23 research outputs found
Week of Czech Youth
No full textSegment from Český zvukový týdeník Aktualita (Czech Aktualita Sound Newsreel) issue no. 29A from 1944 was shot during the Week of Czech Youth event organised by the Board of Trustees for the Education of Youth and held from 1 to 9 July. The programme included a concert held on Old Town Square on 8 July. The orchestra and choir consisted of several hundred young musicians and singers. Minister of Education and People´s Enlightenment and Chairman of the Board Emanuel Moravec and Deputy Mayor Joseph Pfitzner watched the event from the balcony of the Old Town Hall. The Board of Trustees´ youth set out from the square in a parade through the streets of Prague. The following day, a sports afternoon took place at Strahov Stadium. Guests of honour included Prime Minister Jaroslav Krejčí and the General Secretary of the Board František Teuner. Emanuel Moravec spoke to the participants. The programme included women´s floor exercises, track and field races and women in stylised costumes dancing to folk songs. The event was concluded with the athletes and audience paying homage to Adolf Hitler
Epidemiologie.
No full textDie Adipositas lässt sich epidemiologisch gut erheben, da zur Erfassung nur einfache Methoden angewendet werden müssen. Verlässlich sind allerdings nur Untersuchungen, in denen die Probanden auch wirklich gemessen und gewogen wurden; Selbstangaben von Patienten aufgrund von Befragungen sind weniger verlässlich. In den letzten Jahrzehnten hat die Adipositas in Industrienationen zu einer pandemischen Verbreitung geführt. Aufgrund der assoziierten Morbidität und Einschränkung der Lebensqualität hat die Adipositas auch eine erhebliche ökonomische Bedeutung, was für die Gesundheitspolitik und Kostenträger von Sozialleistungen von Interesse ist. Epidemiologische Daten tragen auch zu ätiologischen Erkenntnissen der Adipositas bei
The association between BMI and health-related quality of life in the US population: sex, age and ethnicity matters
No full textAn algorithm for the automatic identification of left ventricular internal wall edges in digital echocardiographic image sequences
No full textS.221 - 226Im vorliegenden Beitrag wird ein automatisiertes Verfahren zur Endokarderkennung in digitalen echokardiographischen Bildsequenzen vorgestellt. Das vorgeschlagene Verfahren gliedert sich modular in drei Verarbeitungsschritte auf und wurde in der Programmiersprache C unter UNIX implementiert. Es umfaßt die Verwendung eines anwendungsspezifisch entworfenen adaptiven Orts-Zeit-Filters für die Rauschreduktion in echokardiographischen Bildsequenzen, eine lokale 3-D-Grauwertäqualisation zur Kontrastanhebung und die Segmentierung des linken Ventrikels mit Hilfe eines Gebietswachstumsverfahrens. Beim Entwurf des adaptiven Orts-Zeit-Filters wurde in Betracht gezogen, daß das Hintergrundrauschen in tangentialer Richtung korreliert ist, verursacht durch die Ablenkung der Schallstrahlen beim Auftreffen auf Grenzflächen, die einen hohen Impedanzsprung aufweisen. Mit Hilfe des anwendungspezifisch entworfenen Filters wird das Hintergrundrauschen, ohne die ventrikularen Konturen zu degradieren, erfolgr eich reduziert. Die vorgestellten Simulationsergebnisse heben die Leistungsfähigkeit des vorgeschlagenen Verfahrens in exemplarischer Weise hervor.43Nr.7-
Automatic texture segmentation for content-based image retrieval application
No full textIn this article, a brief review on texture segmentation is presented, before a novel automatic texture segmentation algorithm is developed. The algorithm is based on a modified discrete wavelet frames and the mean shift algorithm. The proposed technique is tested on a range of textured images including composite texture images, synthetic texture images, real scene images as well as our main source of images, the museum images of various kinds. An extension to the automatic texture segmentation, a texture identifier is also introduced for integration into a retrieval system, providing an excellent approach to content-based image retrieval using texture features
Lubich convolution quadratures and their application to problems described by space-time BIEs
No full text
A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selection and classification
No full textWei N, Flaschel E, Friehs K, Nattkemper TW. A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selection and classification. BMC Bioinformatics. 2008;9(1):449.Background: Cell viability is one of the basic properties indicating the physiological state of the cell, thus, it has long been one of the major considerations in biotechnological applications. Conventional methods for extracting information about cell viability usually need reagents to be applied on the targeted cells. These reagent-based techniques are reliable and versatile, however, some of them might be invasive and even toxic to the target cells. In support of automated noninvasive assessment of cell viability, a machine vision system has been developed. Results: This system is based on supervised learning technique. It learns from images of certain kinds of cell populations and trains some classifiers. These trained classifiers are then employed to evaluate the images of given cell populations obtained via dark field microscopy. Wavelet decomposition is performed on the cell images. Energy and entropy are computed for each wavelet subimage as features. A feature selection algorithm is implemented to achieve better performance. Correlation between the results from the machine vision