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[null,"1.","Record Nr.",null,"UNINA990009339640403321"]
[null,null,"Autore",null,"Wolpert, Scott A."]
[null,null,"Titolo",null,"Families of Riemann surfaces and Weil-Peterson geometry / Scott A. Wolpert"]
[null,null,"Pubbl/distr/stampa",null,"Providence : American Mathematical Society, 2010"]
[null,null,"ISBN",null,"978-0-8218-4986-6"]
[null,null,"Descrizione fisica",null,"VII, 118 p. ; 26 cm"]
[null,null,"Collana",null,"Conference board of the mathematical sciences CBMS : regional conference series in mathematics ; 113"]
[null,null,"Disciplina",null,"515.93"]
[null,null,"Locazione",null,"MA1"]
[null,null,"Collocazione",null,"C-35-(113"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNISA996503470603316"]
[null,null,"Titolo",null,"Advances in visual computing : 17th international symposium, ISVC 2022, San Diego, CA, USA, October 3-5, 2022, proceedings, part I / / edited by George Bebis [and eight others]"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-20713-0"]
[null,null,"Descrizione fisica",null,"1 online resource (486 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science ; ; v.13598"]
[null,null,"Disciplina",null,"929.605"]
[null,null,"Soggetti",null,"Computers"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Keynote Talks -- Towards Scaling Up GANs -- Sensible Machine Learning for Geometry -- Designing Augmented Reality for the Future of Work -- The Future of Visual Computing via Foundation Models (Banquet Keynote Talk) -- 3D Reconstruction: Leveraging Synthetic Data for Lightweight Reconstruction -- Human-AI Interaction in Visual Analytics: Designing for the \"Two Black Boxes\" Problem -- Contents - Part I -- Contents - Part II -- Deep Learning I -- Unsupervised Structure-Consistent Image-to-Image Translation -- 1 Introduction -- 2 Background and Related Work -- 3 Method -- 3.1 Encoder -- 3.2 Generator -- 3.3 Structure and Texture Disentanglement -- 3.4 Objective Function -- 4 Experiments -- 4.1 Comparison to State-of-the-Art -- 5 Applications -- 5.1 Addressing Bias in Training Datasets -- 5.2 Training Datasets for Semantic Segmentation of Satellite Images -- 6 Discussion and Limitations -- 7 Conclusions -- References -- Learning Representations for Masked Facial Recovery -- 1 Introduction -- 2 Relevant Works -- 3 Method -- 3.1 Baseline Model -- 3.2 Unmasking Model -- 3.3 Datasets -- 3.4 Implementation Details -- 4 Experimental Results -- 5 Conclusions -- References -- Deep Learning Based Shrimp Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Acquisition -- 3.2 Preprocessing -- 3.3 Classification -- 4 Experimental Results -- 5 Conclusions -- References -- Gait Emotion Recognition Using a Bi-modal Deep Neural Network -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- Attacking "]
[{"$nulls":4},"Frequency Information with Enhanced Adversarial Networks to Generate Adversarial Samples -- 1 Introduction -- 2 Related Work -- 2.1 Adversarial Samples -- 2.2 Black-Box Attacks -- 2.3 Frequency Features and Attacks."]
