Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Advances in Visual Computing : 18th International Symposium, ISVC 2023, Lake Tahoe, NV, USA, October 16-18, 2023, Proceedings, Part I
| Advances in Visual Computing : 18th International Symposium, ISVC 2023, Lake Tahoe, NV, USA, October 16-18, 2023, Proceedings, Part I |
| Autore | Bebis George |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (630 pages) |
| Altri autori (Persone) |
GhiasiGolnaz
FangYi SharfAndrei DongYue WeaverChris LeoZhicheng LaViola JrJoseph J KohliLuv |
| Collana | Lecture Notes in Computer Science Series |
| ISBN | 3-031-47969-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Keynote Talks -- Machine Learning for Scientific Data Analysis and Visualization -- Estimating the Structure and Motion of Biomolecules at Atomic Resolutions -- Curriculum Learning and Active Learning, for Visual Object Recognition when Data is Scarce -- Have We Solved Image Correspondences? -- Visual Content Manipulation by Learning Generative Models -- Lights, Camera, Animation! Adaptive Simulation Methods for Training and Entertainment -- Beyond the Specs: A Computational and Human-Centered Approach to Wearability in AR/VR -- Contents - Part I -- Contents - Part II -- ST: Biomedical Image Analysis Techniques for Cancer Detection, Diagnosis and Management -- Hybrid Region and Pixel-Level Adaptive Loss for Mass Segmentation on Whole Mammography Images -- 1 Introduction -- 2 Related Work -- 2.1 Mass Segmentation on Whole Mammograms -- 2.2 Loss for Medical Image Segmentation -- 3 Methodology -- 3.1 Hybrid Pixel-Level Loss -- 3.2 Hybrid Region-Level Loss -- 3.3 Density-Adaptive Sample-Level Prioritizing Loss -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Comparison with State-of-the-Art Methods -- 5 Conclusion -- References -- Deep Learning Based GABA Edited-MRS Signal Reconstruction -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 J-Difference Spectrum -- 2.3 Dual Branch Self-Attention Neural Network -- 2.4 Evaluation Metrics -- 3 Results and Discussion -- 4 Conclusion -- References -- Investigating the Impact of Attention on Mammogram Classification -- 1 Introduction -- 2 Data and Methods -- 2.1 Data Selection and Preprocessing -- 2.2 Selection of Models -- 2.3 Selection of Attention Methods -- 2.4 Training and Testing Process -- 3 Results and Discussion -- 3.1 Impact of Attention on CNN Performance -- 3.2 Impact of Model Architecture on Performance Differences.
3.3 Impact of Attention on Resolution -- 3.4 Impact of Attention on Abnormality Type -- 3.5 Relationship Between Model Activation and AU-ROC -- 4 Conclusions -- References -- ReFit: A Framework for Refinement of Weakly Supervised Semantic Segmentation Using Object Border Fitting for Medical Images -- 1 Introduction -- 2 Our ReFit Framework -- 2.1 Unsupervised Segment Detection -- 2.2 Class Activation Map - CAM -- 2.3 The BoundaryFit Module -- 3 Results and Discussion -- 3.1 Ablation Studies -- 4 Conclusion -- References -- A Data-Centric Approach for Pectoral Muscle Deep Learning Segmentation Enhancements in Mammography Images -- 1 Introduction -- 2 Related Work -- 3 Mammography Segmentation -- 3.1 Dataset -- 3.2 Model Training -- 3.3 Drawbacks -- 4 Data-Centric Model Optimization -- 4.1 Stage I: Annotation Correction -- 4.2 Stage II: Downsampling -- 5 Results -- 5.1 Evaluation Metrics -- 5.2 Evaluated Training Datasets -- 5.3 Intersection over Union Evaluation -- 5.4 Classification Metrics for Pectoral Muscle Detection in CC View -- 6 Conclusion -- References -- Visualization -- Visualizing Multimodal Time Series at Scale -- 1 Introduction -- 2 Related Work -- 3 Overview Scenario -- 4 Detail Methods and Implementation -- 4.1 Time Series Dataset -- 4.2 Exploiting Elasticsearch for Fast Search and Big Query -- 4.3 Visualizing Time Series -- 5 Exploring UMAFall Dataset with TimeXplore -- 6 Conclusions and Future Work -- References -- Hybrid Tree Visualizations for Analysis of Gerrymandering -- 1 Introduction -- 2 Related Work -- 3 Gerrymandering -- 4 Data Model in Gerrymandering -- 5 Visual Design -- 6 Analysis Examples -- 6.1 Evaluating the