Image processing Books

Book SynopsisThis 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18?20, 2024.The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications.

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Book SynopsisThis 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18?20, 2024.The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications.

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Book SynopsisThis 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 18?20, 2024.The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications.

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Book SynopsisThis book comprises the proceedings of the International Conference on Machine Vision and Augmented Intelligence (MAI 2022). The conference proceedings encapsulate the best deliberations held during the conference. The diversity of participants in the event from academia, industry, and research reflects in the articles appearing in the book. The book encompasses all industrial and non-industrial applications. This book covers a wide range of topics such as modeling of disease transformation, epidemic forecast, image processing, and computer vision, augmented intelligence, soft computing, deep learning, image reconstruction, artificial intelligence in health care, brain-computer interface, cybersecurity, social network analysis, and natural language processing.​Table of ContentsModelling of Disease Transformation.- Epidemic Forecast.- COVID-19: Theory and practice.- Image Processing and Computer Vision.- Augmented Intelligence: Theory and Applications.- Soft Computing: Theory and Applications.- Deep Learning: Theory and Applications.

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsLearning Bottleneck Transformer for Event Image-Voxel Feature Fusion based Classification.- Multi-scale Dilated Attention Graph Convolutional Network for Skeleton-Based Action Recognition.- Auto-Learning-GCN: An Ingenious Framework for Skeleton-based Action Recognition.- Skeleton-based Action Recognition with Combined Part-wise Topology Graph Convolutional Networks.- Segmenting Key Clues to Induce Human-Object Interaction Detection.- Lightweight Multispectral Skeleton and Multi-Stream Graph Attention Networks for Enhanced Action Prediction with Multiple Modalities.- Spatio-temporal Self-supervision for Few-shot Action Recognition.- A Fuzzy Error based Fine-tune Method for Spatio-temporal Recognition Model.- Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition.- HFGCN-Based Action Recognition System for Figure Skating.- Image Priors Assisted Pre-training for Point Cloud Shape Analysis.-AMM-GAN: Attribute-Matching Memory for Person Text-to-Image Generation.- RecFormer: Recurrent Multi-modal Transformer with History-aware Contrastive Learning for Visual Dialog.- KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing.- Enhancing Text-Image Person Retrieval through Nuances Varied Sample.- Unsupervised Prototype Adapter for Vision-Language Models.- Multimodal Causal Relations Enhanced CLIP for Image-to-Text Retrieval.- Exploring Cross-Modal Inconsistency in Entities and Emotions for Multimodal Fake News Detection.- Deep Consistency Preserving Network for Unsupervised Cross-modal Hashing.- Learning Adapters for Text-guided Portrait Stylization with Pretrained Diffusion Models.- EdgeFusion: Infrared and Visible Image Fusion Algorithm in Low Light.- An Efficient Momentum Framework for Face-Voice Association Learning.- Multi-modal Instance Refinement for Cross-domain Action Recognition.- Modality Interference Decoupling and Representation Alignment for Caricature-Visual Face Recognition.- Plugging Stylized Controls in Open-Stylized Image Captioning.- MGT: Modality-Guided Transformer for Infrared and Visible Image Fusion.- Multimodal Rumor Detection by Using Additive Angular Margin with Class-aware Attention for Hard Samples.- An Effective Dynamic Reweighting Method for Unbiased Scene Graph Generation.- Multi-modal Graph and Sequence Fusion Learning for Recommendation.- Co-attention guided local-global feature fusion for aspect-level multimodal sentiment analysis.- Discovering Multimodal Hierarchical Structures with Graph Neural Networks for Multi-modal and Multi-hop Question Answering.- Enhancing Recommender System with Multi-modal Knowledge Graph.- Location Attention Knowledge Embedding Model for Image-Text Matching.- Pedestrian Attribute Recognition Based on Multimodal Transformer.- RGB-D Road Segmentation Based on Geometric Prior Information.- Contrastive Perturbation Network for Weakly Supervised Temporal Sentence Grounding.- MLDF-Net: Metadata Based Multi-level Dynamic Fusion Network.- Efficient Adversarial Training with Membership Inference Resistance.- Enhancing Image Comprehension for Computer Science Visual Question Answering.- Cross-Modal Attentive Recalibration and Dynamic Fusion for Multispectral Pedestrian Detection.

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsDeep Stereo Matching with Superpixel-based Feature and Cost Aggregation.- GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene Flow.- Diffusion-based 3D Object Detection with Random Boxes.- Blendshape-based Migratable Speech-driven 3D Facial Animation with Overlapping Chunking-Transformer.- FIRE: Fine Implicit Reconstruction Enhancement with Detailed Body Part Labels and Geometric Features.- Sem-Avatar: Semantic Controlled Neural Field for High-Fidelity Audio Driven Avatar.- Depth optimization for accurate 3D Reconstruction from light field images.- TriAxial Low-Rank Transformer for Efficient Medical Image Segmentation.- SACFormer: Unify Depth Estimation and Completion with Prompt.- Rotation-Invariant Completion Network.- Towards Balanced RGB-TSDF Fusion for Consistent Semantic Scene Completion by 3D RGB Feature Completion and a Classwise Entropy Loss Function.- FPTNet: Full Point Transformer Network for Point Cloud Completion.- Efficient Point-based Single Scale 3D Obiect Detection from Traffic Scenes.- Matching-to-Detecting: Establishing Dense and Reliable Correspondences between Images.- Solving Generalized Pose Problem of Central and Non-central Cameras.- RICH: Robust Implicit Clothed Humans Reconstruction from Multi-Scale Spatial Cues.- An Efficient and Consistent Solution to the PnP Problem.- Autoencoder and Masked Image Encoding-based Attentional Pose Network.- A Voxel-Based Multiview Point Cloud Refinement Method via Factor Graph Optimization.- SwinFusion: channel query-response based feature fusion for monocular depth estimation.- PCRT: Multi-branch Point Cloud Reconstruction from a Single Image with Transformers.- Progressive Point Cloud Generating by Shape Decomposing and Upsampling.- Three-dimensional Plant Reconstruction with Enhanced Cascade-MVSNet.- Learning Key Features Transformer Network for Point Cloud Processing.- Unsupervised Domain Adaptation for 3D Object Detection via Self-Training.- Generalizable Neural Radiance Field with Hierarchical Geometry Constraint.- ACFNeRF: Accelerating and Cache-Free Neural Rendering via Point Cloud-based Distance Fields.- OctPCGC-Net: Learning Octree-Structured Context Entropy Model for Point Cloud Geometry Compression.- Multi-modal Feature Guided Detailed 3D Face Reconstruction from a Single Image.- Advanced License Plate Detector in Low-Quality Images with Smooth Regression Constraint.- A Feature Refinement Patch Embedding-Based Recognition Method for Printed Tibetan Cursive Script.- End-to-End Optical Music Recognition with Attention Mechanism and Memory Units Optimization.- Tripartite Architecture License Plate Recognition based on Transformer.- Focus the Overlapping Problem on Few-Shot Object Detection via Multiple Predictions.- Target-aware Bi-Transformer for Few-Shot Segmentation.- Convex Hull Collaborative Representation Learning on Grassmann Manifold with L_1 norm Regularization.- FUFusion: Fuzzy Sets Theory for Infrared and Visible Image Fusion.-Progressive Frequency-aware Network for Laparoscopic Image Desmoking.-A pixel-level segmentation method for water surface reflection detection