[{"$nulls":4},"3 Our Frequency Attack Approach -- 3.1 Separate High and Low Frequency Information -- 3.2 Dual Discriminators Support Attack -- 3.3 Frequency Attack Framework -- 3.4 Network Architecture -- 3.5 Loss Function -- 4 Experiments -- 4.1 Evaluation Metric -- 4.2 Ablation Study -- 4.3 Transferability of FAF -- 4.4 Attack Under Defenses -- 5 Conclusion -- References -- Visualization -- Explainable Interactive Projections for Image Data -- 1 Introduction -- 2 Related Work -- 2.1 Interactive Dimensionality Reduction -- 2.2 Semantic Interaction -- 2.3 Explainability in Deep Learning -- 3 Tasks -- 3.1 Define Custom Similarities Based on Prior Knowledge -- 3.2 Link Human and Machine Defined Similarities -- 4 Workflow and Methodology -- 4.1 Initial State -- 4.2 Interactions and Inverse Projection -- 4.3 Visual Explanations -- 5 Usage Scenario: Edamame Pods -- 6 Discussion -- 7 Conclusion -- References -- MultiProjector: Temporal Projection for Multivariates Time Series -- 1 Introduction -- 2 Related Work -- 2.1 Visualizing High Dimensional Temporal Datasets -- 2.2 Dimension Reduction -- 3 Methodology -- 3.1 Clusterings -- 3.2 Multidimensional Projections -- 3.3 Visualizing the Time Dimension -- 3.4 Multivariate Representations -- 4 Use Cases -- 4.1 Use Case 1: Monthly US Employment Rate -- 4.2 Use Case 2: Monitoring Computer Metrics -- 4.3 Use Case 3: Plant Genetics -- 4.4 Discussion -- 5 Conclusion -- References -- Deep Learning Based Super-Resolution for Medical Volume Visualization with Direct Volume Rendering -- 1 Introduction -- 2 Related Work -- 2.1 Image and Video Super-resolution -- 2.2 Resolution Enhancement for Rendered Content -- 3 Methodology -- 3.1 Direct Volume Rendering Framework -- 3.2 Network Architecture -- 4 Dataset -- 5 Evaluation -- 5.1 Performance Gain with Additional Feature at the Input."]
[{"$nulls":4},"5.2 Performance Gain with Additional Previous Frames -- 5.3 Upsampling Ratio -- 6 Conclusion and Future Work -- References -- Interactive Virtual Reality Exploration of Large-Scale Datasets Using Omnidirectional Stereo Images -- 1 Introduction -- 2 Related Work -- 2.1 Image-Based Visualization -- 2.2 Virtual Reality for Large-Scale Data Sets -- 3 Science Drivers -- 3.1 Cancer Cell Transport -- 3.2 Graphene Superlubricity -- 4 Cinema ODS Image Database -- 4.1 Rendering -- 5 Interactive Cinema ODS Viewer -- 6 Evaluation -- 6.1 Visualization Latency -- 6.2 VR Frame Rate -- 6.3 Qualitative Feedback -- 7 Conclusion -- References -- A Quantitative Analysis of Labeling Issues in the CelebA Dataset -- 1 Introduction -- 2 Related Work -- 3 Incorrect Labels -- 3.1 Contradicting and Conflicting Labels -- 3.2 Mislabeling -- 4 Inconsistent Labels -- 4.1 Consistency -- 4.2 Agreement -- 4.3 Correlated Labels -- 5 Conclusion -- References -- Object Detection and Recognition -- Recognition of Aquatic Invasive Species Larvae Using Autoencoder-Based Feature Averaging -- 1 Introduction -- 2 Related Work -- 2.1 Aquatic Invasive Species -- 2.2 Local Responses to Aquatic Invasive Species -- 2.3 Classification with Image Sets -- 2.4 Underwater Image Classification -- 2.5 Autoencoders -- 3 Methodology -- 3.1 Solution Description -- 3.2 Convolutional Autoencoder -- 3.3 Classification Model -- 3.4 Activation Functions -- 3.5 Loss Functions -- 3.6 Base Model -- 3.7 Dataset -- 4 Results -- 4.1 Evaluation Metric -- 4.2 Quantitative Analysis -- 4.3 Comparative Analysis -- 5 Conclusion -- References -- Subspace Analysis for Multi-temporal Disaster Mapping Using Satellite Imagery -- 1 Introduction -- 2 Subspace Learning-Based Disaster Mapping -- 2.1 Region "]
[{"$nulls":4},"Delineation -- 2.2 Segmentation Fusion -- 2.3 Subspace Learning for Disaster Mapping."]