Efficiency Gap -- 6.2 Assessing Electoral Competition -- 7 Conclusion -- References -- ArcheryVis: A Tool for Analyzing and Visualizing Archery Performance Data -- 1 Introduction -- 2 Related Work. 2.1 Archery Performance Analysis -- 2.2 Archery Scoring Apps -- 3 Data Collection, Processing, and Analysis -- 3.1 Data Collection -- 3.2 Ring and Center Detection -- 3.3 Shot Detection and Calibration -- 3.4 Scoring and Statistical Measures -- 4 Visual Interface and Interaction -- 5 Results and Discussion -- 5.1 Brushing and Filtering -- 5.2 Trainee Comparison -- 5.3 Statistical Measure as Performance Indicator -- 5.4 Empirical Evaluation -- 5.5 Limitations -- 6 Conclusions and Future Work -- References -- Spiro: Order-Preserving Visualization in High Performance Computing Monitoring -- 1 Introduction -- 2 Related Work -- 2.1 Spiral Layout in Visualization -- 2.2 Monitoring with Spiral Layout -- 3 Monitoring Tasks -- 4 Spiro Design -- 4.1 Design Rationales -- 4.2 Visual Encoding -- 5 Case Studies -- 5.1 Clustering on Compute Servers -- 5.2 Exploring Usage Behavior -- 6 Conclusion and Future Work -- References -- From Faces to Volumes - Measuring Volumetric Asymmetry in 3D Facial Palsy Scans -- 1 Introduction -- 2 Related Work -- 3 Data Acquisition -- 4 Methods -- 4.1 3D Landmark Extraction for Facial Palsy Patients -- 4.2 Radial Curves -- 4.3 Lateral Face Mesh Generation -- 4.4 Volume Estimation for Lateral Face Sides -- 4.5 Volumetric Difference Visualization -- 5 Volume Analysis During Dynamic Movements -- 6 Conclusions and Future Work -- References -- Video Analysis and Event Recognition -- Comparison of Autoencoder Models for Unsupervised Representation Learning of Skeleton Sequences -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Proposed Methods -- 4 Experiments -- 4.1 Datasets -- 4.2 Results Analysis and Comparisons -- 5 Conclusion and Future Works -- References -- Local and Global Context Reasoning for Spatio-Temporal Action Localization -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Overall Pipeline. 3.2 Near-Actor Relation Network -- 4 Experiments on JHMDB21 -- 4.1 Implementation Details -- 4.2 Comparison on JHMDB21 -- 4.3 Ablation Study -- 4.4 Qualitative Results -- 5 Experiments on AVA -- 5.1 Implementation Details -- 5.2 Comparison on AVA -- 6 Conclusion -- References -- Zero-Shot Video Moment Retrieval Using BLIP-Based Models -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Computing Image and Text Embeddings -- 3.2 Sparse Frame-Sampling Strategies -- 3.3 Moment-Query Matching -- 4 Experiments -- 5 Results and Discussion -- 6 Conclusions and Future Work -- References -- Self-supervised Representation Learning for Fine Grained Human Hand Action Recognition in Industrial Assembly Lines -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Model Architecture -- 3.2 Masking Method -- 4 Experiments -- 4.1 Datasets -- 4.2 Model Training Environment -- 4.3 Self-supervised Pretraining and Downstream Task -- 5 Results and Analysis -- 5.1 Results Self-supervised Learning -- 5.2 Results Downstream Task -- 5.3 Analysis -- 6 Conclusion and Outlook -- References -- ST: Innovations in Computer Vision & -- Machine Learning for Critical & -- Civil Infrastructures -- Pretext Tasks in Bridge Defect Segmentation Within a ViT-Adapter Framework -- 1 Introduction -- 2 Methods -- 2.1 ViT-Adapter Model -- 2.2 Datasets -- 2.3 Supervised Learning (SL) Pre-training -- 2.4 Self- And Semi-Supervised Learning (SSL) Pre-training -- 2.5 Training Parameters -- 3 Results and Discussion -- 4 Conclusion -- References -- A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation -- 1 Introduction -- 1.1 Current Limitations and Our Contribution -- 2 Proposed Architecture -- 2.1 R2AU-Net Architecture for Road Crack Segmentation -- 2.2 Few-Shot Learning for Segmentation Refinement -- 3 Experimental Setup and Results -- 3.1 Dataset Description. 