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Segmetation of tomographic data in 3D Slicer
Korčuška, Robert ; Dvořák, Pavel (referee) ; Mikulka, Jan (advisor)
This thesis contains basic theoretical information about SVM-based image segmentation and data classification. Basic information about 3D Slicer software are presented. Aspects of medical images segmentation are described. Workplan and implemetation of SVM method for MRI segmentation in 3D Slicer sofware as extension module is created. SVM method is compared with simple segmentation algorithms included in 3D Slicer. Quality of segmentation, based on SVM, tested on real subjects is experimentaly demonstrated.
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Response analysis of train track laboratory model
Heteš, Marek ; Věchet, Stanislav (referee) ; Kšica, Filip (advisor)
In terms of safety, railway tracks have to be kept in good condition. Early and accurate detection of track defects can save both time and money. This thesis deals with simulation of a passing train on laboratory apparatus, and the measurement of the generated response. Apparatus represents scaled down section of a railway track, and allows the simulation of defect formation. With the help of the obtained data, a suitable method for defect detection was created.
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Response analysis of train track laboratory model
Heteš, Marek ; Věchet, Stanislav (referee) ; Kšica, Filip (advisor)
In terms of safety, railway tracks have to be kept in good condition. Early and accurate detection of track defects can save both time and money. This thesis deals with simulation of a passing train on laboratory apparatus, and the measurement of the generated response. Apparatus represents scaled down section of a railway track, and allows the simulation of defect formation. With the help of the obtained data, a suitable method for defect detection was created.
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The Depth of Functional Data.
Nagy, Stanislav ; Hlubinka, Daniel (advisor) ; Omelka, Marek (referee)
The depth function (functional) is a modern nonparametric statistical analysis tool for (finite-dimensional) data with lots of practical applications. In the present work we focus on the possibilities of the extension of the depth concept onto a functional data case. In the case of finite-dimensional functional data the isomorphism between the functional space and the finite-dimensional Euclidean space will be utilized in order to introduce the induced functional data depths. A theorem about induced depths' properties will be proven and on several examples the possibilities and restraints of it's practical applications will be shown. Moreover, we describe and demonstrate the advantages and disadvantages of the established depth functionals used in the literature (Fraiman-Muniz depths and band depths). In order to facilitate the outcoming drawbacks of known depths, we propose new, K-band depth based on the inference extension from continuous to smooth functions. Several important properties of the K-band depth will be derived. On a final supervised classification simulation study the reasonability of practical use of the new approach will be shown. As a conclusion, the computational complexity of all presented depth functionals will be compared.
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The Depth of Functional Data.
Nagy, Stanislav ; Hlubinka, Daniel (advisor) ; Omelka, Marek (referee)
The depth function (functional) is a modern nonparametric statistical analysis tool for (finite-dimensional) data with lots of practical applications. In the present work we focus on the possibilities of the extension of the depth concept onto a functional data case. In the case of finite-dimensional functional data the isomorphism between the functional space and the finite-dimensional Euclidean space will be utilized in order to introduce the induced functional data depths. A theorem about induced depths' properties will be proven and on several examples the possibilities and restraints of it's practical applications will be shown. Moreover, we describe and demonstrate the advantages and disadvantages of the established depth functionals used in the literature (Fraiman-Muniz depths and band depths). In order to facilitate the outcoming drawbacks of known depths, we propose new, K-band depth based on the inference extension from continuous to smooth functions. Several important properties of the K-band depth will be derived. On a final supervised classification simulation study the reasonability of practical use of the new approach will be shown. As a conclusion, the computational complexity of all presented depth functionals will be compared.
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Segmetation of tomographic data in 3D Slicer
Korčuška, Robert ; Dvořák, Pavel (referee) ; Mikulka, Jan (advisor)
This thesis contains basic theoretical information about SVM-based image segmentation and data classification. Basic information about 3D Slicer software are presented. Aspects of medical images segmentation are described. Workplan and implemetation of SVM method for MRI segmentation in 3D Slicer sofware as extension module is created. SVM method is compared with simple segmentation algorithms included in 3D Slicer. Quality of segmentation, based on SVM, tested on real subjects is experimentaly demonstrated.
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