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Road Sign Detection from Camera in Car
Dušek, Jan ; Sochor, Jakub (referee) ; Beran, Vítězslav (advisor)
This bachelor's thesis is focused on detection of traffic signs from image or video. Algorithms common for object detection will be introduced in the beginning. Description of object detection using histogram of oriented gradients and support vector machines will follow. Last part will present accomplished results.
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Detection of selected audio events in a real environment
Kowolowski, Alexander ; Burget, Radim (referee) ; Přinosil, Jiří (advisor)
This work deals with methods for the detection of dangerous events, in this case gunshots, in a real environment. First of all, a testing and training database of sounds from the MIVIA database was created. In this database, the files were contained in six versions of signal-to-noise ratio, so the subsequent testing of the selected methods took place for the various shuffled files, and it was found that some methods are more accurate for cleaner recordings than others, but less accurate for more noisy ones. For the typical feature extraction from the input sound, the mel-frequency cepstral coefficients method was always used. In the thesis, the methods of support vector machines and ensemble of a number of weak classifiers are gradually tested on the created databases. These methods are then further optimized, for example by using statistical variables, and after optimization they achieve better results, as expected. In the work, two scripts were created, where one created a training database and on this data trained the classifier and the other created the test database, tested the selected classifier and obtained the results. The results are processed by confusion matrix and several proportional variables such as accuracy, sensitivity, specificity and others are calculated. These results are always listed in the relevant chapter of the thesis in the tables and column charts and are properly commented on.
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Automatic Photography Categorization
Veľas, Martin ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This thesis deals with content based automatic photo categorization. The aim of the work is to create an application, which is would be able to achieve sufficient precision and computation speed of categorization. Basic solution involves detection of interesting points, extraction of feature vectors, creation of visual codebook by clustering, using k-means algorithm and representing visual codebook by k-dimensional tree. Photography is represented by bag of words - histogram of presence of visual words in a particular photo. Support vector machines (SVM) was used in role of classifier. Afterwards the basic solution is enhanced by dividing picture into cells, which are processed separately, computing color correlograms for advanced image description, extraction of feature vectors in opponent color space and soft assignment of visual words to extracted feature vectors. The end of this thesis concerns to experiments of of above mentioned techniques and evaluation of the results of image categorization on their usage.
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Scala Programming Language and Its Use for Data Analysis
Kohout, Tomáš ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
This thesis deals with comparing the Scala programming language with other commonly used languages for data analysis. These languages are evaluated on the basis of the following categories: data manipulation and visualization, machine learning and concurent processing capabilities. The evaluation then shows the strengths and weaknesses of Scala. The strengths will be demonstrated on application for email categorization.
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Internet Domain Risk Evaluation
Polišenský, Jan ; Homoliak, Ivan (referee) ; Očenášek, Pavel (advisor)
Internet domains play a crucial role in providing web services. It is necessary to be able to detect malicious domains. The aim of this thesis is to summarize current work on domain classification and develop improved system of classification. This system is implemented using support vector machines a neural networks and shows 96.3% accuracy on test data.
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Tool for Classification of Lifestyle Traits Based on Metagenomic Data from the Large Intestine
Kubica, Jan ; Hon, Jiří (referee) ; Smatana, Stanislav (advisor)
This thesis deals with analysis of human microbiome using metagenomic data from large intestine. The main focus is placed on bacteria composition in a sample on different taxonomic levels regarding the lifestyle traits of an individual. For this purpose, a tool for classification of several attributes was created. It considers attributes like diet type and eating habits (vegetarian, vegan, omnivore), gluten and lactose intolerance, body mass index, age or sex. From range of machine learning perspectives considering K Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machines (SVM) were used. Datasets for training and final evaluation of the classifier were taken from American Gut project. The thesis also focuses on particular problems with metagenomic datasets like its multidimensionality, sparsity, compositional character and class imbalance.
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Detection, Localization and Recognition of Traffic Signs
Svoboda, Tomáš ; Juránek, Roman (referee) ; Herout, Adam (advisor)
This master's thesis deals with the localization, detection and recognition of traffic signs. The possibilities of selection of areas with possible traffic signs occurrence are analysed. The properties of different kinds of features used for traffic signs recognition are described next. It focuses on the features based on histogram of oriented gradients. Some possible classifiers are discussed, in the first place the cascade of support vector machines, which are used in resulting system. A description of the system implementation and data sets for 5 types of traffic signs is part of this thesis. Many experiments were accomplished with created system. The results of the experiments are very good. New datasets were acquired from approximately 9 hours of processed video sequences. There are about 13 500 images in these datasets.
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Influence of parcellation atlas on quality of classification in patients with neurodegenerative dissease
Montilla, Michaela ; Lamoš, Martin (referee) ; Gajdoš, Martin (advisor)
The aim of the thesis is to define the dependency of the classification of patients affected by neurodegenerative diseases on the choice of the parcellation atlas. Part of this thesis is the application of the functional connectivity analysis and the calculation of graph metrics according to the method published by Olaf Sporns and Mikail Rubinov [1] on fMRI data measured at CEITEC MU. The application is preceded by the theoretical research of parcellation atlases for brain segmentation from fMRI frames and the research of mathematical methods for classification as well as classifiers of neurodegenerative diseases. The first chapters of the thesis brings a theoretical basis of knowledge from the field of magnetic and functional magnetic resonance imaging. The physical principles of the method, the conditions and the course of acquisition of image data are defined. The third chapter summarizes the graph metrics used in the diploma thesis for analyzing and classifying graphs. The paper presents a brief overview of the brain segmentation methods, with the focuse on the atlas-based segmentation. After a theoretical research of functional connectivity methods and mathematical classification methods, the findings were used for segmentation, calculation of graph metrics and for classification of fMRI images obtained from 96 subjects into the one of two classes using Binary classifications by support vector machines and linear discriminatory analysis. The data classified in this study was measured on patiens with Parkinson’s disease (PD), Alzheimer’s disease (AD), Mild cognitive impairment (MCI), a combination of PD and MCI and subjects belonging to the control group of healthy individuals. For pre-processing and analysis, the MATLAB environment, the SPM12 toolbox and The Brain Connectivity Toolbox were used.
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Automatic Content-Based Image Categorization
Němec, Ladislav ; Španěl, Michal (referee) ; Veľas, Martin (advisor)
This thesis deals with automatic content-based image classification. The main goal of this work is implementation of application which is able to perform this task automatically. The solution consists of variable system using local image features extraction and visual vocabulary built by k-means method. Bag Of Words representation is used as a global feature describing each image. Support Vector Machines - the final component of this system - perform the classification based on this representation. In the last chapter, the results of this experimental system are presented.
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