3.2 Comparative Algorithms and Training Configuration -- 3.3 Experiments and Comparisons -- 4 Conclusions -- References -- Efficient Resource Provisioning in Critical Infrastructures Based on Multi-Agent Rollout Enabled by Deep Q-Learning -- 1 Introduction -- 2 Related Work -- 3 Workload Management in Critical Infrastructures -- 3.1 Infrastructure Model -- 3.2 Problem Formulation -- 3.3 Deterministic Markov Decision Process Model -- 3.4 Multi-Agent Rollout Enabled by Deep Q-Learning -- 4 Simulation Experiments -- 4.1 Experimental Setup -- 4.2 Evaluation Results -- 5 Conclusions -- References -- Video-Based Recognition of Aquatic Invasive Species Larvae Using Attention-LSTM Transformer -- 1 Introduction -- 1.1 Attention-LSTM -- 2 Related Work -- 3 Proposed Method -- 3.1 Model Architecture -- 3.2 Attention-LSTM Layer -- 3.3 Model Variations -- 4 Invasive Species Dataset -- 5 Empirical Evaluation -- 6 Conclusion -- References -- ST: Generalization in Visual Machine Learning -- Latent Space Navigation for Face Privacy: A Case Study on the MNIST Dataset -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Experimental Result -- 5 Future Work -- 6 Conclusion -- References -- Domain Generalization for Foreground Segmentation Using Federated Learning -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 3.1 Model Architecture -- 3.2 Training Technique -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Traditional Foreground Segmentation Experiment -- 4.4 Domain Generalization Experiment -- 4.5 Few-Shot Experiment -- 5 Conclusion and Future Work -- References -- Probabilistic Local Equivalence Certification for Robustness Evaluation -- 1 Introduction -- 2 Related Work -- 3 Probabilistic Local Equivalence Certification -- 3.1 Probabilistic Local Equivalence Certification -- 3.2 When Labels are Available. 3.3 The Case of Classification. |
| Record Nr. | UNISA-996565867203316 |
Bebis George
|
||
| Cham : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in Visual Computing : 18th International Symposium, ISVC 2023, Lake Tahoe, NV, USA, October 16-18, 2023, Proceedings, Part I
| Advances in Visual Computing : 18th International Symposium, ISVC 2023, Lake Tahoe, NV, USA, October 16-18, 2023, Proceedings, Part I |
| Autore | Bebis George |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (630 pages) |
| Altri autori (Persone) |
GhiasiGolnaz
FangYi SharfAndrei DongYue WeaverChris LeoZhicheng LaViola JrJoseph J KohliLuv |
| Collana | Lecture Notes in Computer Science Series |
| ISBN | 3-031-47969-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Keynote Talks -- Machine Learning for Scientific Data Analysis and Visualization -- Estimating the Structure and Motion of Biomolecules at Atomic Resolutions -- Curriculum Learning and Active Learning, for Visual Object Recognition when Data is Scarce -- Have We Solved Image Correspondences? -- Visual Content Manipulation by Learning Generative Models -- Lights, Camera, Animation! Adaptive Simulation Methods for Training and Entertainment -- Beyond the Specs: A Computational and Human-Centered Approach to Wearability in AR/VR -- Contents - Part I -- Contents - Part II -- ST: Biomedical Image Analysis Techniques for Cancer Detection, Diagnosis and Management -- Hybrid Region and Pixel-Level Adaptive Loss for Mass Segmentation on Whole Mammography Images -- 1 Introduction -- 2 Related Work -- 2.1 Mass Segmentation on Whole Mammograms -- 2.2 Loss for Medical Image Segmentation -- 3 Methodology -- 3.1 Hybrid Pixel-Level Loss -- 3.2 Hybrid Region-Level Loss -- 3.3 Density-Adaptive Sample-Level Prioritizing Loss -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Comparison with State-of-the-Art Methods -- 5 Conclusion -- References -- Deep Learning Based GABA Edited-MRS Signal Reconstruction -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 J-Difference Spectrum -- 2.3 Dual Branch Self-Attention Neural Network -- 2.4 Evaluation Metrics -- 3 Results and Discussion -- 4 Conclusion -- References -- Investigating the Impact of Attention on Mammogram Classification -- 1 Introduction -- 2 Data and Methods -- 2.1 Data Selection and Preprocessing -- 2.2 Selection of Models -- 2.3 Selection of Attention Methods -- 2.4 Training and Testing Process -- 3 Results and Discussion -- 3.1 Impact of Attention on CNN Performance -- 3.2 Impact of Model Architecture on Performance Differences.
