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Texture-Based Object Recognition
Wozniak, Jan ; Hradiš, Michal (referee) ; Španěl, Michal (advisor)
This thesis is focused on analysis of texture-based features and classi cation of known objects. The technical report provides basic outline of commonly used texture features and principles of their classifi cation, whereas narrower attention is dedicated to extraction of Local Binary Patterns and Support Vector Machine algorithm based classi er. This work also includes evaluation of attained results by statistical methods Jackkni ng and F-measure.
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Image Segmentation Using Height Maps
Moučka, Milan ; Kršek, Přemysl (referee) ; Španěl, Michal (advisor)
This thesis deals with image segmentation of volumetric medical data. It describes a well-known watershed technique that has received much attention in the field of medical image processing. An application for a direct segmentation of 3D data is proposed and further implemented by using ITK and VTK toolkits. Several kinds of pre-processing steps used before the watershed method are presented and evaluated. The obtained results are further compared against manually annotated datasets by means of the F-Measure and discussed.
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Texture-Based Object Recognition
Wozniak, Jan ; Hradiš, Michal (referee) ; Španěl, Michal (advisor)
This thesis is focused on analysis of texture-based features and classi cation of known objects. The technical report provides basic outline of commonly used texture features and principles of their classifi cation, whereas narrower attention is dedicated to extraction of Local Binary Patterns and Support Vector Machine algorithm based classi er. This work also includes evaluation of attained results by statistical methods Jackkni ng and F-measure.
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Image Segmentation Using Height Maps
Moučka, Milan ; Kršek, Přemysl (referee) ; Španěl, Michal (advisor)
This thesis deals with image segmentation of volumetric medical data. It describes a well-known watershed technique that has received much attention in the field of medical image processing. An application for a direct segmentation of 3D data is proposed and further implemented by using ITK and VTK toolkits. Several kinds of pre-processing steps used before the watershed method are presented and evaluated. The obtained results are further compared against manually annotated datasets by means of the F-Measure and discussed.
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Comparison of selected classification methods for multivariate data
Stecenková, Marina ; Řezanková, Hana (advisor) ; Berka, Petr (referee)
The aim of this thesis is comparison of selected classification methods which are logistic regression (binary and multinominal), multilayer perceptron and classification trees, CHAID and CRT. The first part is reminiscent of the theoretical basis of these methods and explains the nature of parameters of the models. The next section applies the above classification methods to the six data sets and then compares the outputs of these methods. Particular emphasis is placed on the discriminatory power rating models, which a separate chapter is devoted to. Rating discriminatory power of the model is based on the overall accuracy, F-measure and size of the area under the ROC curve. The benefit of this work is not only a comparison of selected classification methods based on statistical models evaluating discriminatory power, but also an overview of the strengths and weaknesses of each method.
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