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Learning Detectors by Tracking
Buchtela, Radim ; Beran, Vítězslav (referee) ; Hradiš, Michal (advisor)
This thesis is devoted to learn detectors by object tracking in video sequence. In this thesis, we discuss methods for object tracking, object detection and online learning and possibilities of their using in sophisticated techniques, which combine object tracking and online learning detectors.
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Deep Book Recommendation
Gráca, Martin ; Kolář, Martin (referee) ; Hradiš, Michal (advisor)
This thesis deals with the field of recommendation systems using deep neural networks and their use in book recommendation. There are the main traditional recommender systems analysed and their representations are summarized, as well as systems with more advanced techniques based on machine learning. The core of the thesis is to use convolutional neural networks for natural language processing and create a hybrid book recommendation system. Suggested system includes matrix factorization and make recommendation based on user ratings and book metadata, including texts descriptions. I designed two models, one with bag-of-words technique and one with convolutional neural network. Both of them defeat baseline methods. On the created data set, that was created from the Goodreads, model with CNN beats model with BOW.
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Convolutional Neural Networks for Emotion Recognition
Jileček, Jan ; Najman, Pavel (referee) ; Hradiš, Michal (advisor)
Convolutional neural networks are used for various tasks, but foremost in machine learning, in which they excel. This work is going to introduce some existing frameworks, other algorithms for recognition and then we describe the training dataset creation and the model for emotion recognition training process. Mentioned model has accuracy of 60%. It is used for emotion statistics retrieval from movie trailers. Model for genre recognition is created from those statistics and then finally used in our application for genre recognition of the input trailer, with best accuracy of 47%.
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Pulse Detection from Video
Matuszek, Martin ; Hradiš, Michal (referee) ; Szentandrási, István (advisor)
The aim of the Master's thesis was to study contemporary methods for human pulse detection from standard video and suggest a method, which can be used to detect the pulse. Approaches of detecting miniature changes between frames of a video are presented. Position changes of the feature points or changes in colour of some part of an image are detected. It capitalize on the fact that those changes are caused by the pulse of blood. The method for color changes magnification is selected as a base for pulse detector. Face regions of interest are analyzed to detect frequency of changes of intensity between frames. 1D signal is gained and its analysis leads to heart rate. Approach to create heat map of frequency changes is also presented.
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Short-Term Forecast Based on Image of Sky
Volf, Martin ; Španěl, Michal (referee) ; Hradiš, Michal (advisor)
The bachelor's thesis submitted is dedicated to weather forecast based only on a video stream. At first, basic weather information which the sky can provide are presented. Cloud types including their properties and methods which the sky can most efficiently describe are dealt with. At second, basic circumstances between weather information are discussed. The objective of this work is to prove accuracy of the methods used for gaining data from the video stream and to find out whether it could be possible to use them for forecasting the rain, air humidity and sunshine for the period of time one hour later.
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Automatic Delivery Note Transcription
Necpál, Dávid ; Kišš, Martin (referee) ; Hradiš, Michal (advisor)
This bachelor thesis aims to create a system for automatic transcription of delivery notes - documents with a fixed structure. The solution is divided into two parts. The first part is table lines detection and subsequent detection and extraction of cells, that contain required data. The second part is handwritten numeric characters recognition in the images of the cutted cells. The resulting system can detect cells with the required data with 100 % accuracy with well-scanned delivery notes, while the success rate of numerical character recognition is more than 95 % for individual characters and more than 92 % for entire character sequences. The benefit of this work is a system for automatic transcription of delivery notes, which provides faster and easier otherwise lengthy rewriting of the contents of delivery notes to the information system in the retail. By using this system, the employee saves more than 50 % of the time on each delivery note.
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Neural Networks for Automatic Table Recognition
Piwowarski, Lukáš ; Španěl, Michal (referee) ; Hradiš, Michal (advisor)
Tato práce seznamuje čtenáře se současnými technikami rozpoznávání tabulek, které se používají především k získávání informací z ručně psaných nebo tištěných historických tabulek. Představujeme také metodu založenou na grafové neuronové síti, která je inspirována představenými přístupy. Metoda se skládá ze tří fází: fáze inicializace grafu, fáze klasifikace uzlů/hran a fáze transformace grafu na text. Ve fázi inicializace grafu používáme algoritmus viditelnosti uzlů a OCR k vytvoření počáteční grafové reprezentace vstupní tabulky. Ve fázi klasifikace uzlů a hran jsou uzly a hrany klasifikovány a ve fázi transformace grafu na text zarovnáváme uzly grafu do mřížky, která je pak použita k vytvoření konečné textové reprezentace tabulky. Náš implementovaný model byl schopen dosáhnout přesnosti 68 % u detekce horizontálních sousedů, přesnosti 71 % u detekce vertikálních sousedů a přesnosti 83 % u detekce buněk na datové sadě ABP.
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Reconstruction of Sparse Sampled Images with Deep Learning
Le, Hoang Anh ; Hradiš, Michal (referee) ; Juránek, Roman (advisor)
The main goal of this thesis was to increase reconstruction quality of sparse sampled microscopic images by using neural networks. The thesis will cover various approaches for image reconstruction and will also include descriptions of implementations, which were used. Implementations will be evaluated based on quality of reconstruction, but also based on segmentation, which could be their main possible application.
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Efficient Image Tagging
Procházka, Václav ; Zemčík, Pavel (referee) ; Hradiš, Michal (advisor)
This thesis investigates efficient manual image tagging approaches. It specifically focuses on organising images into clusters depending on their content, and thus on simplifying the selection of similar photos. Such selections may be efficiently tagged with common tags. The thesis investigates known techniques for visualisation of image collections according to the image content, together with dimensionality reduction methods. The most suitable methods are considered and evaluated. The thesis proposes a novel method for presenting image collections on 2D displays which combines a timeline with similarity grouping (Timeline projection). This method utilizes t-Distributed Stochastic Neighbour Embedding (t-SNE) for otpimally projecting groupings in high dimensional feature spaces onto the low-dimensional screen. Various modifications of t-SNE and ways to combine it with the timeline are discussed and chosen combination is implemented as a web interface and is qualitatively evaluated in a user study. Possible directions of further research on the subject are suggested.
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Search for Duplicities of Groups of Photos
Václavík, Vojtěch ; Hradiš, Michal (referee) ; Zemčík, Pavel (advisor)
The bachelor's thesis aims to create an executable application, which finds duplicate photographs and divide them into groups. The groups can be edited based on specified parameters. For the identification and sorting of duplicate photographs we use EXIF metadata. The application was implemented in C++, graphical interface in Qt framework and the database communication uses SQLite library.
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