3.3 Impact of Attention on Resolution -- 3.4 Impact of Attention on Abnormality Type -- 3.5 Relationship Between Model Activation and AU-ROC -- 4 Conclusions -- References -- ReFit: A Framework for Refinement of Weakly Supervised Semantic Segmentation Using Object Border Fitting for Medical Images -- 1 Introduction -- 2 Our ReFit Framework -- 2.1 Unsupervised Segment Detection -- 2.2 Class Activation Map - CAM -- 2.3 The BoundaryFit Module -- 3 Results and Discussion -- 3.1 Ablation Studies -- 4 Conclusion -- References -- A Data-Centric Approach for Pectoral Muscle Deep Learning Segmentation Enhancements in Mammography Images -- 1 Introduction -- 2 Related Work -- 3 Mammography Segmentation -- 3.1 Dataset -- 3.2 Model Training -- 3.3 Drawbacks -- 4 Data-Centric Model Optimization -- 4.1 Stage I: Annotation Correction -- 4.2 Stage II: Downsampling -- 5 Results -- 5.1 Evaluation Metrics -- 5.2 Evaluated Training Datasets -- 5.3 Intersection over Union Evaluation -- 5.4 Classification Metrics for Pectoral Muscle Detection in CC View -- 6 Conclusion -- References -- Visualization -- Visualizing Multimodal Time Series at Scale -- 1 Introduction -- 2 Related Work -- 3 Overview Scenario -- 4 Detail Methods and Implementation -- 4.1 Time Series Dataset -- 4.2 Exploiting Elasticsearch for Fast Search and Big Query -- 4.3 Visualizing Time Series -- 5 Exploring UMAFall Dataset with TimeXplore -- 6 Conclusions and Future Work -- References -- Hybrid Tree Visualizations for Analysis of Gerrymandering -- 1 Introduction -- 2 Related Work -- 3 Gerrymandering -- 4 Data Model in Gerrymandering -- 5 Visual Design -- 6 Analysis Examples -- 6.1 Evaluating the Efficiency Gap -- 6.2 Assessing Electoral Competition -- 7 Conclusion -- References -- ArcheryVis: A Tool for Analyzing and Visualizing Archery Performance Data -- 1 Introduction -- 2 Related Work. 2.1 Archery Performance Analysis -- 2.2 Archery Scoring Apps -- 3 Data Collection, Processing, and Analysis -- 3.1 Data Collection -- 3.2 Ring and Center Detection -- 3.3 Shot Detection and Calibration -- 3.4 Scoring and Statistical Measures -- 4 Visual Interface and Interaction -- 5 Results and Discussion -- 5.1 Brushing and Filtering -- 5.2 Trainee Comparison -- 5.3 Statistical Measure as Performance Indicator -- 5.4 Empirical Evaluation -- 5.5 Limitations -- 6 Conclusions and Future Work -- References -- Spiro: Order-Preserving Visualization in High Performance Computing Monitoring -- 1 Introduction -- 2 Related Work -- 2.1 Spiral Layout in Visualization -- 2.2 Monitoring with Spiral Layout -- 3 Monitoring Tasks -- 4 Spiro Design -- 4.1 Design Rationales -- 4.2 Visual Encoding -- 5 Case Studies -- 5.1 Clustering on Compute Servers -- 5.2 Exploring Usage Behavior -- 6 Conclusion and Future Work -- References -- From Faces to Volumes - Measuring Volumetric Asymmetry in 3D Facial Palsy Scans -- 1 Introduction -- 2 Related Work -- 3 Data Acquisition -- 4 Methods -- 4.1 3D Landmark Extraction for Facial Palsy Patients -- 4.2 Radial Curves -- 4.3 Lateral Face Mesh Generation -- 4.4 Volume Estimation for Lateral Face Sides -- 4.5 Volumetric Difference Visualization -- 5 Volume Analysis During Dynamic Movements -- 6 Conclusions and Future Work -- References -- Video Analysis and Event Recognition -- Comparison of Autoencoder Models for Unsupervised Representation Learning of Skeleton Sequences -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Proposed Methods -- 4 Experiments -- 4.1 Datasets -- 4.2 Results Analysis and Comparisons -- 5 Conclusion and Future Works -- References -- Local and Global Context Reasoning for Spatio-Temporal Action Localization -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Overall Pipeline. 