[{"$nulls":4},"3 Determining the Changed and Unchanged Regions -- 4 Experiments, Results and Discussion -- 4.1 Experimental Setup -- 4.2 Results and Discussion -- 5 Conclusion -- References -- Open-Set Plankton Recognition Using Similarity Learning -- 1 Introduction -- 2 Related Work -- 2.1 Plankton Recognition -- 2.2 Open-Set Classification -- 2.3 Classification by Metric Learning -- 3 Proposed Method -- 3.1 Angular Margin Loss -- 4 Experiments -- 4.1 Data -- 4.2 Description of Experiments -- 4.3 Results -- 5 Conclusions -- References -- Sensor Fusion Operators for Multimodal 2D Object Detection -- 1 Introduction -- 2 Related Work -- 3 Camera-LiDAR 2D Object Detector -- 4 Sensor Fusion Operators -- 5 Experimental Results -- 5.1 Experimental Setting -- 5.2 Evaluation of Early Sensor Fusion -- 5.3 Evaluation of Mid-Level Sensor Fusion -- 5.4 Complexity Analysis -- 6 Conclusion -- References -- Learning When to Say ``I Don't Know -- 1 Introduction -- 2 Preliminaries -- 3 Related Work -- 4 Proposed Method -- 5 Experiments -- 5.1 Synthetic Data -- 5.2 Image Datasets -- 5.3 Text Datasets -- 5.4 Generalization from Validation to Test Data -- 5.5 Alternative Confidence Interval Formulations -- 5.6 Discussion -- 6 Conclusion -- References -- Multi-class Detection and Tracking of Intracorporeal Suturing Instruments in an FLS Laparoscopic Box Trainer Using Scaled-YOLOv4 -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Scaled-YOLOv4 Architecture -- 3.2 Measurement Algorithm -- 4 Experimental Setup -- 4.1 Dataset -- 4.2 Software Implementation -- 5 Results -- 6 Discussion -- 7 Conclusion and Future Work -- References -- Deep Learning II -- A New Approach to Visual Classification Using Concatenated Deep Learning for Multimode Fusion of EEG and Image Data -- 1 Introduction -- 2 Related Work -- 3 Datasets -- 3.1 EEG-ImageNet."]
[{"$nulls":4},"3.2 Visual Stimuli EEG Dataset: Real-World 3D Objects and Corresponding 2D Image Stimuli -- 4 Data Encoding and Processing -- 4.1 Classical Feature Extraction for EEG Data -- 4.2 Classical Feature Extraction for Image Data -- 4.3 Principal Component Analysis (PCA) Encoding -- 4.4 Grayscale-Image Encoding for EEG Data -- 5 Methods and Model Implementation -- 5.1 Conventional Machine Learning Classifiers -- 5.2 LSTM-Based EEG Model (LEM) ch17ourvisclasspaper -- 5.3 CNN-Based Image Model (CIM) ch17ourvisclasspaper -- 5.4 Grayscale-Image Encoded EEG Model (GEM) -- 5.5 Concatenation-Based Models ch17ourvisclasspaper -- 6 Experiments and Results -- 6.1 Baseline Visual Classification for EEG and Image Data -- 6.2 Classification Using Deep Learning Models -- 6.3 Hemispherical Brain Region Classification Comparison -- 6.4 Visual Classification Using Multimodal Deep Learning -- 6.5 Visual Classification for Real Object Versus Image as Stimuli -- 7 Discussion -- 8 Conclusion -- References -- Deep Learning-Based Classification of Plant Xylem Tissue from Light Micrographs -- 1 Introduction -- 2 Related Works -- 3 Dataset and Problem Definition -- 4 Methodology -- 4.1 Data Augmentation and Pre-processing -- 4.2 Cascading-Like Model -- 4.3 Global Contextualization Approach -- 5 Experiments and Results -- 5.1 Model Evaluation Metric -- 5.2 Baseline Results -- 5.3 Results -- 6 Discussion -- 7 Conclusion -- References -- VampNet: Unsupervised Vampirizing of Convolutional Networks -- 1 Introduction -- 2 Related Work -- 2.1 Correlation-Based Feature Map Analysis -- 2.2 Multitask Neural Networks -- 2.3 Networks Merging -- 3 Method -- 3.1 Linearity Between Feature Maps -- 3.2 Ranking Linearity Between Features -- 3.3 Vampirizing a Feature Using a Convolutional Operator -- 3.4 Vampirizing a Layer -- 3.5 Automatic Selection of the Layer to Be "]
[{"$nulls":4},"Replaced -- 4 Experiments."]
[{"$nulls":4},"4.1 Setup."]