Pattern Recognition and Computer Vision

TRAMA

The 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. 

SOMMARIO

Growth 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.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9789819985579
• Collana: Lecture Notes in Computer Science
• Dimensioni: 235 x 155 mm Ø 801 gr
• Formato: Brossura
• Illustration Notes: XIV, 511 p. 187 illus., 172 illus. in color.
• Pagine Arabe: 511
• Pagine Romane: xiv