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsLoss Filtering Factor for Crowd Counting.- Classifier Decoupled Training for Black-Box Unsupervised Domain Adaptation.- Unsupervised Concept Drift Detection via Imbalanced Cluster Discriminator Learning.- Unsupervised Domain Adaptation for Optical Flow Estimation.- Continuous Exploration via Multiple Perspectives in Sparse Reward Environment.- Network Transplanting for the Functionally Modular Architecture.- TiAM-GAN: Titanium Alloy Microstructure Image Generation Network.- A Robust Detection and Correction Framework for GNN-based Vertical Federated Learning.- QEA-Net: Quantum-Effects-based Attention Networks.- Learning Scene Graph for Better Cross-Domain Image Captioning.- Enhancing Rule Learning on Knowledge Graphs through Joint Ontology and Instance Guidance.- Explore Across-Dimensional Feature Correlations for Few-Shot Learning.- Pairwise-emotion Data Distribution Smoothing for Emotion Recognition.- SIEFusion: Infrared and Visible Image Fusion via Semantic Information Enhancement.- DeepChrom: A Diffusion-Based Framework for Long-Tailed Chromatin State Prediction.- Adaptable Conservative Q-Learning for Offline Reinforcement Learning.- Boosting Out-of-Distribution Detection with Sample Weighting.- Causal discovery via the subsample based reward and punishment mechanism.- Local Neighbor Propagation Embedding.- Inter-class sparsity based non-negative transition sub-space learning.- Incremental Learning Based on Dual-branch Network.- Inter-Image Discrepancy Knowledge Distillation for Semantic Segmentation.- Cascaded Bilinear Mapping Collaborative Hybrid Attention Modality Fusion Model.- CasFormer: Cascaded Transformer based on Dynamic Voxel Pyramid for 3D Object Detection from Point Clouds.- Generalizable and Accurate 6D Object Pose Estimation Network.- An Internal-external Constrained Distillation Framework for Continual Semantic Segmentation.- MTD: Multi-Timestep Detector for Delayed Streaming Perception.- Semi-Direct SLAM with Manhattan for Indoor Low-texture Environment.- L2T-BEV: Local Lane Topology Prediction from Onboard Surround-View Cameras in Bird’s Eye View Perspective.- CCLane: Concise Curve Anchor-based Lane Detection Model with MLP-Mixer.- Uncertainty-aware Boundary Attention Network for Real-time Semantic Segmentation.- URFormer: Unified Representation LiDAR-Camera 3D Object Detection with Transformer.- A Single-Stage 3D Object Detection Method Based on Sparse Attention Mechanism.- WaRoNav: Warehouse Robot Navigation Based on Multi-View Visual-Inertial Fusion.- Enhancing Lidar and Radar Fusion for Vehicle Detection in Adverse Weather via Cross-Modality Semantic Consistency.- Enhancing Active Visual Tracking under Distractor Environments.- Cross-modal and Cross-domain Knowledge Transfer for Label-free 3D Segmentation.- Cross-task Physical Adversarial Attack against Lane Detection System Based on LED Illumination Modulation.- RECO: Rotation Equivariant COnvolutional Neural Network for Human Trajectory Forecasting.- FGFusion: Fine-Grained Lidar-Camera Fusion for 3D Object Detection

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and AnalysisTable of ContentsShared Nearest Neighbor Calibration For Few-Shot Classification.- Prototype Rectification with Region-Wise Foreground Enhancement for Few-Shot Classification.- Rotation Augmented Distillation for Exemplar-Free Class Incremental Learning with Detailed Analysis.- Nonconvex Tensor Hypergraph Learning for Multi-view Subspace Clustering.- A Novel Method for Identifying Bipolar Disorder based on Diagnostic Texts.- Deep Depression Detection based on Feature Fusion and Result Fusion.- Adaptive Cluster Assignment for Unsupervised Semantic Segmentation.- Confidence-Guided Open-World Semi-Supervised Learning.- SSCL: Semi-supervised Contrastive Learning for Industrial Anomaly Detection.- One Step Large-scale Multi-view Subspace Clustering based on Orthogonal Matrix Factorization with Consensus Graph Learning.- Deep Multi-Task Image Clustering with Attention-guided Patch Filtering and Correlation Mining.- Deep Structure and Attention Aware Subspace Clustering.- Broaden Your Positives: A General Rectification Approach for Novel Class Discovery.- CE2: A Copula Entropic Mutual Information Estimator for Enhancing Adversarial Robustness.- Two-step projection of sparse discrimination between classes for unsupervised domain adaptation.- Enhancing Adversarial Robustness via Stochastic Robust Framework.- Pseudo Labels Refinement with Stable Cluster Reconstruction for Unsupervised Re Identification.- Ranking Variance Reduced Ensemble Attack with Dual Optimization Surrogate Search.- PCR: A Large-Scale Benchmark for Pig Counting in Real World.- A Hierarchical Theme Recognition Model for Sandplay Therapy.- Change-Aware Network for Damaged Roads Recognition and Assessment Based on Multi-temporal Remote Sensing Imageries.- UAM-Net: An Attention-Based Multi-Level Feature Fusion UNet for Remote Sensing Image Segmentation.- Improved Conditional Generative Adversarial Networks for SAR-to-Optical Image Translation.- A Novel Cross Frequency-domain Interaction Learning for Aerial Oriented Object Detection.- DBDAN: Dual-Branch Dynamic Attention Network for Semantic Segmentation of Remote Sensing Images.- Multi-scale Contrastive Learning for Building Change Detection in Remote Sensing ImagesShadow Detection of Remote Sensing Image by Fusion of Involution and Shunted Transformer.- Few-shot Infrared Image Classification with Partial Concept Feature.- AGST-LSTM: The ConvLSTM model combines attention and gate structure for spatiotemporal sequence prediction learning.- A Shape-based Quadrangle Detector for Aerial Images.- End-to-end Unsupervised Style and Resolution Transfer Adaptation Segmentation Model for Remote Sensing ImagesA physically feasible counter-attack method for remote sensing imaging point clouds.- Adversarial Robustness via Multi-experts framework for SAR recognition with Class Imbalanced.- Recognizer Embedding Diffusion Generation for Few-shot SAR Recognization.- A Two-Stage Federated Learning Framework for Class Imbalance in Aerial Scene ClassificationSAR Image Authentic Assessment with Bayesian Deep Learning and Counterfactual Explanations.- Circle Representation Network for Specific Target Detection in Remote Sensing Image.- A Transformer-Based Adaptive Semantic Aggregation Method for UAV Visual Geo-Localization.- Lightweight Multiview Mask Contrastive Network for Small-sample Hyperspectral Image Classification.- Dim moving target detection based on imaging uncertainty analysis and hybrid entropy

