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Visual Question Answering
Kocurek, Pavel ; Ondřej, Karel (referee) ; Fajčík, Martin (advisor)
Visual Question Answering (VQA) je systém, kde je vstupem obrázek s otázkou a výstupem je odpověď. Navzdory mnoha pokrokům ve výzkumu se VQA, na rozdíl od počítačově generovaných popisů obrázků, v praxi používá jen zřídka. Cílem této práce je zúžit mezeru mezi výzkumem a praxí. Z tohoto důvodu byla kontaktována komunita zrakově postižených a byla jim nabídnuta demonstrativní aplikace VQA a následně byla vytvořena mobilní aplikace. Byla provedena studie s 20 účastníky z komunity. Nejprve účastníci zkoušeli demonstrativní aplikaci po dobu dvou týdnů a následně byli požádáni o vyplnění dotazníku. 80 % respondentů hodnotilo přesnost aplikace VQA jako dostatečnou nebo lepší a většina z nich by ocenila, kdyby jejich aplikace pro generování popisů podporovala také VQA. Po tomto zjištění práce porovná získané znalosti z VQA se znalostmi z popisů v různých scénářích. Byla vytvořena datová sada 111 obrázků různorodých scén s ručně anotovanými popisky. Experiment porovnávající získané znalosti ukázal úspěšnost 69,9 % pro VQA a 46,2 % pro popisy obrázků. V dalším experimentu v 70,9 % případů účastníci vybrali správný popis za pomocí VQA. Výsledky naznačují, že pomocí VQA je možné zjistit více znalostí o detailech obrázků než je to v případě generovaných popisů.
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Radio Modulation Recognition Networks
Pijáčková, Kristýna ; Maršálek, Roman (referee) ; Götthans, Tomáš (advisor)
Bakalářská práce se zabývá klasifikací rádiových modulací pomocí metod hloubkového učení. V práci jsou navrženy čtyři architektury, kde tři z nich jsou tvořeny pomocí konvolučních a rekurentních neuronových sítí a čtvrtá využívá architekturu transformátorů. Při návrhu architektur byl brán v potaz výsledný počet parametrů jednotlivých sítí, který může výrazně ovlivňovat výslednou velikost sítě. Pro účely návrhu byl využit programovací jazyk Python a knihovna Keras, která umožňuje práci s neuronovými sítěmi. Výsledky práce jsou následně zhodnoceny a porovnány s výsledky sítí navržených v článcích zabývajících se tímto tématem.
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Convolutional Networks for Handwriting Recognition
Sladký, Jan ; Kišš, Martin (referee) ; Hradiš, Michal (advisor)
This thesis deals with handwriting recognition using convolutional neural networks. From the current methods, a network model was chosen to consist of convolutional and recurrent neural networks with the Connectist Temporal Classification. The Vertical Attention Module, which selects the relevant information in each column corresponding to the text in the figure was subsequently implemented in such a model. Then, this module was compared with other possibilities of vertical aggregation between convolutional and recurrent networks. The experiments took place on a data set containing over 80,000 lines of text from Czech letters from the 20th century. The results show that the Vertical Attention Module almost always achieves the best results on all used types of convolution networks. The resulting network achieved the best result with 8,9% of the character error rate. The contribution of this work is a neural network with a newly introduced element that can recognize lines of text.
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Deep Learning for OCR in GUI
Hamerník, Pavel ; Špaňhel, Jakub (referee) ; Lysek, Tomáš (advisor)
Optical character recognition (OCR) has been a topic of interest for many years. It is defined as the process of digitizing a document image into a sequence of characters. Despite decades of intense research, OCR systems with capabilities to that of human still remains an open challenge. In this work there is presented a design and implementation of such system, which is capable of detecting texts in graphical user interfaces.
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An automatic football match event detection
Dvonč, Tomáš ; Říha, Kamil (referee) ; Přinosil, Jiří (advisor)
This diploma thesis describes methods suitable for automatic detection of events from video sequences focused on football matches. The first part of the work is focused on the analysis and creation of procedures for extracting informations from available data. The second part deals with the implementation of selected methods and neural network algorithm for corner kick detection. Two experiments were performed in this work. The first captures static information from one image and the second is focused on detection from spatio-temporal data. The output of this work is a program for automatic event detection, which can be used to interpret the results of the experiments. This work may figure as a basis to gain new knowledge about the issue and also to the further development of detection events from football.
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Image based smoke and fire detection
Ďuriš, Denis ; Burda, Karel (referee) ; Přinosil, Jiří (advisor)
This diploma thesis deals with the detection of fire and smoke from the image signal. The approach of this work uses a combination of convolutional and recurrent neural network. Machine learning models created in this work contain inception modules and blocks of long short-term memory. The research part describes selected models of machine learning used in solving the problem of fire detection in static and dynamic image data. As part of the solution, a data set containing videos and still images used to train the designed neural networks was created. The results of this approach are evaluated in conclusion.
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Deep Learning for OCR in GUI
Hamerník, Pavel ; Špaňhel, Jakub (referee) ; Lysek, Tomáš (advisor)
Optical character recognition (OCR) has been a topic of interest for many years. It is defined as the process of digitizing a document image into a sequence of characters. Despite decades of intense research, OCR systems with capabilities to that of human still remains an open challenge. In this work there is presented a design and implementation of such system, which is capable of detecting texts in graphical user interfaces.
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Convolutional Networks for Historic Text Recognition
Vešelíny, Peter ; Kolář, Martin (referee) ; Kišš, Martin (advisor)
This thesis deals with text line recognition of historical documents. Historical texts dating back to the 17th - 19th centuries are written in fraktur typeface. The character recognition problem is solved using neural network architecture called sequence-to-sequence . This architecture is based on encoder-decoder model and contains attention mechanism. In this thesis a dataset, from texts originated from German archiv called Deutsches Textarchiv , was created. This archive contains 3 897 different German books that have available transcripts and corresponding images of pages. The created dataset was used to train and experiment with the proposed neural network. During the experiments, several convolutional models, hyperparameters and the effects of positional embedding were investigated. The final tool can recognize characters with accuracy 99,63 %. The contribution of this work is the~mentioned dataset and neural network, which can be used to recognize historical documents.
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Holistic License Plate Recognition Based on Convolution Neural Networks
Le, Hoang Anh ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
Main goal of this work was to create a holistic license plate reader, with an emphasis on achieving the highest possible accuracy on low quality images. Combination of convolutional and recurrent neural networks was designed and implemented, with usage of LSTM and CTC, where the inputs are cut-outs from the entire license plate. Competitive networks were also implemented to compare results. Networks were compared on a total of 4 datasets and the results were, that my design has achieved the best results with a recognition accuracy of 97.6%.
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Convolutional Networks for Historic Text Recognition
Kišš, Martin ; Zemčík, Pavel (referee) ; Hradiš, Michal (advisor)
The aim of this work is to create a tool for automatic transcription of historical documents. The work is mainly focused on the recognition of texts from the period of modern times written using font Fraktur. The problem is solved with a newly designed recurrent convolutional neural networks and a Spatial Transformer Network. Part of the solution is also an implemented generator of artificial historical texts. Using this generator, an artificial data set is created on which the convolutional neural network for line recognition is trained. This network is then tested on real historical lines of text on which the network achieves up to 89.0 % of character accuracy. The contribution of this work is primarily the newly designed neural network for text line recognition and the implemented artificial text generator, with which it is possible to train the neural network to recognize real historical lines of text.
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