva (referee) ; Bohačík, Antonín (advisor)
The master's thesis focuses on classification of secure network traffic of energy protocols using convolutional neural network. The theoretical part discusses the issues of neural networks and their use in network traffic classification. In addition, the energy protocols Modbus, IEC 104, TASE.2, DNP3, GOOSE, SMV, MMS, and the standard DLMS/COSEM are analyzed, including their security. In the subsequent practical part, a convolutional neural network is implemented to recognize the mentioned protocols in their secured versions. Unsecured traffic records from publicly available repositories and from traffic simulators of the mentioned protocols, and captured data in an energy polygon were used to train the neural network. TLS and GOOSE convertotrs were developed to obtain secured traffic, which ensured that the protocols using same security mechanisms were secured uniformly. The resulting secured traffic was preprocessed into a two-dimensional format and was presented as input to the neural network for learning. The input image was created from the application parts of packets of the energy protocol session and formatted to the 28 × 28 byte image. The resulting network accuracy on the test data was 95,75 %. Furthermore, the network was tested on real traffic in an energy polygon, where it correctly recognized several protocols. A classifier for the operational state of a station that communicates using IEC 104 secured with TLS was developed as part of a partial objective of the thesis. The task of the classifier was to recognize, using encrypted messages, the state of the tested station. The classifier consisted of a convolutional neural network, which were usinga two-dimensional image consisting of information from a sequence of five consecutive packets as input. The information consisted of the interarrival time between packets, the length of the TLS encrypted application data, and the encrypted application data up to size 64 B. To obtain enough data to train the convolutional network, a simulator of characteristic messages for each state was developed. The classifier showed an accuracy of 43,05 % on the test data after the learning phase. Next, the classifier underwent testing on the test stations, where it was able to distinguish normal state of the state from events, but could not distinguish certain events of similar nature from each other.
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Counting Vehicles in Static Images
Zemánek, Ondřej ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
Tato práce se zaměřuje na problém počítání vozidel v statickém obraze bez znalosti geometrických vlastností scény. V rámci řešení bylo implementováno a natrénováno 5 architektur konvolučních neuronových sítí. Také byl pořízen rozsáhlý dataset s 19 310 snímky pořízených z 12pohledů a zachycujících 7 různých scén. Použité konvoluční sítě mapují vstupní vzorek na mapu hustoty vozidel, ze které lze získat jejich počet a lokalizaci v kontextu vstupního snímku. Hlavním přínosem této práce je porovnání a aplikace dosavadních nejlepších řešení pro počítání objektů v obraze. Většina z těchto architektur byla navržena pro počítání lidí v obraze, proto musely být uzpůsobeny pro potřeby počítání vozidel v statickém obraze. Natrénované modely jsou vyhodnoceny GAME metrikou na TRANCOS datasetu a na velkém spojeném datasetu. Dosažené výsledky všech modelů jsou následně popsány a porovnány.
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