Pattern recognition and computer vision . Part II : 4th Chinese Conference, PRCV 2021, Beijing, China, October 29-November 1, 2021, Proceedings / / Huimin Ma [and seven others] (editors)

• Record Nr.: UNISA996464410803316
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2021]; ©2021
• ISBN: 3-030-88007-9
• Descrizione fisica: 1 online resource (694 pages)
• Collana: Lecture notes in computer science ; ; 13020
• Disciplina: 006.4
• Soggetti: Pattern recognition systems; Computer vision
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents - Part II -- Computer Vision, Theories and Applications -- Dynamic Fusion Network for Light Field Depth Estimation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 The Overall Architecture -- 3.2 Pyramid ConvGRU -- 3.3 Multi-modal Dynamic Fusion Module (MDFM) -- 4 Experiments -- 4.1 Experiments Setup -- 4.2 Ablation Studies -- 4.3 Comparison with State-of-the-arts -- 5 Conclusion -- References -- Metric Calibration of Aerial On-Board Multiple Non-overlapping Cameras Based on Visual and Inertial Measurement Data -- 1 Introduction -- 2 Related Works -- 3 Metric Calibration Based on Visual and Inertial Measurement Data -- 3.1 Notation and Problem Formulation -- 3.2 Relative Pose Estimation via Structure from Motion -- 3.3 Inertial Measurement Data Based Metric Scale Factor Estimation -- 4 Experimental Results -- 4.1 Equipment -- 4.2 Metric Calibration of the Aerial On-Board Non-overlapping Camera System -- 4.3 Metric Calibration of an Industrial Non-overlapping Camera System -- 4.4 Experiments of Applications for Object Metric 3D Reconstruction -- 5 Conclusions -- References -- SEINet: Semantic-Edge Interaction Network for Image Manipulation Localization -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Cross Interaction Pattern -- 3.2 Aggregate Interaction Module -- 3.3 Bidirectional Fusion Module -- 3.4 Training Loss -- 4 Experiments -- ; 4.1 Datasets and Implementation Details -- 4.2 Evaluation Metrics -- 4.3 Ablation Studies -- 4.4 Robustness Analysis -- 4.5 Comparing with State-of-the-Art -- 5 Conclusion -- References -- Video-Based Reconstruction of Smooth 3D Human Body Motion -- 1 Introduction -- 2 Related Work -- 2.1 3D Human Mesh from Single Images -- 2.2 3D Human Mesh from Video -- 2.3 GANs for Modeling -- 3 Approach -- 3.1 3D Body Representation -- 3.2 Temporal Encoder.; 3.3 Constraint Loss -- 3.4 Motion Discriminator -- 4 Experiments -- 4.1 Implement Details -- 4.2 Comparison to Other Methods -- 4.3 Ablation Experiments -- 5 Conclusion -- References -- A Unified Modular Framework with Deep Graph Convolutional Networks for Multi-label Image Recognition -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Image Feature Extraction Module -- 3.2 Label Semantic Extraction Module -- 3.3 Prediction Results and Training Scheme -- 4 Experiments -- 4.1 Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Experimental Results -- 4.4 Ablation Studies -- 4.5 Adjacency Matrix Visualization -- 5 Conclusion -- References -- 3D Correspondence Grouping with Compatibility Features -- 1 Introduction -- 2 Related Work -- 2.1 3D Correspondence Grouping -- 2.2 Learning for Correspondence Grouping -- 3 Methodology -- 3.1 Compatibility Check -- 3.2 CF Feature Extraction -- 3.3 CF Classification -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Method Analysis -- 4.3 Comparative Results and Visualization -- 5 Conclusions -- References -- Contour-Aware Panoptic Segmentation Network -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Panoptic Contour Branch -- 3.2 Panoptic Segmentation Branch -- 3.3 Structure Loss Function -- 4 Experiments -- 4.1 Dataset -- 4.2 Evaluation Metrics -- 4.3 Implementation Details -- 4.4 Comparisons with Other Methods -- 4.5 Ablative Analysis -- 5 Conclusion -- References -- VGG-CAE: Unsupervised Visual Place Recognition Using VGG16-Based Convolutional Autoencoder -- 1 Introduction -- 2 Realted Work -- 2.1 Handcraft-Based Methods -- 2.2 CNN-Based Methods -- 2.3 AE-Based Methods -- 3 VGG16-Based Convolutional Autoencoder -- 3.1 Model Architecture -- 3.2 Training -- 3.3 Matching -- 4 Experiments -- 4.1 Datasets -- 4.2 State-of-the-Art Approaches -- 4.3 Ground Truth -- 4.4 Comparison and Discussion.; 5 Conclusion -- References -- Slice Sequential Network: A Lightweight