3.2 Near-Actor Relation Network -- 4 Experiments on JHMDB21 -- 4.1 Implementation Details -- 4.2 Comparison on JHMDB21 -- 4.3 Ablation Study -- 4.4 Qualitative Results -- 5 Experiments on AVA -- 5.1 Implementation Details -- 5.2 Comparison on AVA -- 6 Conclusion -- References -- Zero-Shot Video Moment Retrieval Using BLIP-Based Models -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Computing Image and Text Embeddings -- 3.2 Sparse Frame-Sampling Strategies -- 3.3 Moment-Query Matching -- 4 Experiments -- 5 Results and Discussion -- 6 Conclusions and Future Work -- References -- Self-supervised Representation Learning for Fine Grained Human Hand Action Recognition in Industrial Assembly Lines -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Model Architecture -- 3.2 Masking Method -- 4 Experiments -- 4.1 Datasets -- 4.2 Model Training Environment -- 4.3 Self-supervised Pretraining and Downstream Task -- 5 Results and Analysis -- 5.1 Results Self-supervised Learning -- 5.2 Results Downstream Task -- 5.3 Analysis -- 6 Conclusion and Outlook -- References -- ST: Innovations in Computer Vision & -- Machine Learning for Critical & -- Civil Infrastructures -- Pretext Tasks in Bridge Defect Segmentation Within a ViT-Adapter Framework -- 1 Introduction -- 2 Methods -- 2.1 ViT-Adapter Model -- 2.2 Datasets -- 2.3 Supervised Learning (SL) Pre-training -- 2.4 Self- And Semi-Supervised Learning (SSL) Pre-training -- 2.5 Training Parameters -- 3 Results and Discussion -- 4 Conclusion -- References -- A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation -- 1 Introduction -- 1.1 Current Limitations and Our Contribution -- 2 Proposed Architecture -- 2.1 R2AU-Net Architecture for Road Crack Segmentation -- 2.2 Few-Shot Learning for Segmentation Refinement -- 3 Experimental Setup and Results -- 3.1 Dataset Description. 3.2 Comparative Algorithms and Training Configuration -- 3.3 Experiments and Comparisons -- 4 Conclusions -- References -- Efficient Resource Provisioning in Critical Infrastructures Based on Multi-Agent Rollout Enabled by Deep Q-Learning -- 1 Introduction -- 2 Related Work -- 3 Workload Management in Critical Infrastructures -- 3.1 Infrastructure Model -- 3.2 Problem Formulation -- 3.3 Deterministic Markov Decision Process Model -- 3.4 Multi-Agent Rollout Enabled by Deep Q-Learning -- 4 Simulation Experiments -- 4.1 Experimental Setup -- 4.2 Evaluation Results -- 5 Conclusions -- References -- Video-Based Recognition of Aquatic Invasive Species Larvae Using Attention-LSTM Transformer -- 1 Introduction -- 1.1 Attention-LSTM -- 2 Related Work -- 3 Proposed Method -- 3.1 Model Architecture -- 3.2 Attention-LSTM Layer -- 3.3 Model Variations -- 4 Invasive Species Dataset -- 5 Empirical Evaluation -- 6 Conclusion -- References -- ST: Generalization in Visual Machine Learning -- Latent Space Navigation for Face Privacy: A Case Study on the MNIST Dataset -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Experimental Result -- 5 Future Work -- 6 Conclusion -- References -- Domain Generalization for Foreground Segmentation Using Federated Learning -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 3.1 Model Architecture -- 3.2 Training Technique -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Traditional Foreground Segmentation Experiment -- 4.4 Domain Generalization Experiment -- 4.5 Few-Shot Experiment -- 5 Conclusion and Future Work -- References -- Probabilistic Local Equivalence Certification for Robustness Evaluation -- 1 Introduction -- 2 Related Work -- 3 Probabilistic Local Equivalence Certification -- 3.1 Probabilistic Local Equivalence Certification -- 3.2 When Labels are Available. 3.3 The Case of Classification. |
| Record Nr. | UNINA-9910767585603321 |
|
Bebis George
|
||
