LEADER 00798nam0-22003131i-450 001 990003328040403321 005 20230112135723.0 035 $a000332804 035 $aFED01000332804 035 $a(Aleph)000332804FED01 035 $a000332804 100 $a20001010d1932----km-y0itay50------ba 101 0 $afre 102 $aIT 105 $ay-------001yy 200 1 $a<> France litteraire$fJospeh Poerio 205 $a33. ed 210 $aNapoli$cArdia$d1932 215 $aXXXVIII, 512 p.$d20 cm 676 $a220 700 1$aPoerio,$bJoseph$0132042 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003328040403321 952 $a220 POE$bLINGUE B050.309$fDECLI 959 $aDECLI 996 $aFrance litte?raire$91329336 997 $aUNINA DB $aING01 LEADER 01340nam0-2200325---450 001 9911015577303321 005 20250730142925.0 100 $a20250728g18581862km-y0itay50------ba 101 0 $afre 102 $aFR 105 $ay-------001yy 200 1 $aNouveau cours de minéralogie$ecomprenant la description de toutes les espčces minérales avec leurs applications directes aux arts$fpar Delafosse 210 $aParis$cRoret$d1858-1862 215 $a3 v.$d23 cm 307 $a1.: 546 p. ; 2.: 486 p. ; 3.: 628 p. 610 0 $aMineralogia 676 $a549$v23$zita 700 1$aDelafosse,$bGabriel$f<1796-1878>$01834314 801 0$aIT$bUNINA$gREICAT$2UNIMARC 856 4 $zVisualizza la versione elettronica in Österreichische Nationalbibliothek$uhttps://viewer.onb.ac.at/10B0F6E4/$e20250728 856 4 $zVisualizza la versione elettronica in Österreichische Nationalbibliothek$uhttps://viewer.onb.ac.at/10B0F6DB/$e20250728 856 4 $zVisualizza la versione elettronica in Österreichische Nationalbibliothek$uhttps://viewer.onb.ac.at/10B0F6C2/$e20250728 901 $aBK 912 $a9911015577303321 952 $aM 1 VII 13 (1$b25579$fNAP14 952 $aM 1 VII 13 (2$b25579$fNAP14 952 $aM 1 VII 13 (3$b25579$fNAP14 959 $aNAP14 996 $aNouveau cours de minéralogie$94409721 997 $aUNINA LEADER 01749nam 2200397z- 450 001 9910346767303321 005 20210211 010 $a1000060221 035 $a(CKB)4920000000100850 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/54603 035 $a(oapen)doab54603 035 $a(EXLCZ)994920000000100850 100 $a20202102d2017 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aNew Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty 210 $cKIT Scientific Publishing$d2017 215 $a1 online resource (XII, 243 p. p.) 311 08$a3-7315-0590-8 330 $aMultidimensional 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. 610 $a3D Bildanalyse 610 $aAlgorithmen 610 $aAlgorithms 610 $aData Mining 610 $aDevelopmental Biology 610 $aEntwicklungsbiologie 610 $aSoftware 610 $aSoftware3D Image Analysis 700 $aStegmaier$b Johannes$4auth$01323753 906 $aBOOK 912 $a9910346767303321 996 $aNew Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty$93035813 997 $aUNINA