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[null,null,"Titolo",null,"Computational Visual Media : 13th International Conference, CVM 2025, Hong Kong SAR, China, April 19–21, 2025, Proceedings, Part I / / edited by Piotr Didyk, Junhui Hou"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"981-9658-09-8"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XXV, 454 p. 171 illus., 167 illus. in color.)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 15663"]
[null,null,"Disciplina",null,"006.37"]
[null,null,"Soggetti",null,"Computer vision"]
[{"$nulls":4},"Pattern recognition systems"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Computer graphics"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Computer Vision"]
[{"$nulls":4},"Automated Pattern Recognition"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"Computer Graphics"]
[{"$nulls":4},"Artificial Intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Medical Image Analysis AGTCNet: Hybrid Network Based on AGT and Curvature Information for Skin Lesion Detection -- Among General Spine Segmentation with Multi-scale and Discriminate Feature Fusion -- SSCL: A Spatial-Spectral and Commonality Learning Network for Semi-Supervised Medical Image Segmentation -- A Multiscale Edge-Guided Polynomial Approximation Network for Medical Image Segmentation -- HIFNet: Medical Image Segmentation Network Utilizing Hierarchical Attention Feature Fusion -- Ynet: medical image segmentation model based on wavelet transform boundary enhancement -- An Effective Algorithm for Skin Disease Segmentation Combining inter-channel Features and Spatial Feature Enhancement -- Detection and Recognition A Comprehensive Framework for Fine-Grained Object Recognition in Remote Sensing -- Towards Reflected Object Detection: A Benchmark -- Consensus-aware Balance Learning "]
[{"$nulls":4},"for Sexually Suggestive Video Classification -- LightStar-Net: A Pseudo-Raw Space Enhancement for Efficient Low-Light Object Detection -- DASSF: Dynamic-Attention Scale-Sequence Fusion for Aerial Object Detection -- Image Enhancement and Generation Degradation-Aware Frequency-Separated Transformer for Blind Super-Resolution -- MAAU-UIE : Multiple Attention Aggregation U-Net for Underwater Image Enhancement -- MANet-CycleGAN: An Unsupervised LDCT Image Denoising Method Based on Channel Attention and Multi-Scale Features -- M3: Manipulation Mask Manufacturer for Arbitrary-Scale Super-Resolution Mask -- Agent-Conditioned Multi-Contrast MRI Super-Resolution for Cross-Subject -- Vision Modeling in Complex Scenarios SEA-Net: A Severity-Aware Network with Visual Prompt Tuning for Underwater Semantic Segmentation -- LAGNet: A Location-Aware Guidance Network for Weak and Strip Defect Detection -- Weighted Spatiotemporal Feature and Multi-task Learning for Masked Facial Expression Recognition -- MBGNet：Mamba-Based Boundary-Guided Multimodal Medical Image Segmentation Network -- MSD: A Mask-Guided and Semantic-Guided Diffusion-based Framework for Stone Surface Defect Detection -- A New Heterogeneous Mixture of Experts Model for Deepfake Detection."]
[null,null,null,"Sommario/riassunto","This book constitutes the refereed proceedings of CVM 2025, the 13th International Conference on Computational Visual Media, held in Hong Kong SAR, China, in April 2025. The 67 full papers were carefully reviewed and selected from 335 submissions. The papers are organized in topical sections as follows: Part I: Medical Image Analysis, Detection and Recognition, Image Enhancement and Generation, Vision Modeling in Complex Scenarios Part II: 3D Geometry and Rendering, Generation and Editing, Image Processing and Optimization Part III: Image and Video Analysis, Multimodal Learning, Geometrical Processing, Applications."]