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Titlebook: Image Processing and Communications Challenges 5; Ryszard S. Choras Conference proceedings 2014 Springer International Publishing Switzerl

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樓主: injurious
21#
發(fā)表于 2025-3-25 03:48:49 | 只看該作者
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發(fā)表于 2025-3-25 13:47:05 | 只看該作者
Piotr Lech,Krzysztof Okarma,Mateusz Tec?awerstanden wird, hat nicht immer existiert, weder dem Bewu?tsein noch dem Gegenstand nach. Die sogenannte ?Geschichtlichkeit des Menschen ‘ist kein überzeitliches Ph?nomen. Das historische Bewu?sein — mit Geschichte als Wissenschaft und Geschichtsphilosophie und Evolutionstheorie als Lehre der gesetz
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發(fā)表于 2025-3-26 02:57:40 | 只看該作者
Corneal Endothelial Grid Structure Factor Based on Coefficient of Variation of the Cell Sides Lengthxagonality (H) and the relative standard deviation of the cell surface (CV). Both the factors do not take into account the length measure of the grid cells, which has been presented in an article on the sample images. The authors propose an additional factor, the average relative standard deviation
27#
發(fā)表于 2025-3-26 04:35:47 | 只看該作者
A Distributed Approach for Development of Deformable Model-Based Segmentation Methodstion stages of the segmentation methods usually require a lot of time and resources, since the variations of the tested algorithms have to be constantly evaluated on many different data sets. To address this problem, we extended our web platform for development of deformable model-based methods with
28#
發(fā)表于 2025-3-26 09:49:32 | 只看該作者
Enhancement of Low-Dose CT Brain Scans Using Graph-Based Anisotropic Interpolationch affects image quality and hinders image segmentation. The method introduced in this paper reduces influence of the distortion by retrieval of pixel intensities under the ‘grains’ using graph-based anisotropic interpolation. Results of applying the introduced method to low-dose CT scans of hydroce
29#
發(fā)表于 2025-3-26 13:10:47 | 只看該作者
30#
發(fā)表于 2025-3-26 18:23:21 | 只看該作者
Preprocessing Using Maximal Autocovariance for Spatio–Temporal Track–Before–Detect Algorithmtation of the maximal autocovariance of signal. The differences are emphasized using Spatio–Temporal Track–Before–Detect algorithm so tracking such objects is possible. The possibilities of this technique are shown in a few illustrative examples and discussed. The detection of weak signals hidden in
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