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsRSID: A remote sensing image dehazing network.- ContextNet: Learning Context Information for Texture-less Light Field Depth Estimation.- An Efficient Way for Active None-line-of-sight: End-to-end Learned Compressed NLOS Imaging.- DFAR-Net: Dual-Input Three-Branch Attention Fusion Reconstruction Network for Polarized Non-Line-of-Sight Imaging.- EVCPP:Example-driven Virtual Camera Pose Prediction for cloud performing arts scenes.- RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution.- WDU-Net: Wavelet-Guided Deep Unfolding Network for Image Compressed Sensing Reconstruction.- Memory-Augmented Spatial-Temporal Consistency Network for Video Anomaly Detection.- Frequency and Spatial Domain Filter Network for Visual Object Tracking.- Enhancing Feature Representation for Anomaly Detection via Local-and-Global Temporal Relations and a Multi-Stage Memory.- DFAformer: A Dual Filtering Auxiliary Transformer for Efficient Online Action Detection in Streaming Videos.- Relation-guided Multi-stage Feature Aggregation Network for Video Object Detection.- Multimodal Local Feature Enhancement Network for Video Summarization.- Asymmetric Attention Fusion for Unsupervised Video Object Segmentatio.- Flow-Guided Diffusion Autoencoder for Unsupervised Video Anomaly detection.- Prototypical Transformer for Weakly Supervised Action Segmentation.- Unimodal-Multimodal Collaborative Enhancement for Audio-Visual Event Localization.- Dual-memory feature aggregation for video object detection.- Going Beyond Closed Sets: A Multimodal Perspective for Video Emotion Analysis.- Temporal-Semantic Context Fusion for Robust Weakly Supervised Video Anomaly Detection.- A Survey: the Sensor-based Method for Sign Language Recognition.- Utilizing Video Word Boundaries and Feature-based Knowledge Distillation Improving Sentence-level Lip Reading.- Denoised Temporal Relation Network for Temporal Action Segmentation.- 3D Lightweight Spatial-Spectral Attention Network for Hyperspectral Image Classification.- Deepfake Detection via Fine-Grained Classification and Global-Local Information Fusion.- Unsupervised Image-to-Image Translation with Style Consistency.- SemanticCrop: Boosting Contrastive Learning via Semantic-cropped Views.- Transformer-based multi-object tracking in unmanned aerial vehicles.- HEI-GAN: A Human-Environment Interaction based GAN for Multimodal Human Trajectory Prediction.- CenterMatch: A Center Matching Method for Semi-supervised Facial Expression Recognition.- Cross-Dataset Distillation with Multi-Tokens for Image Quality Assessment.- Quality-Aware CLIP for Blind Image Quality Assessment.- Multi-Agent Perception via Co-Attentive Communication Mechanism.- DBRNet:Dual-Branch Real-Time Segmentation NetWork For Metal Defect Detection.- MaskDiffuse: Text-guided Face Mask Removal based on Diffusion Models.- Image Generation Based Intra-class Variance Smoothing for Fine-grained Visual Classification.- Cross-Domain Soft Adaptive Teacher for Syn2Real Object Detection.- Dynamic Graph-Driven Heat Diffusion: Enhancing Industrial Semantic Segmentation.- EKGRL: Entity-based Knowledge Graph Representation Learning for Fact-based Visual Question Answering.- Disentangled Attribute Features Vision Transformer for Pedestrian Attribute Recognition.- A high-resolution network based on feature redundancy reduction and attention mechanism.

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsFeature Enhancement with Text-specific Region Contrast for Scene Text Detection.- Learning Efficient Representations for Patent Drawing Retrieval.- HelixNet: Dual Helix Cooperative Decoders for Scene Text Removal.- Semantic-information Space Sharing Interaction Network for Arbitrary Shape Text Detection.- AIE-KB: Information Extraction Technology with Knowledge Base for Chinese Archival Scenario.- Deep Hough Transform For Gaussian Semantic Box-Lines Alignment.- Chinese-Vietnamese Cross-lingual Event Causality Identification Based on Syntactic Graph Convolution.- MCKIE: Multi-Class Key Information Extraction from Complex Documents based on Graph Convolutional Network.- A Pre-trained Model For Chinese Medical Record Punctuation Restoration.- "English and Spanish Bilinguals’ Language Processing: An ALE-based Meta-analysis of Neuroimaging Studies".- Robust Subspace Learning with Double Graph Embedding Unsupervised Feature Selection via Nonlinear Representation and Adaptive Structure Preservation.- Text Causal Discovery Based on Sequence Structure Information.- MetaSelection: A Learnable Masked AutoEncoder for Multimodal Sentiment Feature Selection.- Image Manipulation Localization based on Multiscale Convolutional Attention.- Bi-Stream Multiscale Hamhead Networks with Contrastive Learning for Image forgery Localization.- Fuse Tune: Hierarchical Decoder Towards Efficient Transfer Learning.- Industrial-SAM with Interactive Adapter.- Mining Temporal Inconsistency with 3D Face Model for Deepfake Video Detection.- DT-TransUNet: A Dual-task Model for Deepfake Detection and Segmentation.- Camouflaged Object Detection via Global-edge Context and Mixed-scale Refinement.- Enhancing CLIP-Based Text-Person Retrieval by Leveraging Negative Samples.- Global Selection and Local Attention Network for Referring Image Segmentation.- MTQ-Caps: A Multi-Task Capsule Network for Blind Image Quality Assessment.- VCD: Visual Causality Discovery for Cross-Modal Question Reasoning.- Multimodal Topic and Sentiment Recognition for Chinese Data Based on Pre-trained Encoders.- Multi-Feature Fusion-Based Central Similarity Deep Supervised Hashing.- VVA: Video Values Analysis.- Dynamic Multi-modal Prompting for Efficient Visual Grounding.- A Graph-involved Lightweight Semantic Segmentation Network.- User-Aware Prefix-Tuning is a Good Learner for Personalized Image Captioning.- An End-to-End Transformer with Progressive Tri-modal Attention for Multi-modal Emotion Recognition.- Target-oriented Multi-criteria Band Selection for Hyperspectral Image.- Pairwise Negative Sample Mining for Human-Object Interaction Detection.- An Evolutionary Multiobjective Optimization Algorithm based on Manifold Learning.- Path Planning of Automatic Parking System by A Point-based Genetic AlgorithmPenalty-Aware Memory Loss for Deep Metric Learning.- Central and Directional Muti-Neck Knowledge Distillation.- Online Class-incremental Learning in Image Classification based on AttentionOnline airline baggage packing based on hierarchical tree A2C-reinforcement learning framework