| Cham : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Visual Computing [[electronic resource] ] : 18th International Symposium, ISVC 2023, Lake Tahoe, NV, USA, October 16–18, 2023, Proceedings, Part II / / edited by George Bebis, Golnaz Ghiasi, Yi Fang, Andrei Sharf, Yue Dong, Chris Weaver, Zhicheng Leo, Joseph J. LaViola Jr., Luv Kohli
| Advances in Visual Computing [[electronic resource] ] : 18th International Symposium, ISVC 2023, Lake Tahoe, NV, USA, October 16–18, 2023, Proceedings, Part II / / edited by George Bebis, Golnaz Ghiasi, Yi Fang, Andrei Sharf, Yue Dong, Chris Weaver, Zhicheng Leo, Joseph J. LaViola Jr., Luv Kohli |
| Autore | Bebis George |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (506 pages) |
| Disciplina | 006 |
| Altri autori (Persone) |
GhiasiGolnaz
FangYi SharfAndrei DongYue WeaverChris LeoZhicheng LaViola JrJoseph J KohliLuv |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 3-031-47966-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Virtual Reality -- A Pilot Study Comparing User Interactions Between Augmented and Virtual Reality -- Synthesizing Play-Ready VR Scenes with Natural Language Prompts through GPT API -- Emergent Individual Factors for AR Education and Training -- Segmentation -- ISLE: A Framework for Image Level Semantic Segmentation Ensemble -- Particulate Mapping Centerline Extraction (PMCE), a Novel Centerline Extraction Algorithm Based on Patterns in the Spatial Distribution of Aggregates -- Evaluating Segmentation Approaches on Digitized Herbarium Specimens -- Semantic Scene Filtering for Event Cameras in Long-Term Outdoor Monitoring Scenarios -- SODAWideNet - Salient Object Detection with an Attention augmented Wide Encoder Decoder network without ImageNet pre-training -- Applications -- Foil-Net: Deep Learning-Based Wave Classification for Hydrofoil Surfing -- Inpainting of Depth Images using Deep Neural Networks for Real-Time Applications -- Using 2D and 3D Face Representations to Generate Comprehensive Facial Electromyography Intensity Maps -- Real-world Image Deblurring via Unsupervised Domain Adaptation -- Object Detection and Recognition -- Reliable Matching by Combining Optimal Color and Intensity Information based on Relationships between Target and Surrounding Objects -- Regularized Meta-Training with Embedding Mixup for Improved Few-Shot Learning -- Visual Foreign Object Detection for Wireless Charging of Electric Vehicles -- Deep Representation Learning for License Plate Recognition in Low Quality Video Images -- Optimizing PnP-Algorithms for Limited Point Correspondences Using Spatial Constraints -- Deep Learning -- Unsupervised Deep-Learning Approach for Underwater Image Enhancement -- LaneNet++ : Uncertainty-aware Lane Detection for Autonomous Vehicle -- Task-driven Compression for Collision Encoding based on Depth Images -- Eigenpatches - Adversarial Patches from Principal Components -- Edge-guided Image Inpainting with Transformer -- Poster -- Bayesian Fusion inspired 3D reconstruction via LiDAR-Stereo Camera Pair -- Marimba Mallet Placement Tracker -- DINO-CXR: A Self Supervised Method Based on Vision Transformer for Chest X-Ray Classification -- Social Bias and Image Tagging: Evaluation of Progress in State-of-the-Art Models -- L-TReiD: Logic Tensor Transformer for Re-Identification -- Retinal Disease Diagnosis with a Hybrid ResNet50-LSTM Deep Learning Model -- Pothole Segmentation and Area Estimation with Deep Neural Networks and Unmanned Aerial Vehicles -- Generation method of robot assembly motion considering physicality gap between humans and robots -- A Self-Supervised Pose Estimation Approach for Construction Machines -- Image Quality Improvement of Surveillance Camera Images by Learning Noise Removal Method Using Noise2Noise -- Automating Kernel Size Selection in MRI Reconstruction via a Transparent and Interpretable Search Approach -- Segmentation and Identification of Mediterranean Plant Species -- Exploiting Generative Adversarial Networks in Joint Sensitivity Encoding for Enhanced MRI Reconstruction -- Multisensory Modeling of Tabular Data for Enhanced Perception and Immersive User Experience -- Coping with Bullying Incidents by the Narrative and Multi-modal Interaction in Virtual Reality. |
