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Preprocessing of retinal images aimed at support diagnosis of glaucoma
Holásková, Anna ; Walek, Petr (referee) ; Odstrčilík, Jan (advisor)
Preprocessing of retinal images can serve as a first phase of the further image analysis or the first step preceding diagnosing of various eye diseases. The preprocessing thus represents methods of image adjustments that can improve visual characteristics of fundus images. These methods mainly include the removal of noise generated during data acquisition, contrast and brightness transformations, edge detection and thresholding. This work handles with the basic methods of image preprocessing and specific methods of preprocessing of retinal images. The preprocessing includes global illumination correction, high-pass and homomorphic filtering and adaptive enhancement of the images. Manual methods for fundus image preprocessing that are usually based on the doctor's experience can be used as well. Hence, a procedure for enhancement of retinal images using Adobe Photoshop is mentioned in this work too. Three methods for preprocessing of fundus images were selected and implemented in MATLAB programming software. These methods include homomorphic filtering, CLAHE (Contrast Limited Adaptive Histogram Equalization) and adaptive enhancement. Experimental program functions were created and tested on the available image data. Results of the selected methods are mentioned in the conclusion section. Instructions for use of implemented functions are in appendix.
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Scene Analysis Based on the 2D Images
Hejtmánek, Martin ; Drahanský, Martin (referee) ; Orság, Filip (advisor)
This thesis deals with an object surface analysis in a simple scene represented by two-dimensional raster image. It summarizes the most common methods used within this branch of information technology and explains both their advantages and drawbacks. It introduces the design of an surface profile analysis algorithm based on the lighting analysis using knowledge and experiences from previous work. It contains a detailed description of the implemented algorithm and discusses the experimental results. It also brings up options for the possible enhancement of the projected algorithm.
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Preprocessing and Transformation of Text Data Collections
Maruna, Viktor ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with the issue of text-mining, mostly focused on preprocessing and transformation. In theoretical part there are contained information about development and principles of text-mining processes, text data collections and use in practice. The next part of this thesis describes in detail single steps of preprocessing and transformation of text data collections. In the final parts there are reviews of application development, testing and personal view on this thesis.
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Processing and Visualization of Mass Spectrums
Beneš, Ondřej ; Bendl, Jaroslav (referee) ; Martínek, Tomáš (advisor)
One of new techniques in the field of analytical chemistry, which has more and more practical use, is mass spectrometry imaging. With its ability to record representation of substances in samples during the tissue analyze arise problem with a lot of output data which needs to be handled programmatically. The goal of this work is to create an software for processing and visualization data of new standard imzML. As a part of the work, the field of mass spectrometry, primarily MALDI TOF mass spectrometry, is briefly introduced. There are also introduced some methods for mass spectrometry data preprocessing. The work also contains a summary of current state of available software for processing and visualization of mass spectrometry data. With requests from cooperating laboratory a novel software is designed and implemented, which besides the visualization itself, can preprocess the data for example data smoothing with Savitzky-Golay method, internal calibration or peak detection with continuous wavelet transformation. The software was successfully tested on real data sets.
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Neural Networks Classifier Design using Genetic Algorithm
Tomášek, Michal ; Vašíček, Zdeněk (referee) ; Mrázek, Vojtěch (advisor)
The aim of this work is the genetic design of neural networks, which are able to classify within various classification tasks. In order to create these neural networks, algorithm called NeuroEvolution of Augmenting Topologies (also known as NEAT) is used. Also the idea of preprocessing, which is included in implemented result, is proposed. The goal of preprocessing is to reduce the computational requirements for processing of benchmark datasets for classification accuracy. The result of this work is a set of experiments conducted over a data set for cancer cells detection and a database of handwritten digits MNIST. Classifiers generated for the cancer cells exhibits over 99 % accuracy and in experiment MNIST reduces computational requirements more than 10 % with bringing negligible error of size 0.17 %.
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Face detection and recognition with use of Raspberry Pi
Rozhoňová, Andrea ; Mézl, Martin (referee) ; Hesko, Branislav (advisor)
The following bachelor thesis is focused on the face detection and recognition in an image. The theoretical part divides methods of detection and recognition into several groups and there is better description and explanation of these methods in this part. At the end of the theoretical part is summarized the current utilization of person recognition on the bases of its face in practice. In the practical part is first implemented method for face detection. It is combination of two approaches - approach using haar features and approach using templates of eye. The face recognition is provided by the convolutional neural network. In conclusion there are summarized principles and problems associated with implementation on microcomputer Raspberry Pi and there is also evaluated the success of implemented methods.
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Realization of fingerprint scanner
Kovář, Martin ; Sekora, Jiří (referee) ; Mézl, Martin (advisor)
This master’s thesis deals with the issue of scanning human fingerprints, which is currently very topical and represents the most widespread biometric technology. The theoretical part of the work acquaints the reader with basics of dactyloscopy and biometrics and concerns technologies used for fingerprinting, image preprocessing methods and commercially available contactless optical scanners. The practical part is a realisation of a contactless optical scanner based on a Raspberry Pi minicomputer, implementation of preprocessing algorithms in Python and testing of the device from the perspective of image quality.
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Fast Visualization of Precise Shadows Using Precomputed Scene Geometry
Mikeš, Tibor ; Milet, Tomáš (referee) ; Pečiva, Jan (advisor)
The aim of this bachelor's thesis is to design and implement an effective method of rendering per-pixel correct hard shadows in scenes with static geometry. The principle of the method is in separate rendering of shadowed and lit surfaces. Whether a triangle is in shadow or not is known prior to its rasterization, which allows the renderer to omit per-fragment shadow calculations. Rendering a scene in this way requires it to be preprocessed. Therefore, two possible ways of preprocessing the scenes are described and implemented as well.
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Basic fingerprint liveness detection
Horák, Tomáš ; Smital, Lukáš (referee) ; Kašpar, Jakub (advisor)
Biometric data is unique, safe and often used to protect information. Even in some cases, detectors can be fooled, and therefore liveness is needed to check. This work focuses on the basic recognition of the liveliness of the fingerprint. Only one finger image is used. For perfect recognition, you need to compare the prints, so have more than one print, and that's why this detection is very demanding. Pre-processing was done with a binary segmentation mask. Methods using gray scale ratios, mean, standard deviations or histogram equalization were also used. All the methods were done on the LivDet 2011 database, in the Matlab environment and the goal was to recognize a live fingerprints from a false ones.
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Biometric fingerprint liveness detection
Jurek, Jakub ; Smital, Lukáš (referee) ; Vítek, Martin (advisor)
This project deals with general biometrics issues focusing on fingerprint biometrics, with description of dermal papillae and principles of fingerprint sensors. Next this work deals with fingerprint liveness detection issues, including description of methods of detection. Next this work describes chosen features for own detection, used database of fingerprints and own algorithm for image pre-processing. Furthermore neural network classifier for liveness detection with chosen features is decribed followed by statistic evaluation of the chosen features and detection results as well as description of the created graphical user interface.
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