Unsupervised Point Cloud Completion Network -- 1 Introduction -- 2 Related Work -- 2.1 3D Learning -- 2.2 3D Completion -- 3 Our Method -- 3.1 Overview -- 3.2 Slicer -- 3.3 Multi-scale Point Encoder -- 3.4 Sequential Predictor -- 3.5 Shape Prediction Decoder -- 3.6 Loss Function -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Point Cloud Completion Results -- 4.3 Analysis of Encoder -- 4.4 Robustness to Occlusion -- 4.5 Comparison of Complexity -- 5 Ablation Study -- 6 Conclusion -- References -- From Digital Model to Reality Application: A Domain Adaptation Method for Rail Defect Detection -- 1 Introduction -- 2 Preliminaries -- 3 Method -- 3.1 DT-Based Virtual Data Generation -- 3.2 Dummy-Target Domain -- 3.3 DA-YOLO -- 4 Experiment -- 4.1 Dataset and Evaluation Metrics -- 4.2 Experiment Settings -- 4.3 Experimental Results -- 5 Conclusion -- References -- FMixAugment for Semi-supervised Learning with Consistency Regularization -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 FMixAugment: MixAugment Combined with FMask -- 3.2 Improved Consistency Regularization -- 3.3 Dynamic Growth Threshold -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Experimental Results -- 4.3 Ablation Study -- 5 Conclusion and Future Work -- References -- IDANet: ; Iterative D-LinkNets with Attention for Road Extraction from High-Resolution Satellite Imagery -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Basic Iteration Module -- 3.3 Iterative Architecture -- 4 Experiment -- 4.1 Datasets -- 4.2 Implementation Details -- 5 Results -- 5.1 Comparison of Road Segmentation Methods -- 5.2 Ablation Experiment -- 5.3 The Influence of Network Iteration -- 6 Conclusion -- References.; Disentangling Deep Network for Reconstructing 3D Object Shapes from Single 2D Images -- 1 Introduction -- 2 Related Works -- 3 Disentangling Deep Network -- 3.1 Network Architecture -- 3.2 Learning Objective Functions -- 3.3 Training Strategy -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Ablation Analysis -- 4.3 3D Reconstruction -- 4.4 Effects of 3D Shape Identity -- 5 Conclusion -- References -- AnchorConv: Anchor Convolution for Point Clouds Analysis -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 AnchorConv -- 3.2 Anchor Reweighting Module -- 3.3 Network Architectures -- 4 Experiments -- 4.1 Classification on ModelNet40 -- 4.2 ShapeNet Part Segmentation -- 4.3 3D Segmentation of Indoor Scene -- 4.4 3D Segmentation of Outdoor Scene -- 4.5 Ablation Study -- 4.6 Qualitative Results -- 5 Conclusion -- References -- IFR: Iterative Fusion Based Recognizer for Low Quality Scene Text Recognition -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Iterative Collaboration -- 3.2 Fusion Module RRF -- 3.3 Loss Functions -- 3.4 Paired Training Data Generate -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Ablation Study -- 4.3 Comparisons with State-of-the-Arts -- 5 Conclusion -- References -- Immersive Traditional Chinese Portrait Painting: Research on Style Transfer and Face Replacement -- 1 Introduction -- 2 Related Work -- 2.1 Neural Style Transfer -- 2.2 Face Replacement -- 3 The P-CP Method -- 3.1 Network Architecture -- 3.2 Neural Style Transfer Network -- 3.3 Face Replacement -- 4 Experiment -- 4.1 Comparison of Different Traditional Chinese Painting Styles -- 4.2 Image Detail Exploration and Optimization -- 4.3 Improvement of Face Replacement with Style Transfer -- 5 Conclusion -- References -- Multi-camera Extrinsic Auto-calibration Using Pedestrians in Occluded Environments -- 1 Introduction.; 2 Related Work -- 3 Calibration Based on 3D Positions -- 3.1 3D Head Positions in Local Camera Coordinates -- 3.2 Registration of 3D Point Sets -- 4 Refinement -- 5 Experiments and Results -- 6 Conclusion -- References -- Dual-Layer Barcodes -- 1 Introduction -- 2 Related Work -- 2.1 Steganography -- 2.2 Watermarking -- 2.3 Barcode -- 3 Method -- 3.1 Encoder -- 3.2 Decoder -- 3.3 Noise Layer -- 3.4 Discriminator -- 4 Experiments and Analysis -- 4.1 Dataset and Experimental Setting -- 4.2 Implementation Details -- 4.3 Metrics -- 5 Discussion -- 6 Conclusion -- References -- Graph Matching Based Robust Line Segment Correspondence for Active Camera Relocalization -- 1 Introduction -- 2 Method -- 2.1 System Overview -- 2.2 Robust Line Segment Matching -- 