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Disparity Map Estimation from Stereo Image
Tábi, Roman ; Maršík, Lukáš (referee) ; Španěl, Michal (advisor)
The master thesis focuses on disparity map estimation using convolutional neural network. It discusses the problem of using convolutional neural networks for image comparison and disparity computation from stereo image as well as existing approaches of solutions for given problem. It also proposes and implements system that consists of convolutional neural network that measures the similarity between two image patches, and filtering and smoothing methods to improve the result disparity map. Experiments and results show, that the most quality disparity maps are computed using CNN on input patches with the size of 9x9 pixels combined with matching cost agregation and correction algorithm and bilateral filter.
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Semantic Segmentation in Mountainous Environment
Pelikán, Jakub ; Čadík, Martin (referee) ; Brejcha, Jan (advisor)
Semantic segmentation is one of classic computer vision problems and strong tool for machine processing and understanding of the scene. In this thesis we use semantic segmentation in mountainous environment. The main motivation of this work is to use semantic segmentation for automatic location of geographic position, where the picture was taken. In this thesis we evaluated actual methods of semantic segmentation and we chose three of them that are appropriate for adapting to mountainous environment. We split the dataset with mountainous environment into validation, train and test sets to use for training of chosen semantic segmentation methods. We trained models from chosen methods on mountainous data. We let segments from the best trained models get evaluated in electronic survey by respondents and we evaluated these segments in process of camera orientation estimation. We showed that chosen methods of semantic segmentation are possible to use in mountainous environment. Our models are trained on 11, 5 or 4 mountainous classes and the best of them achieve on 4 class mean IU 57.4%. Models are usable in practise. We show it by their deployment as a part of camera orientation estimation process.
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Convolution neural networks on the Windows platform
Kapusta, Martin ; Rajnoha, Martin (referee) ; Přinosil, Jiří (advisor)
The aim of the bachelor thesis is the latest knowledge of convolution neural networks and their application. The thesis describes the history, biological neuron and analogous mathematical model of a neuron. It also deals with the areas where neural networks are used, as well as the areas in which they expand gradually, the ways of learning and training, the differences between convolution neural networks and classical neural networks and their architecture. The thesis consists of two parts. The first part is the selection of the framework for working with convolution neural networks, which is suitable for implementation in the Windows operating system, the installation of the framework and its troubleshooting. The second part is aimed at creating an automated installation tool for the Windows 7 and Windows 10 operating system, created in JavaFX.
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Image segmentation using deeplearning methods
Lukačovič, Martin ; Burget, Radim (referee) ; Mašek, Jan (advisor)
This thesis deals with the current methods of semantic segmentation using deep learning. Other approaches of neaural networks in the area of deep learning are also discussed. It contains historical solutions of neural networks, their development, and basic principle. Convolutional neural networks are nowadays the most preferable networks in solving tasks as detection, classification, and image segmentation. The functionality was verified on a freely available environment based on conditional random fields as recurrent neural networks and compered with the deep convolutional neural networks using conditional random fields as postprocess. The latter mentioned method has become the basis for training of new models on two different datasets. There are various enviroments used to implement neural networks using deep learning, which offer diverse perform possibilities. For demonstration purposes a Python application leveraging the BVLC\,/\,Caffe framework was created. The best achieved accuracy of a trained model for clothing segmentation is 50,74\,\% and 68,52\,\% for segmentation of VOC objects. The application aims to allow interaction with image segmentation based on trained models.
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Depth Estimation by Convolutional Neural Networks
Ivanecký, Ján ; Španěl, Michal (referee) ; Hradiš, Michal (advisor)
This thesis deals with depth estimation using convolutional neural networks. I propose a three-part model as a solution to this problem. The model contains a global context network which estimates coarse depth structure of the scene, a gradient network which estimates depth gradients and a refining network which utilizes the outputs of previous two networks to produce the final depth map. Additionally, I present a normalized loss function for training neural networks. Applying normalized loss function results in better estimates of the scene's relative depth structure, however it results in a loss of information about the absolute scale of the scene.
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Deep Neural Networks for Person Identification
Duban, Michal ; Herout, Adam (referee) ; Hradiš, Michal (advisor)
This master's thesis deals with design and implementation of convolutional neural networks used in person re-identification. Implemented convolutional neural networks were tested on two datasets CUHK01 a CUHK03. Results, comparable with state of the art methods were acheved on these datasets. Designed networks were implemented in Caffe framework.
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Optical Character Recognition Using Convolutional Networks
Csóka, Pavel ; Behúň, Kamil (referee) ; Hradiš, Michal (advisor)
This thesis aims at creation of new datasets for text recognition machine learning tasks and experiments with convolutional neural networks on these datasets. It describes architecture of convolutional nets, difficulties of recognizing text from photographs and contemporary works using these networks. Next, creation of annotation, using Tesseract OCR, for dataset comprised from photos of document pages, taken by mobile phones, named Mobile Page Photos. From this dataset two additional are created by cropping characters out of its photos formatted as Street View House Numbers dataset. Dataset Mobile Nice Page Photos Characters contains readable characters and Mobile Page Photos Characters adds hardly readable and unreadable ones. Three models of convolutional nets are created and used for text recognition experiments on these datasets, which are also used for estimation of annotation error.
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Detection of Fire in Video
Poledník, Tomáš ; Hradiš, Michal (referee) ; Herout, Adam (advisor)
{This thesis deals with fire detection in video by colour analysis and machine learning, specifically deep convolutional neural networks, using Caffe framework. The aim is to create a vast set of data that could be used as the base element of machine learning detection and create a detector usable in real application. For the purposes of the project a set of tools for fire sequences creation, their segmentation and automatic labeling is proposed and created together with a large test set of short sequences with artificial modelled fire.
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Modul pro vyhledávání nevhodných obrázků
Žurek, Aleš
This work is focused on classifying photos which are uploaded on dating service Lidé.cz. Pictures are classified into two categories based on whether they contain pornographic content or not. Convolutional neural networks are used for classification and these neural networks are taught by using Caffe framework. The results of this work fulfilled all requirements from Seznam.cz, a.s. company. Classification accuracy of the best model on created testing dataset with 5643 photos was 93,64 % and the time for classification of photography is low enough to perform classification in real time. The first part contains an analysis of the current approaches for image classification. The second part focuses on the analysis and draft of the solution and the third part describes the implementation of the solution and the testing of neural networks models.
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