| Record Nr. | UNINA-9910767583103321 |
|
Bebis George
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Visual Computing [[electronic resource] ] : 18th International Symposium, ISVC 2023, Lake Tahoe, NV, USA, October 16–18, 2023, Proceedings, Part II / / edited by George Bebis, Golnaz Ghiasi, Yi Fang, Andrei Sharf, Yue Dong, Chris Weaver, Zhicheng Leo, Joseph J. LaViola Jr., Luv Kohli
| Advances in Visual Computing [[electronic resource] ] : 18th International Symposium, ISVC 2023, Lake Tahoe, NV, USA, October 16–18, 2023, Proceedings, Part II / / edited by George Bebis, Golnaz Ghiasi, Yi Fang, Andrei Sharf, Yue Dong, Chris Weaver, Zhicheng Leo, Joseph J. LaViola Jr., Luv Kohli |
| Autore | Bebis George |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (506 pages) |
| Disciplina | 006 |
| Altri autori (Persone) |
GhiasiGolnaz
FangYi SharfAndrei DongYue WeaverChris LeoZhicheng LaViola JrJoseph J KohliLuv |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 3-031-47966-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Virtual Reality -- A Pilot Study Comparing User Interactions Between Augmented and Virtual Reality -- Synthesizing Play-Ready VR Scenes with Natural Language Prompts through GPT API -- Emergent Individual Factors for AR Education and Training -- Segmentation -- ISLE: A Framework for Image Level Semantic Segmentation Ensemble -- Particulate Mapping Centerline Extraction (PMCE), a Novel Centerline Extraction Algorithm Based on Patterns in the Spatial Distribution of Aggregates -- Evaluating Segmentation Approaches on Digitized Herbarium Specimens -- Semantic Scene Filtering for Event Cameras in Long-Term Outdoor Monitoring Scenarios -- SODAWideNet - Salient Object Detection with an Attention augmented Wide Encoder Decoder network without ImageNet pre-training -- Applications -- Foil-Net: Deep Learning-Based Wave Classification for Hydrofoil Surfing -- Inpainting of Depth Images using Deep Neural Networks for Real-Time Applications -- Using 2D and 3D Face Representations to Generate Comprehensive Facial Electromyography Intensity Maps -- Real-world Image Deblurring via Unsupervised Domain Adaptation -- Object Detection and Recognition -- Reliable Matching by Combining Optimal Color and Intensity Information based on Relationships between Target and Surrounding Objects -- Regularized Meta-Training with Embedding Mixup for Improved Few-Shot Learning -- Visual Foreign Object Detection for Wireless Charging of Electric Vehicles -- Deep Representation Learning for License Plate Recognition in Low Quality Video Images -- Optimizing PnP-Algorithms for Limited Point Correspondences Using Spatial Constraints -- Deep Learning -- Unsupervised Deep-Learning Approach for Underwater Image Enhancement -- LaneNet++ : Uncertainty-aware Lane Detection for Autonomous Vehicle -- Task-driven Compression for Collision Encoding based on Depth Images -- Eigenpatches - Adversarial Patches from Principal Components -- Edge-guided Image Inpainting with Transformer -- Poster -- Bayesian Fusion inspired 3D reconstruction via LiDAR-Stereo Camera Pair -- Marimba Mallet Placement Tracker -- DINO-CXR: A Self Supervised Method Based on Vision Transformer for Chest X-Ray Classification -- Social Bias and Image Tagging: Evaluation of Progress in State-of-the-Art Models -- L-TReiD: Logic Tensor Transformer for Re-Identification -- Retinal