The dissertation thesis deals with 3D reconstruction of the temporomandibular joint from 2D slices of tissue obtained by magnetic resonance. The current practice uses 2D MRI slices in diagnosing. 3D models have many advantages for the diagnosis, which are based on the knowledge of spatial information. Contemporary medicine uses 3D models of tissues, but with the temporomandibular joint tissues there is a problem with segmenting the articular disc. This small tissue, which has a low contrast and very similar statistical characteristics to its neighborhood, is very complicated to segment. For the segmentation of the articular disk new methods were developed based on the knowledge of the anatomy of the joint area of the disk and on the genetic-algorithm-based statistics. A set of 2D slices has different resolutions in the x-, y- and z-axes. An up-sampling algorithm, which seeks to preserve the shape properties of the tissue was developed to unify the resolutions in the axes. In the last phase of creating 3D models standard methods were used, but these methods for smoothing and decimating have different settings (number of polygons in the model, the number of iterations of the algorithm). As the aim of this thesis is to obtain the most precise model possible of the real tissue, it was necessary to establish an objective method by which it would be possible to set the algorithms so as to achieve the best compromise between the distortion and the model credibility achieve.

This thesis deals with extraction of data from cell images and their visualisation. Cell images are processed by FISH method. It discusses theory of diagnosis evaluation automation and cell features visualization. That concerns image processing, cell nuclei segmentation, feature extraction and data visualization.

Klasická inkoustová razítka, která se používají k autorizaci dokumentů, se dnes díky rozšíření moderních technologií dají relativně snadno padělat metodou oskenování a vytištění. V rámci diplomové práce je vyvíjen automatický nástroj pro ověření pravosti razítek, který najde využití zejména v prostředích, kde je nutné zpracovávat velké množství dokumentů. Procesu ověření pravosti razítka musí přirozeně předcházet jeho detekce v dokumentu - úloha zpracování obrazu, která zatím nemá přesvědčivé řešení. V této diplomové práci je navržena zcela nová metoda detekce a ověření pravosti razítka v barevných obrazech dokumentů. Tato metoda zahrnuje plnou segmentaci stránky za účelem určení kandidátních řešení, dále extrakci příznaků a následnou klasifikaci kandidátů za pomoci algoritmu podpůrných vektorů (SVM). Evaluace ukázala, že algoritmus umožňuje rozlišovat razítka od jiných barevných objektů v dokumentu jako jsou například loga a barevné nápisy. Kromě toho algoritmus dokáže rozlišit pravá razítka od kopií.

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.

This paper deals with application of neural networks in image segmentation. First part is an introduction to image processing and neural networks, second part describes an implementation of segmentation system and presents results of experiments. The segmentation system enables to use different types of classifiers, various image features extraction and also to evaluate the success of segmentation. Two classifiers were created - a neural network (self-organizing map) and an algorithm K-means. Colour (RGB and HSV) and texture features and their combinations were used for classification. Texture features were extracted using a set of Gabor filters. Experiments with designed classifiers and feature extractors were carried out and results were compared.

This master's thesis deals with proposal and implementation of system for detection of fingerprint area in image. The first task was to elaborate the theory which is necessary for understanding the image fingerprint area detection problems. It is also necessary to propose a specific system for image fingerprint area detection where it is possible to enhance or improve present methods or design a new one. The proposed system making use of selected method will be able to avoid all problems arising during fingerprint area detection. Description of proposed system implementation and testing on the fingerprint database is described in following part. In last part all the achieved results are discussed.

The clue of learning to recognize objects using neural network lies in imitation of animal neural network's behavior. In spite the details of how brain works is not known yet, the teams consisting of scientists from various medical or technical professions are trying to search for them. Thanks to giants like Geoffrey Hinton science made a big progress in this domain. The convolutional networks which are based on animal model of optical system can be advantageously used for image segmentation and therefore they ware chosen for segmentation of tumor and edema from images of magnetic resonance. The models of artificial neural networks used in this work had achieved the 41\% of success in edema segmentation and 79\% in segmentation of tumor from brain issue.

The thesis brings a project and an implementation of an interactive board game. The implementation can be divided into a detection part and a projection part. Detection, realized by an infra-red camera, serves as a means for recording players' responses. The players interact with the game through simple hand gestures. Projection screens a game plan, which includes the players' figures, on the table with the help of a data projector. The game chosen for projection is a role-playing game Dungeons & Dragons.

<p>Disertacija prikazuje novi algoritam za detekciju i izdvajanje registarskih tablica iz slike vozila koristeći fazi 2D Gaborov filtar. Parametri filtra: orijentacija i talasna dužina su fazifikovani u cilju optimizacije odziva Gaborovog filtra i postizanja dodatne selektivnosti filtra. Prethodno navedeni parametri dominiraju u rezultatu filtriranja. Bellova i trougaona funkcija pripadnosti pokazale su se kao najbolji izbor pri fazifikaciji parametara filtra. Algoritam je evaluiran nad vi&scaron;e baza slika i postignuti su zadovoljavajući rezultati. Komponente od interesa su efikasno izdvojene i postignuta značajna otpornost na &scaron;um i degradaciju na slici.</p> / <p>The thesis presents a new algorithm for detection and extraction of license plates from a vehicle image using a fuzzy two-dimensional Gabor filter. The filter parameters, orientation and wavelengths are fuzzified to optimize the Gabor filter&rsquo;s response and achieve a greater selectivity. It was concluded that Bell&rsquo;s function and triangular membership function are the most efficient methods for fuzzification. Algorithm was evaluated on several databases and has provided satisfactory results. The components of interest were efficiently extracted, and the procedure was found to be very noise-resistant.</p>

In cancer diagnosis there is a goal of having the treatment being tailored to each patient. This in order to increase efficiency and reduce side effects. Using more data on each patient can help in achieving this. One such data source is histological images on tissues, such as lymph nodes. This report sets out to find a method in which such images on lymph nodes can be automatically segmented. This so that they can later be analysed and maybe tell in what stage a cancer is in. Such work is today done by hand, and this makes it a subjective process, that might differ between doctors and institutions. If there was a method done by a computer, the process would be replicable and objective. Also, a lot of time would be saved. The results show that such a method is reachable in this early stage of development. It is also quite efficient when segmenting the lymph node itself. The segmentation of smaller areas of the lymph nodes is not as efficient, but with further work in the area it might improve enough to be useful. Some issues are still had since the method relies in part on a person to decide a parameter in order to get a clean segmentation. The final conclusion is that one model is to prefer compared to the others and that further work on this might make it a useful tool in analysing histological images.