National Repository of Grey Literature 61 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
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.
Regularization and variable selection in regression models
Lahodová, Kateřina ; Komárek, Arnošt (advisor) ; Maciak, Matúš (referee)
This diploma thesis focuses on regularization and variable selection in regres- sion models. Basics of penalised likelihood, generalized linear models and their evaluation and comparison based on prediction quality and variable selection are described. Methods called LASSO and LARS for variable selection in normal linear regression are briefly introduced. The main topic of this thesis is method called Boosting. General Boosting algorithm is introduced including functional gradient descent, followed by selection of base procedure, especially the componentwise linear least squares method. Two specific application of general Boosting algorithm are introduced with derivation of some important characteristics. These methods are AdaBoost for data with conditional binomial distribution and L2Boosting for condi- tional normal distribution. As a final point a simulation study comparing LASSO, LARS and L2Boosting methods was conducted. It is shown that methods LASSO and LARS are more suitable for variable selection whereas L2Boosting is more fitting for new data prediction.
Vehicle Classification Using Radar
Raszka, Aleš ; Zemčík, Pavel (referee) ; Maršík, Lukáš (advisor)
This Master thesis deals with usage of radar signal for vehicle classification. The thesis uses radar modules with continuous wave based on Doppler effect. Radar signal is processed by a series of signal processing method finished by Fourier transform. Data produced by FFT is used to create SVM and AdaBoost classifier which can be used to classify vehicles into groups.
Acceleration of Object Detection Using Classifiers
Juránek, Roman ; Zemčík, Pavel (advisor)
Detection of objects in computer vision is a complex task. One of most popular and well explored  approaches is the use of statistical classifiers and scanning windows. In this approach, classifiers learned by AdaBoost algorithm (or some modification) are often used as they achieve low error rates, high detection rates and they are suitable for detection in real-time applications. Object detection run-time which uses such classifiers can be implemented by various methods and properties of underlying architecture can be used for speed-up of the detection.  For the purpose of acceleration, graphics hardware, multi-core architectures, SIMD or other means can be used. The detection is often implemented on programmable hardware.  The contribution of this thesis is to introduce an optimization technique which enhances object detection performance with respect to an user defined cost function. The optimization balances computations of previously learned classifiers between two or more run-time implementations in order to minimize the cost function.  The optimization method is verified on a basic example -- division of a classifier to a pre-processing unit implemented in FPGA, and a post-processing unit in standard PC.
Image analysis in tribodiagnostics
Machalík, Stanislav ; 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.
Sharing Local Information for Faster Scanning-Window Object Detection
Hradiš, Michal ; Zemčík, Pavel (advisor)
This thesis aims to improve existing scanning-window object detectors by exploiting information shared among neighboring image windows. This goal is realized by two novel methods which are build on the ideas of Wald's Sequential Probability Ratio Test and WaldBoost. Early non-Maxima Suppression  moves non-maxima suppression decisions from a post-processing step to an early classification phase in order to make the decisions as soon as possible and thus avoid normally wasted computations. Neighborhood suppression enhances existing detectors with an ability to suppress evaluation at overlapping positions. The proposed methods are applicable to a wide range of detectors. Experiments show that both methods provide significantly better speed-precision trade-off compared to state-of-the-art WaldBoost detectors which process image windows independently. Additionally, the thesis presents results of extensive experiments which evaluate commonly used image features in several detection tasks and scenarios.
License Plate Detection and Recognition from Still Image
Janíček, Kryštof ; Sochor, Jakub (referee) ; Špaňhel, Jakub (advisor)
This thesis describes the design and implementation of system for detection and recognition of license plate. This system is divided into three parts which are license plate detection, character segmentation and optical character recognition. License plate detection is done by cascade classifier that achieves hit rate of 95.5% and precision rate of 95.9%. Character segmentation is based on contour finding that achieves hit rate of 93.3% and precision rate of 96.5%. Optical character recognition is done by neural network and achieves hit rate of 98.4% for individual characters. The whole system is able to detect and recognize up to 81.5% of license plates from the test data set.
Detection of racist symbols in pictures
Klapal, Matěj ; Říha, Kamil (referee) ; Povoda, Lukáš (advisor)
Goal of this thesis is detector of racist symbols from the picture using functions from the open source library OpenCV. Text also summarizes description of basic processes of image processing via computers. This text contains descriptions of some methods from the library allowing us to train and afterwards detect and localize requested object. This text also compares accuracy of detection using Haar-like features, Local Binary Patterns (LBP) and histogram of oriented gradients. Text also summarizes results of a test of detection for three supported symbols, swastika, signs of SS and triskelion.
Detection of groups of people in images
Mikulčík, Ondřej ; Zukal, Martin (referee) ; Číka, Petr (advisor)
This work describes two methods for detecting objects in images. The first method is the Viola-Jones, the second is the method of histograms oriented gradients. Start of work deals with the theoretical description of the methods. In the other parts of this work is presented creation of the training databases, implementation methods in the RapidMiner and their testing. In conclusion, the results and the use of methods for detection of groups of people in the database of images are evaluated.
Application of facial biometric data in recognition of persons
Bazala, Lukáš ; Přinosil, Jiří (referee) ; Burget, Radim (advisor)
he work deals with themes of detection and identification of human faces in an image. Described are the different biometric methods and work with biometric systems. Further, a problem of image processing is described and proposed a method for locating faces in an image and implementation of Viola-Jones detector in identifying key points in the face

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