LEADER 00913nam0-2200277 --450 001 9910673899803321 005 20230320162039.0 010 $a978-88-921-3167-5 020 $aIT$b2020-5316 100 $a20230320d2020----kmuy0itay5050 ba 101 0 $aita 102 $aIT 105 $a 001yy 200 1 $a<>mutuo nel sistema giuridico romanistico$eprofili di consensualità nel mutuo reale$fAntonio Saccoccio 210 $aTorino$cGiappichelli$d2020 215 $aXII, 241 p.$d24 cm 225 1 $aCollana del Dipartimento di giurisprudenza, Università degli studi di Brescia. Terza serie$v17 676 $a346.073$v23$zita 700 1$aSaccoccio,$bAntonio$0260159 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910673899803321 952 $aUNIV. 585 (17)$b2021/1606$fFGBC 959 $aFGBC 996 $aMutuo nel sistema giuridico romanistico$91762844 997 $aUNINA LEADER 02578nam 2200325 450 001 996674181003316 005 20250910123812.0 010 $a978-88-04-78339-8 100 $a20250910d2023----km y0itay5003 ba 101 1 $aita$cfre 102 $aIT 105 $aa 00 y 200 1 $a<>orso$estoria di un re decaduto$fMichel Pastoureau$gtraduzione di Chiara Bongiovanni Bertini 210 $aMilano$cMondadori$d2023 215 $aXXI, 348 p., [12] carte di tav.$cill.$d23 cm 225 2 $aOscar storia$v232 330 $aOgni cultura, in un certo momento della sua evoluzione, elegge un «re degli animali», quello che non può essere sconfitto da nessuno, e ne fa il protagonista del suo bestiario simbolico. In Europa, il re degli animali è stato a lungo l?orso: ammirato, venerato, considerato come un progenitore o un antenato dell?uomo. Non per nulla la prima statua modellata ? la statua di Montespan, risalente a 15-20.000 anni fa ? raffigura un orso! Ancora in età carolingia, in gran parte dell?Europa non mediterranea, l?orso era visto come una figura divina, un dio ancestrale il cui culto rimaneva ben radicato. La Chiesa doveva dichiarargli guerra, combatterlo con tutti i mezzi. E ciò fece, finendo per identificarlo con il diavolo tout court. Oltre i confini del Medioevo che con tanta determinazione l?aveva detronizzato, ormai privato di ogni prestigio, l?orso era divenuto una bestia da circo, umiliato e ridicolizzato. Eppure continuava a occupare un posto di primo piano nell?immaginario occidentale.A poco a poco, riapparve come oggetto di sogni e fantasia, fino a prendersi la sua rivincita nel Novecento, quando si è trasformato in un vero e proprio feticcio: l?orsacchiotto di peluche. Il grandioso animale è tornato a essere quello di decine di migliaia di anni fa: un compagno dell?uomo, un suo nume tutelare. Con stile brillante e profonda conoscenza dell?universo immaginifico medievale, Michel Pastoureau indaga tra le pieghe della storia e traccia in queste pagine una vicenda avventurosa, per ricostruire il plurimillenario rapporto tra l?uomo e l?orso, tra natura e cultura. (Fonte: editore) 410 0$aOscar storia$v232 500 10$a<>ours$924676 606 0 $aFolclore$xTemi [:] Orsi$2BNCF 676 $a398.369978 700 1$aPASTOUREAU,$bMichel$0211701 702 1$aBONGIOVANNI,$bChiara 801 0$aIT$bcba$gREICAT 912 $a996674181003316 951 $aIII.2. 1808$b292652 L.M.$cIII.2.$d573477 959 $aBK 969 $aUMA 996 $aOurs$924676 997 $aUNISA LEADER 06404nam 22007215 450 001 996660361303316 005 20250516130253.0 010 $a3-031-88977-0 024 7 $a10.1007/978-3-031-88977-6 035 $a(CKB)38859163200041 035 $a(DE-He213)978-3-031-88977-6 035 $a(MiAaPQ)EBC32123549 035 $a(Au-PeEL)EBL32123549 035 $a(EXLCZ)9938859163200041 100 $a20250516d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSupervised and Semi-supervised Multi-structure Segmentation and Landmark Detection in Dental Data $eMICCAI 2024 Challenges: ToothFairy 2024, 3DTeethLand 2024, and STS 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings /$fedited by Yaqi Wang, Dahong Qian, Shuai Wang, Achraf Ben-Hamadou, Sergi Pujades, Luca Lumetti, Costantino Grana, Federico Bolelli 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (XVII, 242 p. 77 illus., 72 illus. in color.) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v15571 311 08$a3-031-88976-2 327 $aToothFairy2: Multi-Structure Segmentation in CBCT Volumes -- Inferior Alveolar Nerve Segmentation in CBCT Images Using Connectivity-based Selective Re-training -- Scaling nnU-Net for CBCT Segmentation -- DiENTeS: Dynamic ENTity Segmentation with Local-Global Transformers -- Enhanced Multi-Structure Segmentation in CBCT Images with Adaptive Structure Optimization -- Weakly-Supervised Convolutional Neural Networks for Inferior Alveolar Nerve Segmentation in CBCT images -- A Multi-Axial Network for Oral Structural Segmentation -- Automatic Multi-Structure Segmentation in Cone Beam Computed Tomography Volumes Using Deep Encoder-Decoder Architectures -- Video Foundation Model for Medical 3D Segmentation -- STS: Semi-supervised Teeth Segmentation -- A Two-Stage Semi-Supervised nnU-Net Model for Automated Tooth Segmentation in Panoramic X-ray Images -- Two-Stage Semi-Supervised nnU-Net Framework for Tooth Segmentation in CBCT Images -- SemiT-SAM: Building a Visual Foundation Model for Tooth Instance Segmentation on Panoramic Radiographs -- Multi-stage Dental Visual Detection Based on YOLOv8: Dental 3D CBCT -- Efficient Semi-Supervised Tooth Instance Segmentation in Panoramic X-rays Using ResUnet50 and SAM Networks -- DAE-Net: Dual Attention Embedding-based Tooth Instance Segmentation Approach for Panoramic X-ray Images -- A Self-Training Pipeline for Semi-Supervised 2D Teeth Instance Segmentation -- Deformable Inherent Consistent Learning Network for Accurate Tooth Segmentation in Dental Panoramic Radiographs -- Semi-Supervised 2D Dental Image Segmentation via Cross Teaching Network -- A Novel Two-Stage Approach for 3D Dental Tooth Instance Segmentation -- 3DTeethLand24: 3D Teeth Landmarks Detection Challenge -- A Two-Stage Framework with Dual-Branch Network for End-to-End 3D Tooth Landmark Detection -- Leveraging Point Transformers for Detecting Anatomical Landmarks in Digital Dentistry -- ToothInstanceNet: Comprehensive Information from Intra-Oral Scans by Integration of Large-Context and High-Resolution Predictions. 330 $aThis book constitutes three challenges that were held in conjunction with the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, on October 6, 2024: ToothFairy challenge(ToothFairy2: Multi-Structure Segmentation in CBCT Volumes), Semi-supervised Teeth Segmentation (STS 2024), and the 3DTeethLand (3D Teeth Landmarks Detection Challenge). The 21 papers presented in this volume were carefully reviewed and selected from 28 submissions. ToothFairy challenges focused on the development of deep learning frameworks to segment anatomical structures in CBCTs by incrementally extending the amount of publicly available 3D-annotated CBCT scans and providing the first publicly available fully annotated datasets. The STS Challenge promoted the development of teeth segmentation in panoramic X-ray images and CBCT scans. It also provided instance annotations for different teeth, including pertinent category information. The 3DTeethLand24 Challenge played a key role in advancing automation and leveraging AI to optimize orthodontic treatments. It also aims to tackle the challenge of limited access to data, providing a valuable resource that encourages community engagement in this vital area with potential clinical implications. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v15571 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aComputers 606 $aApplication software 606 $aMachine learning 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aComputing Milieux 606 $aComputer and Information Systems Applications 606 $aMachine Learning 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aComputers. 615 0$aApplication software. 615 0$aMachine learning. 615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aComputing Milieux. 615 24$aComputer and Information Systems Applications. 615 24$aMachine Learning. 676 $a006 702 $aWang$b Yaqi$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aQian$b Dahong$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWang$b Shuai$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBen-Hamadou$b Achraf$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPujades$b Sergi$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLumetti$b Luca$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGrana$b Costantino$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBolelli$b Federico$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996660361303316 996 $aSupervised and Semi-supervised Multi-structure Segmentation and Landmark Detection in Dental Data$94381052 997 $aUNISA