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Texture Characteristics
Zahradnik, Roman ; Šiler, Ondřej (referee) ; Švub, Miroslav (advisor)
Aim of this project is to evaluate effectivity of various texture features within the context of image processing, particulary the task of texture recognition and classification. My work focuses on comparing and discussion of usage and efficiency of texture features based on local binary patterns and co- ccurence matrices. As classification algorithm is concerned, cluster analysis was choosen.
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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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Methods for texture analysis in ophthalmologic images
Hanyášová, Lucie ; Szabó, Zoltán (referee) ; Kolář, Radim (advisor)
This thesis is focused on texture analysis methods. The project contains an overview of widely used methods. The main aim of the thesis is to develop a method for texture analysis of retinal images, which will be used for distinction of two patient groups, one with glaucoma eyes and one healthy. It is observed that glaucoma patients don´t have a texture on the eye ground. Preprocessing of the images is found by transfer of the image to different color spaces to achieve the best emphasis of the eye ground texture. Co-occurrence matrix is chosen for texture analysis of this data. The thesis contains detail description of the chosen solutions and feature discussion and the result is a list of features, which can be used for distinction between glaucoma and healthy eyes. The method is implemented in Matlab environment.
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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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Optical Character Recognition
Juřica, Dalibor ; Bartoň, Radek (referee) ; Švub, Miroslav (advisor)
The document is discussing the issue of the computer vision with ability to character recignition in the image. Wavelet transform is used for preprocessing the image. Pixel energy feature is firstly used for searchich candidate text pixels. Density region growing method is then used to collect candidate pixels to the separate regions, which will be candidate text regions. Several of the features are calculated over the regions and the SVM classifier is used to derive, if the region is really a text region or not.
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Texture Characteristics
Zahradnik, Roman ; Šiler, Ondřej (referee) ; Švub, Miroslav (advisor)
Aim of this project is to evaluate effectivity of various texture features within the context of image processing, particulary the task of texture recognition and classification. My work focuses on comparing and discussion of usage and efficiency of texture features based on local binary patterns and co- ccurence matrices. As classification algorithm is concerned, cluster analysis was choosen.
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Methods for texture analysis in ophthalmologic images
Hanyášová, Lucie ; Szabó, Zoltán (referee) ; Kolář, Radim (advisor)
This thesis is focused on texture analysis methods. The project contains an overview of widely used methods. The main aim of the thesis is to develop a method for texture analysis of retinal images, which will be used for distinction of two patient groups, one with glaucoma eyes and one healthy. It is observed that glaucoma patients don´t have a texture on the eye ground. Preprocessing of the images is found by transfer of the image to different color spaces to achieve the best emphasis of the eye ground texture. Co-occurrence matrix is chosen for texture analysis of this data. The thesis contains detail description of the chosen solutions and feature discussion and the result is a list of features, which can be used for distinction between glaucoma and healthy eyes. The method is implemented in Matlab environment.
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