TY - GEN A1 - Tolkiehn, Günter-Ulrich T1 - Untersuchung der Lumineszenz von festem Xenon mit Hilfe von Emissions- und Anregungsspektroskopie im Temperaturbereich von 5K bis 75K T1 - Investigation of the luminescence of solid Xenon by menas of emission- and excitation-spectroscopy in the temperature range from 5K to 75K N2 - Verschiedene Eigenschaften der Emissions- und Anregungsspektren von festem Xenon wurden im Detail untersucht. Wesentliche Aspekte konnten gut mit Hilfe der Vorstellung der Diffusionsbewegung des freien n=1-Exzitons und dessen strahlungsloser Rekombination an Restgasatomen auf der Probenoberfläche erklärt werden. Eine wesentliche Änderung des Emissionsspektrums bei höheren Temperaturen konnte dokumentiert, jedoch mit Hilfe der bekannten Modelle nicht erklärt werden. N2 - Different features of the emission- and excitation-spectra of solid Xenon were invertigated in detail. Substantial aspects could be explained well by the concept of diffusion of the free n=1-exciton and its radiationless recombination at rest gas atoms on the sample surface. A substantial change of the emission spectrum at higher temperature was documented, but could not explained within the known models. KW - Exziton KW - Xenon KW - Lumineszenz KW - Synchrotronstrahlung KW - Hamburg / Deutsches Elektronen-Synchrotron KW - Monochromator KW - Vakuum-UV-Spektroskopie KW - Anregung KW - self-trapped exciton KW - excitation spectrum Y1 - 1976 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus-1745 ER - TY - CHAP A1 - Metzler, Volker H. A1 - Vandenhouten, Ralf A1 - Krone, Jörg A1 - Grebe, Reinhard T1 - Unsupervised image segementation by stochastic reconstruction T2 - Proceedings of SPIE N2 - To segment complex and versatile image data from different modalities it is almost impossible to achieve satisfying results without the consideration of contextual information. In this approach, image segmentation is regarded as a high- dimensional optimization task, that can be solved by stochastical methods like evolutionary algorithms (EA). Initially, the iterative algorithm is provided with a set of good-quality sample segmentations. An efficient EA-based learning strategy generates a segmentation for a given target image from the provided samples. This two-level process consists of a global image-based optimization whose convergence is enhanced by locally operating pixel-based Boltzmann processes which restrict the search space to reasonable subsets. The stochastic reconstruction extracts the relevant information from the samples in order to adapt it onto the current segmentation problem, which results in a consistent labeling for the target image. The algorithm works unsupervised, because the range of possible labels and their contextual interpretation is provided implicitly by the sample segmentations. To prove the usefulness of the method experimental results based on both, reproducible phantom images and physiological NMR scans are presented. Moreover, an analysis of the basic segmentation and convergence properties is provided. Y1 - 1998 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-9329 SN - 1996-756X VL - 3338 ER -