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Mobile App for Recognition of Leukocoria in an Image of Human Face
Hřebíček, Pavel ; Kodym, Oldřich (referee) ; Herout, Adam (advisor)
The goal of this thesis is to design and implement a multiplatform multilingual mobile application for detecting leukocoria in an image of human face for iOS and Android platforms. Leukocoria is a whitish light of the pupil, which can be seen on the photo when the flash is used. Early detection of this symptom can save human eyesight. The application itself allows to analyze a user's photo and detect the presence of leukocoria. The goal of the application is to analyze eyes of the human, from which the mobile application name - Eye Check is derived. React Native framework was used to create a multiplatform mobile application. The Dlib library was chosen for human face and eye detection, the OpenCV library for working with the photo. The convolutional neural network was used to classify the eyes for the possible presence of leukocoria. Client-Server communication is solved using the REST architecture. The result is a mobile application that detects leukocoria and allerts the user to visit his doctor if leukocoria is detected.
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Deep learning methods for vessel and optic disc segmentation in ophthalmologic sequences
Rozhoňová, Andrea ; Odstrčilík, Jan (referee) ; Hesko, Branislav (advisor)
The aim of the following thesis was to study the issue of optical disc and retinal vessels segmentation in ophthalmologic sequences. The theoretical part of the thesis summarizes the principles of different approaches in the field of deep learning, which are used in connection with the given issue. Based on the theoretical part, methods for optical disk segmentation and retinal vessel segmentation based on the convolutional neural networks Linknet, PSPNet, Unet and MaskRCNN are proposed. The practical part of the thesis deals with the description of their implementation and subsequent evaluation.
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Food classification using deep neural networks
Kuvik, Michal ; Přinosil, Jiří (referee) ; Burget, Radim (advisor)
The aim of this thesis is to study problems of deep convolutional neural networks and the connected classification of images and to experiment with the architecture of particular network with the aim to get the most accurate results on the selected dataset. The thesis is divided into two parts, the first part theoretically outlines the properties and structure of neural networks and briefly introduces selected networks. The second part deals with experiments with this network, such as the impact of data augmentation, batch size and the impact of dropout layers on the accuracy of the network. Subsequently, all results are compared and discussed with the best result achieved an accuracy of 86, 44% on test data.
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Utilization of deep learning for channel estimation in OFDM systems
Hubík, Daniel ; Staněk, Miroslav (referee) ; Miloš, Jiří (advisor)
This paper describes a wireless communication model based on IEEE 802.11n. Typical methods for channel equalisation and estimation are described, such as the least squares method and the minimum mean square error method. Equalization based on deep learning was used as well. Coded and uncoded bit error rate was used as a performance identifier. Experiments with topology of the neural network has been performed. Programming languages such as MATLAB and Python were used in this work.
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Vehicle License Plate Detection and Recognition Software
Masaryk, Adam ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
The aim of this bachelor thesis is to design and develop software that can detect and recognize license plates from images. The software is divided into 3 parts - license plates detection, detector output processing and license plates characters recognition. We decided to implement detection and recognition using modern methods using convolutional neural networks.
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Neural network for style transfer
Kadlec, Filip ; Matoušek, Radomil (referee) ; Hůlka, Tomáš (advisor)
In this bachelor’s thesis we describe machine learning, types of artificial neural networks and internal processes of neural networks, such as feedforward data processing and training neural networks. We are also pursuing comparison and description of libraries (such as TensorFlow and Keras), which are suitable for neural networks implementation. In the practical part of thesis, we are dealing with problem called artistic style transfer with convolutional neural network.
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Fooling of Algorithms of Computer Vision
Hrabal, Matěj ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The goal of this work was to research existing methods of computer vision and computer recognition fooling. My focus was on group of methods called pixel attacks. Another part of my thesis talks about methods of detecting and fighting against computer vision fooling. Implementation of various pixel attack methods and methods of defending against these kinds of attacks was done using the python programming language and python library Keras. Solution that I have created works as standalone application allowing user to perform various pixel attack methods on chosen image. This tool also allows collection of statistics from performed pixel attacks and is able to detect possible attacks in these images.
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Fine-Grained Vehicle Recognition from Traffic Surveillance Camera
Mencner, Pavel ; Špaňhel, Jakub (referee) ; Sochor, Jakub (advisor)
The aim of this thesis is image based detection of vehicles from traffic surveillance camera and fine-grained vehicle type recognition (manufacturer and model). In the thesis the Unpack normalization method is implemented which transforms the vehicle image into its apparent flat representation in order to increase the classifier's success rate. The Unpack method make use of 3D bounding box of the vehicle. This bounding box is constructed during test period using the information of vehicle contour and direction toward vanishing points. The thesis involve accuracy comparison between direct and Unpack classification methods. The proposed solution is based on several related parts that benefit from convolutional neural networks. These parts are: vehicle detection from image data, estimation of the directions towards vanishing points solved as classification task, vehicle contour detection using convolutional Encoder-Decoder network and fine-grained vehicle type classification. Using Unpack based classification the 2% accuracy improvement against direct classification has been achieved, resulting in 86% overall success rate. The outcome of this thesis is fine-grained vehicle classification system that works with traffic surveillance video without any viewpoint limitations.
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Machine Translation Using Artificial Neural Networks
Holcner, Jonáš ; Beneš, Karel (referee) ; Szőke, Igor (advisor)
The goal of this thesis is to describe and build a system for neural machine translation. System is built with recurrent neural networks - encoder-decoder architecture in particular. The result is a nmt library used to conduct experiments with different model parameters. Results of the experiments are compared with system built with the statistical tool Moses.
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Deep Neural Networks for Classifying Objects in an Image
Mlynarič, Tomáš ; Zemčík, Pavel (referee) ; Hradiš, Michal (advisor)
This paper deals with classifying objects using deep neural networks. Whole scene segmentation was used as main algorithm for the classification purpose which works with video sequences and obtains information between two video frames. Optical flow was used for getting information from the video frames, based on which features maps of a~neural network are warped. Two neural network architectures were adjusted to work with videos and experimented with. Results of the experiments show, that using videos for image segmentation improves accuracy (IoU) compared to the same architecture working with images.
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