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Record Nr. |
UNICAMPANIAVAN00268301 |
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Autore |
Takesaki, Masamichi |
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Titolo |
1. / M. Takesaki |
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Pubbl/distr/stampa |
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New York, : Springer-Verlag, 1979 |
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Descrizione fisica |
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Soggetti |
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46-XX - Functional analysis [MSC 2020] |
46L05 - General theory of C*-algebras [MSC 2020] |
46L10 - General theory of von Neumann algebras [MSC 2020] |
46L35 - Classifications of C* -algebras [MSC 2020] |
46L51 - Noncommutative measure and integration [MSC 2020] |
46L53 - Noncommutative probability and statistics [MSC 2020] |
46L54 - Free probability and free operator algebras [MSC 2020] |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910346767303321 |
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Autore |
Stegmaier Johannes |
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Titolo |
New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty |
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Pubbl/distr/stampa |
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KIT Scientific Publishing, 2017 |
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ISBN |
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Descrizione fisica |
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1 online resource (XII, 243 p. p.) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images. |
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