Pattern Recognition and Computer Vision
Springer Nature (Verlag)
978-981-97-8498-1 (ISBN)
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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.
Anchored Supervised Contrastive Learning for Long-Tailed Medical Image Regression.- Dynamic Feature Fusion Based on Consistency and Complementarity of Brain Atlases.- FUF-TransUNet: a transformer-based U-Net with fully utilize of features for liver and liver-tumor segmentation in CT images.- Dual-View Dual-Boundary Dual U-Nets for Multiscale Segmentation of Oral CBCT ImagesA Novel Diffusion Model with Wavelet Transform for Optic Disc and Cup Segmentation in Fundus Images.- STCTb: A Spatio-Temporal Collaborative Transformer Block for Brain Diseases Classification using fMRI Time Series.A Generalized Contrast-adjustment Guided Growth Method for Medical Image Segmentation.- MDNet: Morphology-Driven Weakly Supervised Polyp DetectionMMR-Sleep: A Multi-Channel and Multi-Receptive Field Sleep Stage recognition Model.- CPNet: Cross Prototype Network for Few-shot Medical Image Segmentation.- SBC-UNet: A Network Based on Improved Hourglass Attention Mechanism and U-Net for Medical Image Segmentation.- Bridge the gap of semantic context: A Boundary-guided Context Fusion UNet for Medical Image Segmentation.- Bilinear Fine-grained Classification of Ultrasound Images Integrated with Interpretable Radiomics.- GCNet: Global context-guided uncertainty boundary for polyp segmentation.- Comprehensive Transformer Integration Network (CTIN): Advancing Endoscopic Disease Segmentation with Hybrid Transformer Architecture.- IPM: An Intelligent Component for 3D Brain Tumor Segmentation Integrating Semantic Extractor and Pixel RefinerEdge-Net: A Self-supervised Medical Image Segmentation Model Based on Edge Attention.- Fundus image disease diagnosis and quality assessment based on dual-task collaborative optimization.- Multi-modality Correlation Learning Network for Pediatric Ventricular Septal Defects Identification.- MFIS-net: A Deep Learning Framework for Left Atrial Segmentation.- Semi-Supervised Gland Segmentation via Label Purification and Reliable Pixel Learning.- DFANet: A Dual-stream Deep Feature Aware Network for Multi-focus Image FusionMST-Gait：Application of Multi-Scale Temporal Modeling to Gait Recognition.- Identity-Preserving Animal Image Generation for Animal Individual Identification.- FIL-FLD: Few-shot Incremental Learning with EMD Metric for High Generalization Fingerprint Liveness DetectionText Based Unsupervised Domain Generalization Person Re-identificationSF-Gait: Two-Stage Temporal Compression Network for Learning Gait Micro-Motions and Cycle Patterns.- Coarse-to-Fine Domain Adaptation for Cross-subject EEG Emotion Recognition with Contrastive Learning.- Face Anti-spoofing based on Multi-view Anomaly Detection. -Online Signature Verification Based on Recurrent Attentional Time-Delay Neural Networks.- Multimodal finger recognition based on feature fusion attention for fingerprints, finger-veins, and f inger-knuckle-prints. -Hierarchical Discrepancy-aware Interaction Network for Face Forgery DetectionAU-vMAE: Knowledge-Guide Action Units Detection via Video Masked AutoencoderTransformer-based Multimodal Spatial-Temporal Fusion for Gait Recognition.- Multi-level Distributional Discrepancy Enhancement for Cross Domain Face Forgery Detection.- Unsupervised person Re-ID based on nonlinear asymmetric metric learning.- FR-watermarking: A Fusion Framework for Face-Based Digital Watermarking.- Enhancing Semi-Dense Feature Matching through Probabilistic Modeling of Cascaded Supervision and Consistency.- Concentrating Estimation Attention: Human Prior Constrained Methods for Robust Classification.
| Erscheint lt. Verlag | 18.12.2024 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science |
| Zusatzinfo | 189 Illustrations, black and white; Approx. 500 p. |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 981-97-8498-0 / 9819784980 |
| ISBN-13 | 978-981-97-8498-1 / 9789819784981 |
| Zustand | Neuware |
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