2.3 Active Camera Relocation -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Analysis of Line Segment Matching -- 3.3 Analysis of Relocalization Accuracy and Convergence Speed -- 3.4 Analysis of Robustness in Hard Scenes -- 4 Conclusion -- References -- Unsupervised Learning Framework for 3D Reconstruction from Face Sketch -- 1 Introduction -- 2 Related Work -- 2.1 Image-to-Image Translation -- 2.2 3D Shape Reconstruction -- 3 Method -- 3.1 Dataset Construction -- 3.2 Network Architecture -- 3.3 Loss Functions -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Quantitative Results and Ablation Study -- 4.3 Qualitative Results -- 5 Conclusion -- References -- HEI-Human: A Hybrid Explicit and Implicit ; Method for Single-View 3D Clothed Human Reconstruction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Explicit Model -- 3.3 Implicit Model -- 3.4 Loss Functions -- 4 Experiments -- 4.1 Dataset and Protocol -- 4.2 Training Details -- 4.3 Quantitative Results -- 4.4 Qualitative Results -- 4.5 Ablation Studies -- 5 Conclusions -- References.; A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction.

Detection of Intrusions and Malware, and Vulnerability Assessment : 16th International Conference, DIMVA 2019, Gothenburg, Sweden, June 19–20, 2019, Proceedings / / edited by Roberto Perdisci, Clémentine Maurice, Giorgio Giacinto, Magnus Almgren

• Record Nr.: UNINA9910337858203321
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
• ISBN: 3-030-22038-9
• Edizione: [1st ed. 2019.]
• Descrizione fisica: 1 online resource (XV, 504 p. 220 illus., 105 illus. in color.)
• Collana: Security and Cryptology, , 2946-1863 ; ; 11543
• Disciplina: 353.00722; 005.8
• Soggetti: Data protection; Computer crimes; Computers; Operating systems (Computers); Computer networks; Computer engineering; Data and Information Security; Computer Crime; Computing Milieux; Operating Systems; Computer Communication Networks; Computer Engineering and Networks
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di contenuto: Wild Wild Web -- Wild Extensions: Discovering and Analyzing Unlisted Chrome Extensions -- New Kid on the Web: A Study on the Prevalence of WebAssembly in the Wild -- Morellian Analysis for Browsers: Making Web Authentication Stronger With Canvas Fingerprinting -- On the Perils of Leaking Referrers in Online Collaboration Services -- Cyber-Physical Systems -- Detecting, Fingerprinting and Tracking Reconnaissance Campaigns Targeting Industrial Control Systems -- Overshadow PLC to Detect Remote Control-Logic Injection Attacks -- A Security Evaluation of Industrial Radio Remote Controllers -- Understanding the Security of Traffic Signal Infrastructure -- Malware -- Practical Enclave Malware with Intel SGX -- How does Malware Use RDTSC? A Study on Operations Executed by Malware for CPU Cycle Measurement -- On Deception-Based Protection Against Cryptographic Ransomware -- PowerDrive: Accurate De-Obfuscation and Analysis of PowerShell Malware -- Software Security and Binary Analysis -- Memory Categorization: Separating Attacker-Controlled Data -- TypeMiner: Recovering Types in Binary Programs using Machine Learning -- SAFE: Self-Attentive Function Embeddings for Binary Similarity -- Triggerflow: Regression Testing by Advanced Execution Path Inspection -- Network Security -- Large-scale Analysis of Infrastructure-leaking DNS Servers -- Security In Plain TXT: Observing the Use of DNS TXT Records in the Wild -- No Need to Marry to Change Your Name! Attacking Profinet IO Automation Networks Using DCP -- DPX: Data-Plane eXtensions for SDN Security Service Instantiation -- Attack Mitigation -- Practical Password Hardening based on TLS -- Role Inference + Anomaly Detection = Situational Awareness in BACnet Networks -- BinTrimmer: Towards Static Binary Debloating through Abstract Interpretation.
• Sommario/riassunto: This book constitutes the proceedings of the 16th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2019, held in Gothenburg, Sweden, in June 2019. The 23 full papers presented in this volume were carefully reviewed and selected from 80 submissions. The contributions were organized in topical sections named: wild wild web; cyber-physical systems; malware; software security and binary analysis; network security; and attack mitigation. .