01282nam 2200349Ia 450 99638507570331620221108093711.0(CKB)4940000000073417(EEBO)2248514756(OCoLC)11733988(EXLCZ)99494000000007341719850225d1668 uy |engurbn||||a|bb|The royal common-wealth's man, or King David's picture[electronic resource] represented in a sermon preached at the solemnity of the funeral of Sir Tho. Adams, knight and baronet, and alderman of London ; in St. Katherine Creechurch, on the 10th of March, 1667 /by Nath. Hardy ..[London?] In the Savoy Printed by Tho. Newcomb, for William Grantham ...1668[8], 40 pReproduction of original in Cambridge University Library.eebo-0021Christian lifeSermonsFuneral sermonsChristian lifeFuneral sermons.Hardy Nathaniel1618-1670.821523EAAEAAm/cWaOLNBOOK996385075703316The royal common-wealth's man, or King David's picture2419824UNISA06809nam 22007695 450 991048329810332120260317151231.03-030-69525-510.1007/978-3-030-69525-5(CKB)4100000011781454(MiAaPQ)EBC6548237(Au-PeEL)EBL6548237(OCoLC)1241066293(PPN)253858828(DE-He213)978-3-030-69525-5(EXLCZ)99410000001178145420210226d2021 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierComputer Vision – ACCV 2020 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part I /edited by Hiroshi Ishikawa, Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi1st ed. 2021.Cham :Springer International Publishing :Imprint: Springer,2021.1 online resource (755 pages) illustrationsImage Processing, Computer Vision, Pattern Recognition, and Graphics,3004-9954 ;126223-030-69524-7 Includes bibliographical references.3D 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.The 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.Image Processing, Computer Vision, Pattern Recognition, and Graphics,3004-9954 ;12622Computer visionArtificial intelligenceComputer engineeringComputer networksPattern recognition systemsComputer VisionArtificial IntelligenceComputer Engineering and NetworksComputer Communication NetworksAutomated Pattern RecognitionVisió per ordinadorthubReconeixement de formes (Informàtica)thubCongressosthubLlibres electrònicsthubComputer vision.Artificial intelligence.Computer engineering.Computer networks.Pattern recognition systems.Computer Vision.Artificial Intelligence.Computer Engineering and Networks.Computer Communication Networks.Automated Pattern Recognition.Visió per ordinadorReconeixement de formes (Informàtica)006.37Ishikawa HiroshiMiAaPQMiAaPQMiAaPQBOOK9910483298103321Computer vision-ACCV 20201890247UNINA