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsA Quantum-based Attention Mechanism in Scene Text Detection.- NCMatch: Semi-Supervised Learning with Noisy Labels via Noisy Sample Filter and Contrastive Learning.- Data-free Low-bit Quantization via Dynamic Multi-teacher Knowledge Distillation.- LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation.- DUFormer: Solving Power Line Detection Task in Aerial Images using Semantic Segmentation.- Space-Transform Margin Loss with Mixup for Long-tailed Visual Recognition.- A Multi-Perspective Squeeze Excitation Classifier Based on Vision Transformer for Few Shot Image Classification.- ITCNN: Incremental Learning Network Based on ITDA and Tree Hierarchical CNN.- Periodic-Aware Network for Fine-grained Action Recognition.- Learning Domain-invariant Representations from Text for Domain Generalization.- TSTD:A Cross-modal Two Stages Network with New Trans-Decoder for Point Cloud Semantic Segmentation.- NeuralMAE: Data-Efficient Neural Architecture Predictor with Masked Autoencoder.- Co-Regularized Facial Age Estimation with Graph-Causal Learning.- Online Distillation and Preferences Fusion for Graph Convolutional Network-based Sequential Recommendation.- Grassmann Graph Embedding for Few-Shot Class Incremental Learning.- Global Variational Convolution Network for Semi-Supervised Node Classification on Large-scale Graphs.- Frequency Domain Distillation for Data-Free Quantization of Vision Transformer.- An ANN-Guided Approach to Task-Free Continual Learning with Spiking Neural Networks.- Multi-Adversarial Adaptive Transformers for Joint Multi-Agent Trajectory Prediction.- Enhancing Open-Set Object Detection via Uncertainty-Boxes Identification.- Interventional Supervised Learning for Person Re-Identification.- DP-INNet: Dual-Path Implicit Neural Network for Spatial and Spectral Features Fusion in Pan-sharpening.- Stable Visual Pattern Mining via Pattern Probability Distribution.- Dynamic Visual Prompt Tuning for Parameter Efficient Transfer Learning.- C-volution: A Hybrid operator for Visual Recognition.- Motor Imagery EEG Recognition Based on an Improved Convolutional Neural Network with Parallel Gate Recurrent Unit.- A Stable Vision Transformer for Out-of-Distribution Generalization.- Few-Shot Classification with Semantic Augmented Activators.- MixPose: 3D Human Pose Estimation with Mixed Encoder.- Image Manipulation Detection Based on Ringed Residual Edge Artifact Enhancement and Multiple Attention Mechanisms.- Improving Masked Autoencoders by Learning Where to Mask.- An Audio Correlation-Based Graph Neural Network for Depression Recognition.- Dynamic Gesture Recognition based on 3D Central Difference Separable Residual LSTM Coordinate Attention Networks.- QESAR: Query Effective Decision-based Attack on Skeletal Action Recognition.- A Closer Look at Few-shot Object Detection.- Learning-without-Forgetting via Memory Index in Incremental Object Detection.- SAMDConv: Spatially Adaptive Multi-scale Dilated Convolution.- SADD:Generative Adversarial Networks via Self-Attention and Dual Discriminator in Unsupervised Domain Adaptation.- ELFLN: An Efficient Lightweight Facial Landmark Network Based on Hybrid Knowledge Distillation.- Enhancing Continual Noisy Label Learning with Uncertainty-based Sample Selection and Feature Enhancement

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsDecoupled Contrastive Learning for Long-Tailed Distribution.- MFNet: A Channel Segmentation-based Hierarchical Network for Multi-Food Recognition.- Improving the Adversarial Robustness of Object Detection with Contrastive Learning.- CAWNet: A Channel Attention Watermarking Attack Network Based on CWABlock.- Global Consistency Enhancement Network for Weakly-Supervised Semantic Segmentation.- Enhancing Model Robustness against Adversarial Attacks with an Anti-Adversarial Module.- FGPTQ-ViT:Fine-grained Post-training Quantization for Vision Transformers.- Learning Hierarchical Representations in Temporal and Frequency Domains for Time Series Forecasting.- DeCAB: Debiased Semi-Supervised Learning for Imbalanced Open-Set Data.- An Effective Visible-Infrared Person Re-identification Network based on Second-Order Attention and Mixed Intermediate Modality.- Quadratic polynomial residual network for no-reference image quality assessment.- Interactive Learning for Interpretable Visual Recognition via Semantic-Aware Self-Teaching Framework.-Adaptive and Compact Graph Convolutional Network for Micro-Expression Recognition.- Consistency Guided Multiview Hypergraph Embedding Learning with Multiatlas-Based Functional Connectivity Networks Using Resting-State fMRI.- A Diffusion Simulation User Behavior Perception Attention Network for Information Diffusion Prediction.- A Representation Learning Link Prediction Approach Using Line Graph Neural Networks.- Event Sparse Net: Sparse Dynamic Graph Multi-representation Learning with Temporal Attention for Event-based Data.- Federated Learning Based on Diffusion Model to Cope with non-IID DataSFRSwin: A shallow significant feature retention Swin Transformer for fine-grained image classification of wildlife species.- A robust and high accurate method for hand kinematics decoding from neural populations.- Multi-head Attention Induced Dynamic Hypergraph Convolutional Networks.- Self Supervised Temporal Ultrasound Reconstruction for Muscle Atrophy EvaluationSalient Object Detection Using Reciprocal Learning.- Graphormer-based Contextual Reasoning Network for Small Object Detection.- PVT-Crowd:Bridging Multi-scale Features from Pyramid Vision Transformer for Weakly-Supervised Crowd Counting.- Multi-view Contrastive Learning Network for Recommendation.- Uncertainty-confidence fused pseudo-labeling for Graph Neural Networks.- FSCD-Net: A Few-Shot Stego Cross-Domain Net for Image Steganalysis.- Preference Contrastive Learning for Personalized Recommendation.- GLViG: Global and Local Vision GNN May be What You Need for Vision.- SVDML: Semantic and Visual space Deep Mutual Learning for Zero-Shot Learning.- Heterogeneous Graph Attribute Completion via Efficient Meta-path Context-aware Learning.- Fine-Grain Classification Method of Non-Small Cell Lung Cancer Based on Progressive Jigsaw and Graph Convolutional Network.- Improving Transferability of Adversarial Attacks with Gaussian Gradient Enhance Momentum.- Boundary Guided Feature fusion Network for Camouflaged Object Detection.- Saliency Driven Monocular Depth Estimation based on Multi-scale Graph Convolutional Network.- Mask-guided Joint Single Image Specular Highlight Detection and Removal.- CATrack: Convolution and Attention Feature Fusion for Visual Object TrackingSText-DETR: End-to-End Arbitrary-Shaped Text Detection with Scalable Query in Transformer.- SSHRF-GAN: Spatial-Spectral Joint High Receptive Field GAN for Old Photo Restoration