system and commonly accepted gold standards becomes stronger if wavelet features are utilized. The best performance is achieved with a selected subset of wavelet features. Conclusion: The machine vision system based on dark field microscopy in conjugation with supervised machine learning and wavelet feature selection automates the cell viability assessment, and yields comparable results to commonly accepted methods. Wavelet features are found to be suitable to describe the discriminative properties of the live and dead cells in viability classification. According to the analysis, live cells exhibit morphologically more details and are intracellularly more organized than dead ones, which display more homogeneous and diffuse gray values throughout the cells. Feature selection increases the system's performance. The reason lies in the fact that feature selection plays a role of excluding redundant or misleading information that may be contained in the raw data, and leads to better results
Optimierte primärärztliche Versorgung von Kindern und Jugendlichen mit psychischen Auffälligkeiten und Störungen - Evaluation von Qualität und Outcomes (PrimA-QuO)
No full textModeling of evolving textures using granulometries
No full textThis chapter describes a statistical approach to classification of dynamic texture images, called parallel evolution functions (PEFs). Traditional classification methods predict texture class membership using comparisons with a finite set of predefined texture classes and identify the closest class. However, where texture images arise from a dynamic texture evolving over time, estimation of a time state in a continuous evolutionary process is required instead. The PEF approach does this using regression modeling techniques to predict time state. It is a flexible approach which may be based on any suitable image features. Many textures are well suited to a morphological analysis and the PEF approach uses image texture features derived from a granulometric analysis of the image. The method is illustrated using both simulated images of Boolean processes and real images of corrosion. The PEF approach has particular advantages for training sets containing limited numbers of observations, which is the case in many real world industrial inspection scenarios and for which other methods can fail or perform badly. [41] G.W. Horgan, Mathematical morphology for analysing soil structure from images, European Journal of Soil Science, vol. 49, pp. 161–173, 1998. [42] G.W. Horgan, C.A. Reid and C.A. Glasbey, Biological image processing and enhancement, Image Processing and Analysis, A Practical Approach, R. Baldock and J. Graham, eds., Oxford University Press, Oxford, UK, pp. 37–67, 2000. [43] B.B. Hubbard, The World According to Wavelets: The Story of a Mathematical Technique in the Making, A.K. Peters Ltd., Wellesley, MA, 1995. [44] H. Iversen and T. Lonnestad. An evaluation of stochastic models for analysis and synthesis of gray-scale texture, Pattern Recognition Letters, vol. 15, pp. 575–585, 1994. [45] A.K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, vol. 24(12), pp. 1167–1186, 1991. [46] T. Jossang and F. Feder, The fractal characterization of rough surfaces, Physica Scripta, vol. T44, pp. 9–14, 1992. [47] A.K. Katsaggelos and T. Chun-Jen, Iterative image restoration, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 208–209, 2000. [48] M. K¨oppen, C.H. Nowack and G. R¨osel, Pareto-morphology for color image processing, Proceedings of SCIA99, 11th Scandinavian Conference on Image Analysis 1, Kangerlussuaq, Greenland, pp. 195–202, 1999. [49] S. Krishnamachari and R. Chellappa, Multiresolution Gauss-Markov random field models for texture segmentation, IEEE Transactions on Image Processing, vol. 6(2), pp. 251–267, 1997. [50] T. Kurita and N. Otsu, Texture classification by higher order local autocorrelation features, Proceedings of ACCV93, Asian Conference on Computer Vision, Osaka, pp. 175–178, 1993. [51] S.T. Kyvelidis, L. Lykouropoulos and N. Kouloumbi, Digital system for detecting, classifying, and fast retrieving corrosion generated defects, Journal of Coatings Technology, vol. 73(915), pp. 67–73, 2001. [52] Y. Liu, T. Zhao and J. Zhang, Learning multispectral texture features for cervical cancer detection, Proceedings of 2002 IEEE International Symposium on Biomedical Imaging: Macro to Nano, pp. 169–172, 2002. [53] G. McGunnigle and M.J. Chantler, Modeling deposition of surface texture, Electronics Letters, vol. 37(12), pp. 749–750, 2001. [54] J. McKenzie, S. Marshall, A.J. Gray and E.R. Dougherty, Morphological texture analysis using the texture evolution function, International Journal of Pattern Recognition and Artificial Intelligence, vol. 17(2), pp. 167–185, 2003. [55] J. McKenzie, Classification of dynamically evolving textures using evolution functions, Ph.D. Thesis, University of Strathclyde, UK, 2004. [56] S.G. Mallat, Multiresolution approximations and wavelet orthonormal bases of L2(R), Transactions of the American Mathematical Society, vol. 315, pp. 69–87, 1989. [57] S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 674–693, 1989. [58] B.S. Manjunath and W.Y. Ma, Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 837–842, 1996. [59] B.S. Manjunath, G.M. Haley and W.Y. Ma, Multiband techniques for texture classification and segmentation, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 367–381, 2000. [60] G. Matheron, Random Sets and Integral Geometry, Wiley Series in Probability and Mathematical Statistics, John Wiley and Sons, New York, 1975
