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Visipedia - Embedding-driven Visual Feature Extraction and Learning
Jakeš, Jan ; Beran, Vítězslav (referee) ; Zemčík, Pavel (advisor)
Multidimenzionální indexování je účinným nástrojem pro zachycení podobností mezi objekty bez nutnosti jejich explicitní kategorizace. V posledních letech byla tato metoda hojně využívána pro anotaci objektů a tvořila významnou část publikací spojených s projektem Visipedia. Tato práce analyzuje možnosti strojového učení z multidimenzionálně indexovaných obrázků na základě jejich obrazových příznaků a přestavuje metody predikce multidimenzionálních souřadnic pro předem neznámé obrázky. Práce studuje příslušené algoritmy pro extrakci příznaků, analyzuje relevantní metody strojového účení a popisuje celý proces vývoje takového systému. Výsledný systém je pak otestován na dvou různých datasetech a provedené experimenty prezentují první výsledky pro úlohu svého druhu.
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Automatic Image Labelling
Lukáč, Michal ; Řezníček, Ivo (referee) ; Hradiš, Michal (advisor)
This thesis focuses on automatic image labelling to semantic categories. It describes the theory of classif cation and local features detection. It explains fundamental machine learning models used for image tagging, and how such models can be learned with Gradient descent. It propose solution with hierarchy for ImageNet and tagging images with attributes. MapReduce computing model is considered for learning on big data sets. In the last part it is described implementation, experimental and test results.
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Object Instance Search in Image Dataset
Medvec, Juraj ; Brejcha, Jan (referee) ; Beran, Vítězslav (advisor)
This project is focused on the selected techniques for image searching. In the introduction are presented all existing solutions, which are used in daily use. The other chapters are focused on creating resulting framework. On the end is introduced the demo application, which is using the framework for searching books by front page image.
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Raster Image Processing Using FPGA
Musil, Petr ; Kadlček, Filip (referee) ; Zemčík, Pavel (advisor)
This thesis describes the design and implementation of hardware unit to detect objects in the image. Design of unit is optimized for fast streaming processing. Object detection is performed by the trained classifiers using local image features. It describes a new technique for multi-scale detection. Detector used accelerating algorithm based on neighboring positions. The correct functionality of the detector is verified by simulation and part of a whole is implemented on development kit.
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Recognition of Weather in an Outdoor Stationary Camera View
Jenčo, Michal ; Juránková, Markéta (referee) ; Herout, Adam (advisor)
This thesis deals with classification of weather from stationary outdoor camera images with a landscape view. It classifies fog, clear, partly cloudy and overcast weather in particular. The problem was solved by computing five image flags and using machine learning. Hit rate of 95% was achieved with only small variances between weather types. The main finding of this work is that it is possible to successfully differentiate between the selected weather types with the chosen set of simple image flags. The implementation of this system enables plotting a graph of weather progression during a chosen day.
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Image analysis in tribodiagnostics
Machalík, Stanislav ; Stodola,, Jiří (referee) ; Tillová,, Eva (referee) ; Zemčík, Pavel (advisor)
Image analysis of wear particles is a suitable support tool for detail analysis of engine, gear, hydraulic and industrial oils. It allows to obtain information not only of basic parameters of abrasion particles but also data that would be very difficult to obtain using classical ways of evaluation. Based on the analysis of morphological or image characteristics of particles, the progress of wearing the machine parts out can be followed and, as a result, possible breakdown of the engine can be prevented or the optimum period for changing the oil can be determined. The aim of this paper is to explore the possibilities of using the image analysis combined with the method of analytical ferrography and suggest a tool for automated particle classification. Current methods of wear particle analysis are derived from the evaluation that does not offer an exact idea of processes that take place between the friction surfaces in the engine system. The work is based upon the method of analytical ferrography which allows to evaluate the state of the machine. The benefit of use of classifiers defined in this wirk is the possibility of automated evaluation of analytical ferrography outputs; the use of them eliminates the crucial disadvantage of ferrographical analysis which is its dependence on the subjective evaluation done by the expert who performs the analysis. Classifiers are defined as a result of using the methods of machine learning. Based on an extensive database of particles that was created in the first part of the work, the classifiers were trained as a result, they make the evaluation of ferrographically separated abrasion particles from oils taken from lubricated systems possible. In the next stage, experiments were carried out and optimum classifier settings were determined based on the results of the experiments.
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Pattern Recognition in Image Using Classifiers
Juránek, Roman ; Španěl, Michal (referee) ; Herout, Adam (advisor)
An AdaBoost algorithm for construction of strong classifier from several weak hypotesis will be presented in this work. Theoretical background of the algorithm and the method of construction of strong classifiers will be explained. WaldBoost extension to the algorithm will be described. The thesis deals with image features that are often used as element of weak classifiers. Brief introduction to pattern recognition in context of computer vision will be outlined in the begining of the work. Also some widely used methods of classifier training will be presented. An object detection library based on AdaBoost classifiers was developed as part of the work. The library was used in implementation of software that in praktice demonstrates object detection in videosquences. Last part of the work describes tool for training of AdaBoost classifiers.
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Detection of Vehicles in Image
Pomykal, Antonín ; Beran, Vítězslav (referee) ; Herout, Adam (advisor)
This work deals with the possibility of detection of cars in the image using the characteristics of cars with custom created image features , which are made pursuant to Haar-like features, and using methods of AdaBoost to train and their detection. We introduce the possibilities and types of custom picture features, OpenCV library, which was used in the implementation of the program, and we show the results and the success of this combination of detection algorithms.
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Automatic Photography Categorization
Gajová, Veronika ; Hradiš, Michal (referee) ; Španěl, Michal (advisor)
Purpose of this thesis is to design and implement a tool for automatic categorization of photos. The proposed tool is based on the Bag of Words classification method and it is realized as a plug-in for the XnView image viewer. The plug-in is able to classify a selected group of photos into predefined image categories. Subsequent notation of image categories is written directly into IPTC metadata of the picture as a keyword.
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Object Instance Search in Image Dataset
Medvec, Juraj ; Brejcha, Jan (referee) ; Beran, Vítězslav (advisor)
This project is focused on the selected techniques for image searching. In the introduction are presented all existing solutions, which are used in daily use. The other chapters are focused on creating resulting framework. On the end is introduced the demo application, which is using the framework for searching books by front page image.
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