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsDual-stream Context-aware Neural Network for Survival Prediction from Whole Slide Images.- A multi-label image recognition algorithm based on spatial and semantic correlation interaction.- Hierarchical Spatial-Temporal Network for Skeleton-Based Temporal Action Segmentation.- Multi-Behavior Enhanced Graph Neural Networks for Social Recommendation.- A Complex-valued Neural Network based Robust Image Compression.- Binarizing Super-resolution Neural Network without Batch Normalization.- Infrared and Visible Image Fusion via Test-Time Training.- Graph-based Dependency-aware Non-Intrusive Load Monitoring.- Few-shot Object Detection via Classify-free RPN.- IPFR: Identity-Preserving Face Reenactment with Enhanced Domain Adversarial Training and Multi-level Identity Priors.- L2MNet: Enhancing Continual Semantic Segmentation with Mask Matching.- Adaptive Channel Pruning for Trainability Protection.- Exploiting Adaptive Crop and Deformable Convolution for Road Damage Detection.- Cascaded-scoring Tracklet Matching for Multi-object Tracking.- Boosting Generalization Performance in Person Re-IdentificationSelf-Guided Transformer for Video Super-Resolution.- SAMP: Sub-task Aware Model Pruning with Layer-wise Channel Balancing for Person Search.- MKB: Multi-kernel Bures Metric for Nighttime Aerial Tracking.- Deep Arbitrary-Scale Unfolding Network for Color-Guided Depth Map Super-Resolution.- SSDD-Net: A Lightweight and Efficient Deep Learning Model for Steel Surface Defect Detection.- Effective Small Ship Detection with Enhanced-YOLOv7.- PiDiNeXt: An Efficient Edge Detector based on Parallel Pixel Difference Networks.- Transpose and Mask: Simple and Effective Logit-Based Knowledge Distillation for Multi-Attribute and Multi-Label Classification.- CCSR-Net: Unfolding coupled convolutional sparse representation for multi-focus image fusion.- FASONet: A Feature Alignment-Based SAR and Optical Image Fusion Network for Land Use Classification.- De Novo Design of Target-Specific Ligands Using BERT-Pretrained Transformer.- CLIP for Lightweight Semantic Segmentation.- Teacher-Student Cross-Domain Object Detection Model Combining Style Transfer and Adversarial Learning.- Computing 2D Skeleton via Generalized Electric Potential.- Illumination Insensitive Monocular Depth Estimation based on Scene Object Attention and Depth Map Fusion.- A Few-Shot Medical Image Segmentation Network with Boundary Category Correction.- Repdistiller: Knowledge Distillation Scaled by Re-parameterization for Crowd Counting.- Multi-depth Fusion Transformer and Batch Piecewise Loss for Visual Sentiment Analysis.- Expanding the Horizons: Exploring Further Steps in Open-Vocabulary Segmentation.- Exploring a Distillation with Embedded Prompts for Object Detection in Adverse Environments.- TEFNet: Target-Aware Enhanced Fusion Network for RGB-T Tracking.- DARN: Crowd Counting Network Guided by Double Attention Refinement.- DFR-ECAPA: Diffusion Feature Refinement for Speaker Verification based on ECAPA-TDNN.- Half Aggregation Transformer for Exposure Correction.- Deformable Spatial-Temporal Attention for Lightweight Video Super-Resolution

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsOKGR: Occluded Keypoint Generation and Refinement for 3D Object Detection.- Camouflaged Object Segmentation Based on Fractional Edge Perception.- DecTrans: Person Re-identification with Multifaceted Part Features via Decomposed Transformer.- AHT: A Novel Aggregation Hyper-Transformer for Few-Shot Object Detection.- Feature Refinement from Multiple Perspectives for High Performance Salient Object Detection.- Feature Disentanglement and Adaptive Fusion for Improving Multi-Modal Tracking.- Modality Balancing Mechanism for RGB-Infrared Object Detection in Aerial Image.- Pacific Oyster Gonad Identification and Grayscale Calculation Based on Unapparent Object Detection.- Multi-task Self-supervised Few-Shot Detection.- CSTrack: A Comprehensive and Concise Vision Transformer Tracker.- Feature Implicit Enhancement via Super-Resolution for Small Object Detection.- Improved detection method for SODL-YOLOv7 intensive juvenile abalone.- MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation.- Context-FPN and Memory Contrastive Learning for Partially Supervised Instance Segmentation.- A Dynamic Tracking Framework Based on Scene Perception.- HPAN: A Hybrid Pose Attention Network for Person Re-identification.- SpectralTracker: Jointly High and Low-Frequency Modeling for Tracking.- DiffusionTracker: Targets Denoising based on Diffusion Model for Visual Tracking.- Instance-proxy Loss for Semi-supervised Learning with Coarse Labels.- FAFVTC: A Real-time Network for Vehicle Tracking and Counting.- Ped-Mix: Mix Pedestrians for Occluded Person Re-Identification.- Object-Aware Transfer-based Black-box Adversarial Attack on Object Detector.- HTNet: A Hybrid Model Boosted by Triple Self-Attention for Crowd Counting.- Reliable Boundary Samples-based Proxy Pairs for Unsupervised Person Re- dentification.- High-Resolution Feature Representation Driven Infrared Small-Dim Object Detection.- Few-Shot Object Detection Algorithm Based on Adaptive Relation Distillation.- A real-time safety detector based on re-parameterization multiscale feature fusion for forklift driving.- RTMDet-R2：An Improved Real-Time Rotated Object Detector.- Boosting Object Detection in Foggy Scenes via Dark Channel Map and Union Training Strategy.- Object Centric Body Part Attention Network for Human-Object Interaction Detection.- Salient Feature Enhanced Multi-Object Tracking with Soft-Sparse Attention in Transformer.- A BiGRU based Adaptive Gain Estimation for radar Multi-target Tracking.- Prompt based Lifelong Person Re-identification.- Hierarchical Focused Feature Pyramid Network for Small Object Detection JLInst: Boundary-Mask Joint Learning for Instance Segmentation.- Boosting One-stage Multi Object Tracking with Attention Learning.- TPNet: Enhancing Weakly Supervised Polyp Frame Detection with Temporal Encoder and Prototype-based Memory Bank.- Learning Frequency-based Disentanglement and Filtering for Generalizable Person Re-identification.- Stereo3DMOT: Stereo Vision Based 3D Multi-Object Tracking with Multimodal ReID.- Emphasizing Boundary-Positioning and Leveraging Multi-Scale Feature Fusion for Camouflaged Object Detection

