LEADER 00941nam0-22003251i-450 001 990007897250403321 005 20250529090810.0 035 $a000789725 035 $aFED01000789725 035 $a(Aleph)000789725FED01 035 $a000789725 100 $a20040618d2004----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $aa---a---001yy 200 1 $aCorrenti e movimenti artistici dei nostri giorni$fPietro Nuzzo 210 $aS.Maria a Vico (CE)$cil Cuneo$d2004 215 $a260 p.$cill.$d27 cm 610 0 $aMovimenti artistici contemporanei 676 $a759.067 5$v21$zita 700 1$aNuzzo,$bPietro$0421840 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990007897250403321 952 $a759.06 NUZ 1$bBibl.49538$fFLFBC 952 $aXVII 1397$b48152*$fFGBC 959 $aFLFBC 959 $aFGBC 996 $aCorrenti e movimenti artistici dei nostri giorni$9667882 997 $aUNINA LEADER 06537nam 22006975 450 001 9910483298103321 005 20251113174924.0 010 $a3-030-69525-5 024 7 $a10.1007/978-3-030-69525-5 035 $a(CKB)4100000011781454 035 $a(MiAaPQ)EBC6548237 035 $a(Au-PeEL)EBL6548237 035 $a(OCoLC)1241066293 035 $a(PPN)253858828 035 $a(DE-He213)978-3-030-69525-5 035 $a(EXLCZ)994100000011781454 100 $a20210226d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputer Vision ? ACCV 2020 $e15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 ? December 4, 2020, Revised Selected Papers, Part I /$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 (755 pages) $cillustrations 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12622 311 08$a3-030-69524-7 320 $aIncludes bibliographical references. 327 $a3D Computer Vision -- Weakly-supervised Reconstruction of 3D Objects with Large Shape Variation from Single In-the-Wild Images -- HPGCNN: Hierarchical Parallel Group Convolutional Neural Networks for Point Clouds Processing -- 3D Object Detection and Pose Estimation of Unseen Objects in Color Images with Local Surface Embeddings -- Reconstructing Creative Lego Models, George Tattersall -- Multi-View Consistency Loss for Improved Single-Image 3D Reconstruction of Clothed People -- Learning Global Pose Features in Graph Convolutional Networks for 3D Human Pose Estimation -- SGNet: Semantics Guided Deep Stereo Matching -- Reconstructing Human Body Mesh from Point Clouds by Adversarial GP Network -- SDP-Net: Scene Flow Based Real-time Object Detection and Prediction from Sequential 3D Point Clouds -- SAUM: Symmetry-Aware Upsampling Module for Consistent Point Cloud Completion -- Faster Self-adaptive Deep Stereo -- AFN: Attentional Feedback Network based 3D Terrain Super-Resolution -- Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds -- Anatomy and Geometry Constrained One-Stage Framework for 3D Human Pose Estimation -- DeepVoxels++: Enhancing the Fidelity of Novel View Synthesis from 3D Voxel Embeddings -- Dehazing Cost Volume for Deep Multi-view Stereo in Scattering Media -- Homography-based Egomotion Estimation Using Gravity and SIFT Features -- Mapping of Sparse 3D Data using Alternating Projection -- Best Buddies Registration for Point Clouds -- Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data -- Dynamic Depth Fusion and Transformation for Monocular 3D Object Detection -- Attention-Aware Feature Aggregation for Real-time Stereo Matching on Edge Devices -- FKAConv: Feature-Kernel Alignment for Point Cloud Convolution -- Sparse Convolutions on Continuous Domains for Point Cloud and Event Stream Networks -- IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image -- Attended-Auxiliary Supervision Representation for Face Anti-spoofing -- 3D Object Detection from Consecutive Monocular Images -- Data-Efficient Ranking Distillation for Image Retrieval -- Quantum Robust Fitting -- HDD-Net: Hybrid Detector Descriptor with Mutual Interactive Learning -- Segmentation and Grouping -- RGB-D Co-attention Network for Semantic Segmentation -- Semantics through Time: Semi-supervised Segmentation of Aerial Videos with Iterative Label Propagation -- Dense Dual-Path Network for Real-time Semantic Segmentation -- Learning More Accurate Features for Semantic Segmentation in CycleNet -- 3D Guided Weakly Supervised Semantic Segmentation -- Real-Time Segmentation Networks should be Latency Aware -- Mask-Ranking Network for Semi-Supervised Video Object Segmentation -- SDCNet: Size Divide and Conquer Network for Salient Object Detection -- Bidirectional Pyramid Networks for Semantic Segmentation -- DEAL: Difficulty-aware Active Learning for Semantic Segmentation -- EPSNet: Efficient Panoptic Segmentation Network with Cross-layer Attention Fusion -- Local Context Attention for Salient Object Segmentation -- Generic Image Segmentation in Fully Convolutional Networks by Superpixel Merging Map. 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,$x3004-9954 ;$v12622 606 $aComputer vision 606 $aArtificial intelligence 606 $aComputer engineering 606 $aComputer networks 606 $aPattern recognition systems 606 $aComputer Vision 606 $aArtificial Intelligence 606 $aComputer Engineering and Networks 606 $aComputer Communication Networks 606 $aAutomated Pattern Recognition 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aPattern recognition systems. 615 14$aComputer Vision. 615 24$aArtificial Intelligence. 615 24$aComputer Engineering and Networks. 615 24$aComputer Communication Networks. 615 24$aAutomated Pattern Recognition. 676 $a006.37 702 $aIshikawa$b Hiroshi 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483298103321 996 $aComputer vision-ACCV 2020$91890247 997 $aUNINA