LEADER 01177cam--2200385---450 001 990000371390203316 005 20211022135222.0 035 $a0037139 035 $aUSA010037139 035 $a(ALEPH)000037139USA01 035 $a0037139 100 $a20010323d1962----km-y0itay5003----ba 101 1 $aita$ceng 102 $aIT 105 $aaf||||||001yy 200 1 $aNascita della religione$fEdwin Oliver James$gtraduzione di Roberto Bosi 205 $a2. ed 210 $aMilano$cIl saggiatore$d1962 215 $a378 p., [56] carte di tav.$cill.$d24 cm 225 2 $a<> portolano$v1 410 $12001$a<> portolano$v1 454 0$12001$aPrehistoric religion$926134 606 0 $aReligioni primitive$2BNCF 676 $a201.42 700 1$aJAMES,$bEdwin Oliver$0159816 702 1$aBOSI,$bRoberto 801 0$aIT$bcba$gREICAT 912 $a990000371390203316 951 $aII.2. 157(Varie Coll. 157/1)$b48843 L.M.$cII.2.$d512427 951 $aII.2. 157a(Varie Coll. 157/1 bis)$b62845 L.M.$cII.2.$d512440 959 $aBK 969 $aUMA 979 $aPATTY$b90$c20010323$lUSA01$h0928 996 $aPrehistoric religion$926134 997 $aUNISA LEADER 02037nam0 22004333i 450 001 PMI0012873 005 20251003044309.0 010 $a9788895399560 100 $a20111125d2011 ||||0itac50 ba 101 | $aita 102 $ait 181 1$6z01$ai $bxxxe 182 1$6z01$an 200 1 $aˆL'‰innovazione vincente$fAdriano De Maio$gprefazione di Luigi Nicolais$gpostfazione di Gaetano Quagliariello 210 $aMilano$cBrioschi$d2011 215 $a301 p.$d24 cm. 606 $aAziende$xInnovazione tecnologica$xItalia$2FIR$3BVEC037888$9E 606 $aAmministrazione pubblica$xInnovazione tecnologica$xItalia$2FIR$3CFIC207258$9N 676 $a658.4063$9GESTIONE DEL CAMBIAMENTO. INNOVAZIONE INTRODOTTA DALLA DIREZIONE$v22 676 $a658.4063$9GESTIONE DEI CAMBIAMENTI. INNOVAZIONE INTRODOTTA DALLA DIREZIONE$v23 676 $a658.514$9GESTIONE DELLA PRODUZIONE. Uso della tecnologia$v20 676 $a658.570945$9$v21 696 $aImprese 696 $aPubblica amministrazione$aOrgani della pubblica amministrazione$aOrgani ordinari 699 $aAziende$yImprese 699 $aAmministrazione pubblica$yPubblica amministrazione 699 $aAmministrazione pubblica$yOrgani della pubblica amministrazione 699 $aAmministrazione pubblica$zOrgani ordinari 700 1$aDe Maio$b, Adriano$3AQ1V000074$4070$0460631 702 1$aNicolais$b, Luigi$3AQ1V005360 702 1$aQuagliariello$b, Gaetano$f <1960- >$3CFIV112513 801 3$aIT$bIT-000000$c20111125 850 $aIT-BN0095 901 $bNAP 01$cPOZZO LIB.$nVi sono collocati fondi di economia, periodici di ingegneria e scienze, periodici di economia e statistica e altri fondi comprendenti documenti di economia pervenuti in dono. 912 $aPMI0012873 950 0$aBiblioteca Centralizzata di Ateneo$c1 v.$d 01POZZO LIB.F. CORTI 377$e 01 0000117385E VMA 1 v.$fB $h20230707$i20230913 977 $a 01 996 $aInnovazione vincente$93551734 997 $aUNISANNIO LEADER 03742nam 22007215 450 001 9910253967503321 005 20251116145905.0 010 $a981-10-0631-8 024 7 $a10.1007/978-981-10-0631-9 035 $a(CKB)3710000000602478 035 $a(EBL)4427872 035 $a(SSID)ssj0001653998 035 $a(PQKBManifestationID)16433603 035 $a(PQKBTitleCode)TC0001653998 035 $a(PQKBWorkID)14982983 035 $a(PQKB)11285046 035 $a(DE-He213)978-981-10-0631-9 035 $a(MiAaPQ)EBC4427872 035 $a(PPN)192220039 035 $a(EXLCZ)993710000000602478 100 $a20160224d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBig Visual Data Analysis $eScene Classification and Geometric Labeling /$fby Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo 205 $a1st ed. 2016. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2016. 215 $a1 online resource (128 p.) 225 1 $aSpringerBriefs in Signal Processing,$x2196-4076 300 $aDescription based upon print version of record. 311 08$a981-10-0629-6 320 $aIncludes bibliographical references. 327 $aIntroduction -- Scene Understanding Datasets -- Indoor/Outdoor classi?cation with Multiple Experts -- Outdoor Scene Classi?cation Using Labeled Segments -- Global-Attributes Assisted Outdoor Scene Geometric Labeling -- Conclusion and Future Work. 330 $aThis book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoor scene classification, and outdoor scene layout estimation. It is illustrated with numerous natural and synthetic color images, and extensive statistical analysis is provided to help readers visualize big visual data distribution and the associated problems. Although there has been some research on big visual data analysis, little work has been published on big image data distribution analysis using the modern statistical approach described in this book. By presenting a complete methodology on big visual data analysis with three illustrative scene comprehension problems, it provides a generic framework that can be applied to other big visual data analysis tasks. 410 0$aSpringerBriefs in Signal Processing,$x2196-4076 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aOptical data processing 606 $aMathematics 606 $aVisualization 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aVisualization$3https://scigraph.springernature.com/ontologies/product-market-codes/M14034 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aOptical data processing. 615 0$aMathematics. 615 0$aVisualization. 615 14$aSignal, Image and Speech Processing. 615 24$aImage Processing and Computer Vision. 615 24$aVisualization. 676 $a620 700 $aChen$b Chen$4aut$4http://id.loc.gov/vocabulary/relators/aut$0761219 702 $aRen$b Yuzhuo$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aKuo$b C.-C. Jay$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910253967503321 996 $aBig Visual Data Analysis$92545039 997 $aUNINA