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Book SynopsisThe 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. Table of ContentsGrowth Simulation Network for Polyp Segmentation.- Brain Diffuser: An End-To-End Brain Image to Brain Network Pipeline.- CCJ-SLC: A Skin Lesion Image Classification Method based on Contrastive Clustering and Jigsaw Puzzle.- A Real-Time Network for Fast Breast Lesion Detection in Ultrasound Videos.- CBAV-Loss: Crossover and Branch Losses for Artery-vein Segmentation in OCTA Images.- Leveraging Data Correlations For Skin Lesion Classification.- CheXNet: Combing Transformer and CNN for Thorax Disease Diagnosis from Chest X-ray Images.- Cross Attention Multi Scale CNN-Transofmer Hybrid encoder is General Medical Image Learner.- Weakly/Semi-supervised Left Ventricle Segmentation in 2D Echocardiography with Uncertain Region-aware Contrastive Learning.- Spatial-Temporal Graph Convolutional Network for Insomnia Classification via Brain Functional Connectivity Imaging of rs-fMRI.- Probability-based Nuclei Detection and Critical-Region Guided Instance Segmentation.- FlashViT: A Flash Vision Transformer with Large-scale Token Merging for Congenital Heart Disease Detection.- Semi-supervised Retinal Vessel Segmentation through Point Consistency.- Knowledge Distillation of Attention and Residual U-Net: Transfer from Deep to Shallow Models for Medical Image Classification.- Two-stage deep learning segmentation for tiny brain regions.- Encoder Activation Diffusion and Decoder Transformer Fusion Network for Medical Image Segmentation.- Liver segmentation via learning cross-modality content-aware representation.- Semi-Supervised Medical Image Segmentation based on Multi-scale Knowledge Discovery and Multi-task Ensemble.- LATrans-Unet: Improving CNN-Transformer with Location-Adaptive for Medical Image Segmentation.- Adversarial Keyword Extraction and Semantic-Spatial Feature Aggregation for Clinical Report Guided Thyroid Nodule Segmentation.- A Multi-Modality Driven Promptable Transformer for Automated Parapneumonic Effusion Staging.- Assessing the Social Skills of Children with Autism Spectrum Disorder via Language-Image Pre-training Models.- PPS: Semi-supervised 3D Biomedical Image Segmentation via Pyramid Pseudo-Labeling Supervision.- A Novel Diffusion-Model-Based OCT Image Inpainting Algorithm for Wide Saturation Artifacts.- Only Classification Head is Sufficient for Medical Image Segmentation.- Task-incremental Medical Image Classification with Task-specific Batch Normalization.- Hybrid Encoded Attention Networks for Accurate Pulmonary Artery-Vein Segmentation in Noncontrast CT Images.- Multi-Modality Fusion based Lung Cancer Survival Analysis with Self-Supervised Whole Slide Image Representation Learning.- Incorporating Spiking Neural Network for Dynamic Vision Emotion Analysis.- PAT-Unet: Paired Attention Transformer for Efficient and Accurate Segmentation of 3D Medical Images.- Cell-CAEW: Cell Instance Segmentation based on ConvAttention and Enhanced Watershed.- A Comprehensive Multi-modal Domain Adaptative Aid Framework for Brain Tumor Diagnosis.- Joint Boundary-Enhanced and Topology-Preserving Dual-Path Network for Retinal Layer Segmentation in OCT Images with Pigment Epithelial Detachment.- Spatial Feature Regularization and Label Decoupling based Cross-Subject Motor Imagery EEG Decoding.- Autism spectrum disorder diagnosis using graph neural network based on graph pooling and self-adjust filter.- CDBIFusion: A Cross-Domain Bidirectional Interaction Fusion Network for PET and MRI Images.- LF-LVS: Label-Free Left Ventricular Segmentation for Transthoracic Echocardiogram.- Multi-atlas Representations based on Graph Convolutional Networks for Autism Spectrum Disorder Diagnosis.- MS-UNet: Swin Transformer U-Net with Multi-scale Nested Decoder for Medical Image Segmentation with Small Training Data.- GCUNET: Combining GNN and CNN for Sinogram Restoration in Low-Dose SPECT Reconstruction.- A two-stage whole body bone SPECT scan image inpainting algorithm for residual urine artifacts based on contextual attention.

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Book SynopsisUnmanned aircraft systems (UAS) are rapidly emerging as flexible platforms for capturing imagery and other data across the sciences. Many colleges and universities are developing courses on UAS-based data acquisition. Fundamentals of Capturing and Processing Drone Imagery and Data is a comprehensive, introductory text on how to use unmanned aircraft systems for data capture and analysis. It provides best practices for planning data capture missions and hands-on learning modules geared toward UAS data collection, processing, and applications.FEATURES Lays out a step-by-step approach to identify relevant tools and methods for UAS data/image acquisition and processing. Provides practical hands-on knowledge with visual interpretation, well-organized and designed for a typical 16-week UAS course offered on college and university campuses. Suitable for all levels of readers and does not require prior knowTable of ContentsPart I: Getting Started with Drone Imagery and Data 1. Introduction to Capturing and Processing Drone Imagery and Data 2. An Introduction to Drone Remote Sensing and Photogrammetry 3. Choosing a Sensor for UAS Imagery Collection 4. Mission Planning for Capturing UAS Imagery 5. Drone Regulations: What You Need to Know before You Fly 6. Structure from Motion (SfM) Workflow for Processing Drone Imagery 7. Aerial Cinematography with UAS Part II: Hands-On Applications Using Drone Imagery and Data 8. Planning Unoccupied Aircraft Systems (UAS) Missions 9. Aligning and Stitching Drone-Captured Images 10. Counting Wildlife from Drone-Captured Imagery Using Visual and Semi-Automated Techniques 11. Terrain and Surface Modeling of Vegetation Height Using Simple Linear Regression 12. Assessing the Accuracy of Digital Surface Models of an Earthen Dam Derived from SfM Techniques 13. Estimating Forage Mass from Unmanned Aircraft Systems in Rangelands 14. Applications of UAS-Derived Terrain Data for Hydrology and Flood Hazard Modeling 15. Comparing UAS and Terrestrial Laser Scanning Methods for Change Detection in Coastal Landscapes 16. Digital Preservation of Historical Heritage Using 3D Models and Augmented Reality 17. Identifying Burial Mounds and Enclosures Using RGB and Multispectral Indices Derived from UAS Imagery 18. Detecting Scales of Drone-Based Atmospheric Measurements Using Semivariograms 19. Assessing the Greenhouse Gas Carbon Dioxide in the Atmospheric Boundary Layer

