LEADER 01155nam0-22003611i-450 001 990005411910403321 005 20220303170553.0 035 $a000541191 035 $aFED01000541191 035 $a(Aleph)000541191FED01 035 $a000541191 100 $a19990604d1968----km-y0itay50------ba 101 0 $afre 102 $aFR 105 $ay---n---001yy 200 1 $aRecherches sur l'<>$ela loi curiate et les auspices d'investiture$fpar Andrč Magdelain 210 $aParis$cPresses Universitaires de France$d1968 215 $a73 p.$d24 cm 225 1 $aTravaux et recherches de la Faculté de Droit et des Sciences Economiques de Paris$iSer. Sciences Historiques$v12 676 $a340$v11 rid.$zita 676 $a340.54$v22$zita 700 1$aMagdelain,$bAndré$0396678 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990005411910403321 952 $a340.54 MAG 1$bANT. G.R. 1034$fFLFBC 952 $aUniv. 357 (12)$b97597$fFGBC 952 $aDDR-XVII Cb 031$b1388 ddr$fDDR$m21-4901 959 $aFGBC 959 $aFLFBC 959 $aDDR 996 $aRecherches sur l'Imperium$9280085 997 $aUNINA LEADER 06438nam 22006855 450 001 9910484687903321 005 20251113191146.0 010 $a3-030-69532-8 024 7 $a10.1007/978-3-030-69532-3 035 $a(CKB)4100000011781455 035 $a(MiAaPQ)EBC6506902 035 $a(Au-PeEL)EBL6506902 035 $a(OCoLC)1241066422 035 $a(PPN)253858798 035 $a(DE-He213)978-3-030-69532-3 035 $a(EXLCZ)994100000011781455 100 $a20210226d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputer Vision ? ACCV 2020 $e15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 ? December 4, 2020, Revised Selected Papers, Part II /$fedited by Hiroshi Ishikawa, Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (733 pages) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12623 311 08$a3-030-69531-X 327 $aLow-Level Vision, Image Processing -- Image Inpainting with Onion Convolutions -- Accurate and Efficient Single Image Super-Resolution with Matrix Channel Attention Network -- Second-order Camera-aware Color Transformation for Cross-domain Person Re-identification -- CS-MCNet:A Video Compressive Sensing Reconstruction Network with Interpretable Motion Compensation -- MCGKT-Net: Multi-level Context Gating Knowledge Transfer Network for Single Image Deraining -- Degradation Model Learning for Real-World Single Image Super-resolution -- Chromatic Aberration Correction Using Cross-Channel Prior in Shearlet Domain -- Raw-Guided Enhancing Reprocess of Low-Light Image via Deep Exposure Adjustment -- Robust High Dynamic Range (HDR) Imaging with Complex Motion and Parallax -- Low-light Color Imaging via Dual Camera Acquisition -- Frequency Attention Network: Blind Noise Removal for Real Images -- Restoring Spatially-Heterogeneous Distortions using Mixture of Experts Network -- Color Enhancement usingGlobal Parameters and Local Features Learning -- An Efficient Group Feature Fusion Residual Network for Image Super-Resolution -- Adversarial Image Composition with Auxiliary Illumination -- Overwater Image Dehazing via Cycle-Consistent Generative Adversarial Network -- Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning -- Multi-scale Attentive Residual Dense Network for Single Image Rain Removal -- FAN: Feature Adaptation Network for Surveillance Face Recognition and Normalization -- Human Motion Deblurring using Localized Body Prior -- Synergistic Saliency and Depth Prediction for RGB-D Saliency Detection -- Deep Snapshot HDR Imaging Using Multi-Exposure Color Filter Array -- Deep Priors inside an Unrolled and Adaptive Deconvolution Model -- Motion and Tracking -- Adaptive Spatio-Temporal Regularized Correlation Filters for UAV-based Tracking -- Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation -- Self-supervised Sparse toDense Motion Segmentation -- Recursive Bayesian Filtering for Multiple Human Pose Tracking from Multiple Cameras -- Adversarial Refinement Network for Human Motion Prediction -- Semantic Synthesis of Pedestrian Locomotion -- Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation -- Visual Tracking by TridentAlign and Context Embedding -- Leveraging Tacit Information Embedded in CNN Layers for Visual Tracking -- A Two-Stage Minimum Cost Multicut Approach to Self-Supervised Multiple Person Tracking -- Learning Local Feature Descriptors for Multiple Object Tracking -- VAN: Versatile Affinity Network for End-to-end Online Multi-Object Tracking -- COMET: Context-Aware IoU-Guided Network for Small Object Tracking -- Adversarial Semi-Supervised Multi-Domain Tracking -- Tracking-by-Trackers with a Distilled and Reinforced Model -- Motion Prediction Using Temporal Inception Module -- A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking -- Modeling Cross-Modal interaction in a Multi-detector, Multi-modal Tracking Framework -- Dense Pixel-wise Micro-motion Estimation of Object Surface by using Low Dimensional Embedding of Laser Speckle Pattern. 330 $aThe six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.* The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; segmentation and grouping Part II: low-level vision, image processing; motion and tracking Part III: recognition and detection; optimization, statistical methods, and learning; robot vision Part IV: deep learning for computer vision, generative models for computer vision Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis Part VI: applications of computer vision; vision for X; datasets and performance analysis *The conference was held virtually. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12623 606 $aComputer vision 606 $aArtificial intelligence 606 $aPattern recognition systems 606 $aComputer engineering 606 $aComputer networks 606 $aComputer Vision 606 $aArtificial Intelligence 606 $aAutomated Pattern Recognition 606 $aComputer Engineering and Networks 606 $aComputer Engineering and Networks 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aPattern recognition systems. 615 0$aComputer engineering. 615 0$aComputer networks. 615 14$aComputer Vision. 615 24$aArtificial Intelligence. 615 24$aAutomated Pattern Recognition. 615 24$aComputer Engineering and Networks. 615 24$aComputer Engineering and Networks. 676 $a006.3 702 $aIshikawa$b Hiroshi 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484687903321 996 $aComputer vision-ACCV 2020$91890247 997 $aUNINA