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Text recognition with artificial neural networks
Peřinová, Barbora ; Hesko, Branislav (referee) ; Mézl, Martin (advisor)
This master’s thesis deals with optical character recognition. The first part describes the basic types of optical character recognition tasks and divides algorithm into individual phases. For each phase the most commonly used methods are described in the next part. Within the character recognition phase the problematics of artificial neural networks and their usage in given phase is explained, specifically multilayer perceptron and convolutional neural networks. The second part deals with requirements definition for specific application to be used as feedback for robotic system. Convolution neural networks and CNTK library for deep learning using algorithm implementation in .NET is introduced. Finally, the test results of the individual phases of the proposed solution and the comparison with the open source Tesseract engine are discussed.
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Neural Network Library and Editor
Rouček, Martin ; Ježek, Pavel (advisor) ; Pešková, Klára (referee)
Neural network models are more often used in desktop applications given the increasing speed of computers. A very widespread platform for writing desktop applicatons is .NET Framework. Nevertheless, there is no neural networks library for the .NET Framework platform with a simple API and the possibility to work with library objects in a graphical interface. The author decided to create such a library. The main part of the thesis is a neural networks library GNNL that is initially limited to implementing two frequently used neural networks models which are a multilayer perceptron and self- organizing map together with learning algorithms of backpropagation and competitive learning. Graphical support of the library GNNL consists of a library GNNLV and neural network editor. The Library GNNLV contains the controls that allow working with GNNL library objects and a programmer can use them in his or hers application. The Neural network editor enables the programmer to create a neural network in a graphical interface, train it, analyze it, save it, and later use it in different applications. Text of the thesis focuses on analyzing and describing the implementation of the library with its graphical support. A major component of the text is a summary of neural networks theory for laics or programmers using library...
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Elliot Wave Detection
Kaleta, Marek ; Šperka, Svatopluk (referee) ; Petřík, Patrik (advisor)
This work deals with Elliott wave detection, which are statistical tool used to describe financial makret cycles and predict market trends. The work proposes methods to detect Elliott Waves and evaluetes them. From several methods of Elliott wave detection, Committee machines of multilayer perceptrons are used. Result of this work is a program, which detect Elliott impulse waves on input signal and builds hierarchy of Elliott waves.
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Neural Network Based Edge Detection
Křepský, Jan ; Grézl, František (referee) ; Švub, Miroslav (advisor)
Utilization of artificial neural networks in digital image processing is nothing new. The aim of this work is to design and implement neural network based edge detector and learn how effective this approach is for edge detection in images and to compare these results with common detectors. In theoretical part of my work I describe some methods of image pre-processing, common approach to edge detection and their thinning and I try to introduce basics for understanding artificial neural networks theory.
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Neural Network Based Edge Detection
Janda, Miloš ; Žák, Pavel (referee) ; Švub, Miroslav (advisor)
Aim of this thesis is description of neural network based edge detection methods that are substitute for classic methods of detection using edge operators. First chapters generally discussed the issues of image processing, edge detection and neural networks. The objective of the main part is to show process of generating synthetic images, extracting training datasets and discussing variants of suitable topologies of neural networks for purpose of edge detection. The last part of the thesis is dedicated to evaluating and measuring accuracy values of neural network.
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Recurrent Neural Networks in Computer Vision
Křepský, Jan ; Řezníček, Ivo (referee) ; Španěl, Michal (advisor)
The thesis concentrates on using recurrent neural networks in computer vision. The theoretical part describes the basic knowledge about artificial neural networks with focus on a recurrent architecture. There are presented some of possible applications of the recurrent neural networks which could be used for a solution of real problems. The practical part concentrates on face recognition from an image sequence using the Elman simple recurrent network. For training there are used the backpropagation and backpropagation through time algorithms.
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Comparison of Libraries of Artificial Neural Networks
Dohnal, Zdeněk ; Zbořil, František (referee) ; Dalecký, Štěpán (advisor)
This thesis is about comparison of libraries of artificial neural networks. Basic theory of neuron, neural networks and their learning algorithms are explained here. Multilayer perceptron, Self organizing map and Hopfield net are chosen for experiments. Criteria of comparison such as licence, community or last actualization are designed. Approximation of function, association and clustering are chosen as task for experiments. After that, there is implementation of applications using chosen libraries. At the end, result of comparison and experiment are evaluated.
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Analysis of Human Signature Based on Artificial Neural Network
Ševčík, Pavel ; Horák, Karel (referee) ; Pohl, Jan (advisor)
This bachelor thesis deals with methods of human signature and its analysis in practical service of artificial neural network. Actual processing and analysis of human signature consist in few steps. First of all, the signature pattern is digitized and processed with the assistance of preprocessing and segmentation methods. Afterwards, the object of human signature pattern is described with the assistance of centric geometric moments and moments invariant characteristics. Finally, the pattern is classified by multilayer perceptron, whose outputs determine the person, to that signature belongs to.
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Artificial neural network for modeling electromagnetic fields in a car
Kostka, Filip ; Škvor, Zbyněk (referee) ; Raida, Zbyněk (advisor)
The project deals with artificial neural networks. After designing and debugging the test data set and the training sample set, we created a multilayer perceptron network in the Neural NetworkToolbox (NNT) of Matlab. When creating networks, we used different training algorithms and algorithms improving the generalization of the network. When creating a radial basis network, we did not use the NNT, but a specific source code in Matlab was written. Functionality of neural networks was tested on simple training and testing patterns. Realistic training data were obtained by the simulation of twelve monoconic antennas operating in the frequency range from 2 to 6 GHz. Antennas were located inside a mathematical model of Octavia II. Using CST simulations, electromagnetic fields in a car were obtained. Trained networks are described by regressive characteristics andthe mean square error of training. Algorithms improving generalization are applied on the created and trained networks. The performance of individual networks is mutually compared.
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Comparison of selected classification methods for multivariate data
Stecenková, Marina ; Řezanková, Hana (advisor) ; Berka, Petr (referee)
The aim of this thesis is comparison of selected classification methods which are logistic regression (binary and multinominal), multilayer perceptron and classification trees, CHAID and CRT. The first part is reminiscent of the theoretical basis of these methods and explains the nature of parameters of the models. The next section applies the above classification methods to the six data sets and then compares the outputs of these methods. Particular emphasis is placed on the discriminatory power rating models, which a separate chapter is devoted to. Rating discriminatory power of the model is based on the overall accuracy, F-measure and size of the area under the ROC curve. The benefit of this work is not only a comparison of selected classification methods based on statistical models evaluating discriminatory power, but also an overview of the strengths and weaknesses of each method.
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