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Book SynopsisUnmanned aircraft systems (UAS) are rapidly emerging as flexible platforms for capturing imagery and other data across the sciences. Many colleges and universities are developing courses on UAS-based data acquisition. Fundamentals of Capturing and Processing Drone Imagery and Data is a comprehensive, introductory text on how to use unmanned aircraft systems for data capture and analysis. It provides best practices for planning data capture missions and hands-on learning modules geared toward UAS data collection, processing, and applications.FEATURES Lays out a step-by-step approach to identify relevant tools and methods for UAS data/image acquisition and processing. Provides practical hands-on knowledge with visual interpretation, well-organized and designed for a typical 16-week UAS course offered on college and university campuses. Suitable for all levels of readers and does not require prior knowTable of ContentsPart I: Getting Started with Drone Imagery and Data 1. Introduction to Capturing and Processing Drone Imagery and Data 2. An Introduction to Drone Remote Sensing and Photogrammetry 3. Choosing a Sensor for UAS Imagery Collection 4. Mission Planning for Capturing UAS Imagery 5. Drone Regulations: What You Need to Know before You Fly 6. Structure from Motion (SfM) Workflow for Processing Drone Imagery 7. Aerial Cinematography with UAS Part II: Hands-On Applications Using Drone Imagery and Data 8. Planning Unoccupied Aircraft Systems (UAS) Missions 9. Aligning and Stitching Drone-Captured Images 10. Counting Wildlife from Drone-Captured Imagery Using Visual and Semi-Automated Techniques 11. Terrain and Surface Modeling of Vegetation Height Using Simple Linear Regression 12. Assessing the Accuracy of Digital Surface Models of an Earthen Dam Derived from SfM Techniques 13. Estimating Forage Mass from Unmanned Aircraft Systems in Rangelands 14. Applications of UAS-Derived Terrain Data for Hydrology and Flood Hazard Modeling 15. Comparing UAS and Terrestrial Laser Scanning Methods for Change Detection in Coastal Landscapes 16. Digital Preservation of Historical Heritage Using 3D Models and Augmented Reality 17. Identifying Burial Mounds and Enclosures Using RGB and Multispectral Indices Derived from UAS Imagery 18. Detecting Scales of Drone-Based Atmospheric Measurements Using Semivariograms 19. Assessing the Greenhouse Gas Carbon Dioxide in the Atmospheric Boundary Layer

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Book SynopsisHybrid Computational Intelligent Systems Modeling, Simulation and Optimization unearths the latest advances in evolving hybrid intelligent modeling and simulation of human-centric data-intensive applications optimized for real-time use, thereby enabling researchers to come up with novel breakthroughs in this ever-growing field.Salient features include the fundamentals of modeling and simulation with recourse to knowledge-based simulation, interaction paradigms, and human factors, along with the enhancement of the existing state of art in a high-performance computing setup. In addition, this book presents optimization strategies to evolve robust and failsafe intelligent system modeling and simulation.The volume also highlights novel applications for different engineering problems including signal and data processing, speech, image, sensor data processing, innovative intelligent systems, and swarm intelligent manufacturing systems.Features:A Table of ContentsChapter 1 Creating ratings of agricultural universities based on their digital footprint Chapter 2 Mechatronic Complex’s Fuzzy System for Fixating Moving Objects Chapter 3 Quad Sensor-based Soil-Moisture Prediction using Machine Learning Chapter 4 Stability Analysis for a Diffusive Ratio-dependent Predator-prey Model involving two Delays Chapter 5 Analysis and Prediction of Physical Fitness Test Data of College Students Based on Grey Model Chapter 6 Analysis and Research on Book Borrowing Tendency Based on Apriori Algorithm Chapter 7 Performance Evaluation of Cargo Inspection Systems with the Function of Materials Recognition Chapter 8 Automated Medical Report Generation on Chest X-Ray Images using Co-Attention mechanism Chapter 9 An Energy Efficient Secured Arduino based Home Automation using Android Interface Chapter 10 A Multithreaded Android App to Notify Available `CoWIN’ Vaccination Slots to Multiple Recipients Chapter 11 Binary MMBAIS for Feature Selection Problem Chapter 12 Audio to Indian Sign Language Interpreter (AISLI) using Machine Translation and NLP Techniques Chapter 13 Fragile Medical Image Watermarking using Auto-generated Adaptive Key based Encryption Chapter 14 Designing of a Solution Model for Global Warming and Climate Change using Machine Learning and Data Engineering Techniques Chapter 15 Human Age Estimation using sit-to-stand exercise Data-driven Decision Making by Neural Network Chapter 16 Feature Based Suicide Ideation Detection from Twitter Data Using Machine Learning Techniques Chapter 17 Analyzing the role of Indian Media during the second wave of COVID using Topic Modeling Chapter 18 Hardware Efficient FIR Filter Design using Fast Converging Flower Pollination Algorithm - A Case Study of denoising PCG Signal Chapter 19 Voice Recognition System Using Deep Learning Chapter 20 Modified Harris Hawk Optimization Algorithm for Multi-level Image Thresholding Chapter 21 An automatic probabilistic framework for detection and segmentation of tumor in brain MRI images Chapter 22 Comparative Study of Generative Adversarial Networks for Sensor Data Generation based Remaining Useful Life Classification Chapter 23 Towards a Framework for Implementation of Quantum-Inspired Evolutionary Algorithm on Noisy Intermediate Scale Quantum Devices (IBMQ) for Solving Knapsack Problems

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Book SynopsisThis Lab Manual is a companion to the textbook Remote Sensing and Digital Image Processing with R. It covers examples of natural resource data analysis applications including numerous, practical problem-solving exercises, and case studies that use the free and open-source platform R. The intuitive, structural workflow helps students better understand a scientific approach to each case study in the book and learn how to replicate, transplant, and expand the workflow for further exploration with new data, models, and areas of interest. Features Aims to expand theoretical approaches of remote sensing and digital image processing through multidisciplinary applications using R and R packages. Engages students in learning theory through hands-on real-life projects. All chapters are structured with solved exercises and homework and encourage readers to understand the potential and the limitations of the environments. CoversTable of Contents1. Principles of R Language in Remote Sensing and Digital Image Processing 2. Introduction to Remote Sensing and Digital Image Processing with R 3. Remote Sensing of Electromagnetic Radiation 4. Remote Sensing Sensors and Satellite Systems 5. Remote Sensing of Vegetation 6. Remote Sensing of Water 7. Remote Sensing of Soils, Rocks, and Geomorphology 8. Remote Sensing of the Atmosphere 9. Scientific Applications of Remote Sensing and Digital Image Processing for Project Design 10. Visual Interpretation and Enhancement of Remote Sensing Images 11. Unsupervised Classification of Remote Sensing Images 12. Supervised Classification of Remote Sensing Images 13. Uncertainty and Accuracy Analysis in Remote Sensing and Digital Image Processing 14. Scientific Applications of Remote Sensing and Digital Image Processing to Elaborate Articles

