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Full Description
This book constitutes the refereed proceedings of ICIVIS2025, held in Hangzhou, China, in May 2025. This book provides a comprehensive collection of cutting-edge research and innovative solutions in image, vision and intelligent systems. The primary audience consists of academic researchers, industry professionals, and graduate students working in the domains of image, vision, and intelligent systems. This publication serves as an essential resource for those seeking to stay at the forefront of their respective fields, expand their knowledge, and explore new avenues for research and development.
Contents
FundusMorph: A Segmentation-Free Registration Framework for Deformable Multi-Modal Fundus Image Registration.- Research on the architecture of the generative artificial intelligence model in the power network security scenario.- A street-to-aerial image dataset with eye-tracking for cross-view matching.- Surface defect detection of hot rolled steel based on Dual-stripe Window Pyramid Network.-SGAGS: Semantic-Guided Adaptive 3D Gaussian Splatting.- Reconstruction Based Multimodal Sentiment Analysis with Uncertain Missing Modalities.- Adaptive Few-Shot Learning for Entity Relation Extraction.- An image-based human body reconstruction method based on improved PIFUHD.- E-MRNet: An Edge-Morphological Refinement Network with Multi-Scale Feature Enhancement for Medical Pathology Image Segmentation.- Deep Learning Approach for Shrimp Weight Estimation.- Tobacco Leaf Pest and Disease Detection Algorithm based on Improved YOLOv10n.- LA-PUNet:A Lightweight and Adaptive Framework for Efficient PointCloud Upsampling with Geometric Consistency.- Improvement of cervical cancer tissue pathology images classification and training method based on Few-Shot Learning.- StrokeNet: Multi-Path 3D-2D CNN Architecture for CT-based Stroke Detection.- The Application Value of Large Language Models in ACR TI-RADS Grading Evaluation of Thyroid Nodules.- A U-net Architecture with Kolmogorov-Arnold Networks and Hybrid Pooling Attention for Medical Image Segmentation.
