| TÃtulo : |
15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part I |
| Tipo de documento: |
documento electrónico |
| Autores: |
Ishikawa, Hiroshi, ; Liu, Cheng-Lin, ; Pajdla, Tomas, ; Shi, Jianbo, |
| Mención de edición: |
1 ed. |
| Editorial: |
[s.l.] : Springer |
| Fecha de publicación: |
2021 |
| Número de páginas: |
XVIII, 740 p. 10 ilustraciones |
| ISBN/ISSN/DL: |
978-3-030-69525-5 |
| Nota general: |
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. |
| Palabras clave: |
Visión por computador Inteligencia artificial IngenierÃa Informática Red de computadoras Sistemas de reconocimiento de patrones IngenierÃa Informática y Redes Redes de comunicación informática Reconocimiento de patrones automatizado |
| Ãndice Dewey: |
006.37 Visión artificial |
| Resumen: |
El conjunto de seis volúmenes de LNCS 12622-12627 constituye las actas de la 15.ª Conferencia asiática sobre visión artificial, ACCV 2020, celebrada en Kioto (Japón) en noviembre/diciembre de 2020.* El total de 254 contribuciones se revisó cuidadosamente y se seleccionó de 768 presentaciones durante dos rondas de revisión y mejora. Los artÃculos se centran en los siguientes temas: Parte I: visión artificial en 3D; segmentación y agrupamiento Parte II: visión de bajo nivel, procesamiento de imágenes; movimiento y seguimiento Parte III: reconocimiento y detección; optimización, métodos estadÃsticos y aprendizaje; visión robótica Parte IV: aprendizaje profundo para visión artificial, modelos generativos para visión artificial Parte V: rostro, pose, acción y gesto; análisis de vÃdeo y reconocimiento de eventos; análisis de imágenes biomédicas Parte VI: aplicaciones de la visión artificial; visión para X; conjuntos de datos y análisis de rendimiento *La conferencia se celebró de forma virtual. |
| Nota de contenido: |
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. |
| En lÃnea: |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
| Link: |
https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i |
15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part I [documento electrónico] / Ishikawa, Hiroshi, ; Liu, Cheng-Lin, ; Pajdla, Tomas, ; Shi, Jianbo, . - 1 ed. . - [s.l.] : Springer, 2021 . - XVIII, 740 p. 10 ilustraciones. ISBN : 978-3-030-69525-5 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
| Palabras clave: |
Visión por computador Inteligencia artificial IngenierÃa Informática Red de computadoras Sistemas de reconocimiento de patrones IngenierÃa Informática y Redes Redes de comunicación informática Reconocimiento de patrones automatizado |
| Ãndice Dewey: |
006.37 Visión artificial |
| Resumen: |
El conjunto de seis volúmenes de LNCS 12622-12627 constituye las actas de la 15.ª Conferencia asiática sobre visión artificial, ACCV 2020, celebrada en Kioto (Japón) en noviembre/diciembre de 2020.* El total de 254 contribuciones se revisó cuidadosamente y se seleccionó de 768 presentaciones durante dos rondas de revisión y mejora. Los artÃculos se centran en los siguientes temas: Parte I: visión artificial en 3D; segmentación y agrupamiento Parte II: visión de bajo nivel, procesamiento de imágenes; movimiento y seguimiento Parte III: reconocimiento y detección; optimización, métodos estadÃsticos y aprendizaje; visión robótica Parte IV: aprendizaje profundo para visión artificial, modelos generativos para visión artificial Parte V: rostro, pose, acción y gesto; análisis de vÃdeo y reconocimiento de eventos; análisis de imágenes biomédicas Parte VI: aplicaciones de la visión artificial; visión para X; conjuntos de datos y análisis de rendimiento *La conferencia se celebró de forma virtual. |
| Nota de contenido: |
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. |
| En lÃnea: |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
| Link: |
https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i |
|  |
15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part I