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Book SynopsisDifferently oriented specialists and students involved in image processing and analysis need to have a firm grasp of concepts and methods used in this now widely utilized area. This book aims at being a single-source reference providing such foundations in the form of theoretical yet clear and easy to follow explanations of underlying generic concepts.Medical Image Processing, Reconstruction and Analysis Concepts and Methods explains the general principles and methods of image processing and analysis, focusing namely on applications used in medical imaging. The content of this book is divided into three parts: Part I Images as Multidimensional Signals provides the introduction to basic image processing theory, explaining it for both analogue and digital image representations. Part II Imaging Systems as Data Sources offers a non-traditional view on imaging modalities, explaining their prTable of ContentsPART I Images as Multidimensional Signals Chapter 1 Analogue (Continuous-Space) Image Representation Chapter 2 Digital Image Representation PART II Imaging Systems as Data Sources Chapter 3 Planar X-Ray Imaging Chapter 5 Magnetic Resonance Imaging Chapter 6 Nuclear Imaging Chapter 7 Ultrasonography Chapter 8 Other Modalities PART III Image Processing and Analysis Chapter 9 Reconstructing Tomographic Images Chapter 10 Image Fusion Chapter 11 Image Enhancement Chapter 12 Image Restoration Chapter 13 Lower-Level Image Analysis Chapter 14 Selected Higher-Level Image Analysis Methods Chapter 15 Medical Image Processing Environment

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Book SynopsisMathematical modelling is an important part of nuclear medicine. Therefore, several chapters of this book have been dedicated towards describing this topic. In these chapters, an emphasis has been put on describing the mathematical modelling of the radiation transport of photons and electrons, as well as on the transportation of radiopharmaceuticals between different organs and compartments. It also includes computer models of patient dosimetry. Two chapters of this book are devoted towards introducing the concept of biostatistics and radiobiology. These chapters are followed by chapters detailing dosimetry procedures commonly used in the context of diagnostic imaging, as well as patient-specific dosimetry for radiotherapy treatments. For safety reasons, many of the methods used in nuclear medicine and molecular imaging are tightly regulated. Therefore, this volume also highlights the basic principles for radiation protection. It discusses the process of how guidelines and reTable of ContentsContentsPreface...............................................................................................................................viiEditor..................................................................................................................................ixContributors.......................................................................................................................xiChapter 1 Introduction to Biostatistics..........................................................................1Johan Gustafsson and Markus NilssonChapter 2 Radiobiology....................................................................................................17Lidia Strigari and Marta CremonesiChapter 3 Diagnostic Dosimetry.............................................................................................33Lennart Johansson† and Martin AnderssonChapter 4 Time- activity Curves: Data, Models, Curve Fitting, and Model Selection..........................69Gerhard GlattingChapter 5 Tracer Kinetic Modelling and Its Use in PET Quantification..............................................83Mark Lubberink and Michel KooleChapter 6 Principles of Radiological Protection in Healthcare..........................................................101Soren MattssonChapter 7 Controversies in Nuclear Medicine Dosimetry..................................................................115Michael G. StabinChapter 8 Monte Carlo Simulation of Photon and Electron Transport in Matter..............................123Jose M. Fernandez-​VareaChapter 9 Patient Models for Dosimetry Applications..........................................................141Michael G. StabinChapter 10 Patient- specific Dosimetry Calculations.............................................................155Manuel Bardies, Naomi Clayton, Gunjan Kayal, and Alex Vergara GilChapter 11 Whole- body Dosimetry..................................................169Jonathan GearChapter 12 Personalized Dosimetry in Radioembolization........................................................................................183Remco Bastiaannet and Hugo W.A.M. de JongChapter 13 Thyroid Imaging and Dosimetry..........................................................................207Michael Lassmann and Heribert HanscheidChapter 14 Bone Marrow Dosimetry........................................................................................223Cecilia HindorfChapter 15 Cellular and Multicellular Dosimetry...................................................................235Roger W. HowellChapter 16 Alpha- particle Dosimetry................................................................267Stig PalmChapter 17 Staff Radiation Protection........................................................................275Lena JonssonChapter 18 IAEA Support to Nuclear Medicine...........................................................293Gian Luca Poli

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book Synopsis''Phase-only Fresnel holograms,'' which can be displayed on a single SLM without the need for lenses or complicated optical accessories, substantially simplifies 3-D holographic display systems. Exploring essential concepts, theories, and formulations of these phase-only Fresnel holograms, this book provides comprehensive coverage of modern methods for generating such holograms, which pave the way for commercial products such as compact holographic projectors, heads-up displays, and data security enhancement. Relevant MATLAB codes are provided for readers to implement and evaluate the theories and formulations of different methods, and can be used as a quick start framework for further research and development. This is a crucial and up-to-date treatment of phase-only Fresnel holograms for students and researchers in electrical and electronic engineering, computer science/engineering, applied physics, information technology, and multimedia technology, as well as engineers and scientistTrade Review'MATLAB programs, scattered throughout the book, might be the starting point for further research and be used as an educational tool (there are exercises at the end of each chapter). References are adequate, useful and up-to-date, while the index improves readability. The book is specialized, and I recommend it to those who actively use SLMs for holographic manipulation of light, but it can be useful to a more general community dealing with digital holography.' Dejan Pantelić, Optics & Photonics NewsTable of ContentsPreface; 1. Introduction to Digital Holography; 2. Fast Methods for Computer Generated Holography; 3. Generation of Phase-Only Fresnel Hologram; 4. Conversion of Complex-Valued Holograms to Phase-Only Holograms; 5. Applications of Phase-Only Hologram in Display, Holographic Encryption and Steganography.

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How can we provide guarantees of behaviours for autonomous systems such as driverless cars? This tutorial text, for professionals, researchers and graduate students, explains how autonomous systems, from intelligent robots to driverless cars, can be programmed in ways that make them amenable to formal verification. The authors review specific definitions, applications and the unique future potential of autonomous systems, along with their impact on safer decisions and ethical behaviour. Topics discussed include the use of rational cognitive agent programming from the Beliefs-Desires-Intentions paradigm to control autonomous systems and the role model-checking in verifying the properties of this decision-making component. Several case studies concerning both the verification of autonomous systems and extensions to the framework beyond the model-checking of agent decision-makers are included, along with complete tutorials for the use of the freely-available verifiable cognitive agent too

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