Disease Diagnosis with a Hybrid ResNet50-LSTM Deep Learning Model -- Pothole Segmentation and Area Estimation with Deep Neural Networks and Unmanned Aerial Vehicles -- Generation method of robot assembly motion considering physicality gap between humans and robots -- A Self-Supervised Pose Estimation Approach for Construction Machines -- Image Quality Improvement of Surveillance Camera Images by Learning Noise Removal Method Using Noise2Noise -- Automating Kernel Size Selection in MRI Reconstruction via a Transparent and Interpretable Search Approach -- Segmentation and Identification of Mediterranean Plant Species -- Exploiting Generative Adversarial Networks in Joint Sensitivity Encoding for Enhanced MRI Reconstruction -- Multisensory Modeling of Tabular Data for Enhanced Perception and Immersive User Experience -- Coping with Bullying Incidents by the Narrative and Multi-modal Interaction in Virtual Reality. |
| Record Nr. | UNISA-996574260103316 |
|
Bebis George
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Imaging for Patient-Customized Simulations and Systems for Point-of-Care Ultrasound [[electronic resource] ] : International Workshops, BIVPCS 2017 and POCUS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, 2017, Proceedings / / edited by M. Jorge Cardoso, Tal Arbel, João Manuel R.S. Tavares, Stephen Aylward, Shuo Li, Emad Boctor, Gabor Fichtinger, Kevin Cleary, Bradley Freeman, Luv Kohli, Deborah Shipley Kane, Matt Oetgen, Sonja Pujol
| Imaging for Patient-Customized Simulations and Systems for Point-of-Care Ultrasound [[electronic resource] ] : International Workshops, BIVPCS 2017 and POCUS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, 2017, Proceedings / / edited by M. Jorge Cardoso, Tal Arbel, João Manuel R.S. Tavares, Stephen Aylward, Shuo Li, Emad Boctor, Gabor Fichtinger, Kevin Cleary, Bradley Freeman, Luv Kohli, Deborah Shipley Kane, Matt Oetgen, Sonja Pujol |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XV, 164 p. 99 illus.) |
| Disciplina | 610.285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Health informatics Computer communication systems Data mining Image Processing and Computer Vision Health Informatics Computer Communication Networks Data Mining and Knowledge Discovery |
| ISBN | 3-319-67552-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996465974603316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Imaging for Patient-Customized Simulations and Systems for Point-of-Care Ultrasound [[electronic resource] ] : International Workshops, BIVPCS 2017 and POCUS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, 2017, Proceedings / / edited by M. Jorge Cardoso, Tal Arbel, João Manuel R.S. Tavares, Stephen Aylward, Shuo Li, Emad Boctor, Gabor Fichtinger, Kevin Cleary, Bradley Freeman, Luv Kohli, Deborah Shipley Kane, Matt Oetgen, Sonja Pujol
| Imaging for Patient-Customized Simulations and Systems for Point-of-Care Ultrasound [[electronic resource] ] : International Workshops, BIVPCS 2017 and POCUS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, 2017, Proceedings / / edited by M. Jorge Cardoso, Tal Arbel, João Manuel R.S. Tavares, Stephen Aylward, Shuo Li, Emad Boctor, Gabor Fichtinger, Kevin Cleary, Bradley Freeman, Luv Kohli, Deborah Shipley Kane, Matt Oetgen, Sonja Pujol |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XV, 164 p. 99 illus.) |
| Disciplina | 610.285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Health informatics Computer communication systems Data mining Image Processing and Computer Vision Health Informatics Computer Communication Networks Data Mining and Knowledge Discovery |
| ISBN | 3-319-67552-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910483327503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||