Pattern Recognition
Springer International Publishing (Verlag)
978-3-031-78127-8 (ISBN)
- Noch nicht erschienen - erscheint am 09.01.2025
- Versandkostenfrei innerhalb Deutschlands
- Auch auf Rechnung
- Verfügbarkeit in der Filiale vor Ort prüfen
- Artikel merken
The multi-volume set of LNCS books with volume numbers 15301-15333 constitutes the refereed proceedings of the 27th International Conference on Pattern Recognition, ICPR 2024, held in Kolkata, India, during December 1-5, 2024. The 963 papers presented in these proceedings were carefully reviewed and selected from a total of 2106 submissions. They deal with topics such as Pattern Recognition; Artificial Intelligence; Machine Learning; Computer Vision; Robot Vision; Machine Vision; Image Processing; Speech Processing; Signal Processing; Video Processing; Biometrics; Human-Computer Interaction (HCI); Document Analysis; Document Recognition; Biomedical Imaging; Bioinformatics.
DeepEMD: A Transformer-based Fast Estimation of the Earth Mover's Distance.- Equivariant Neural Networks for TEM Virus Images Improves Data Efficiency.- AI Based Story Generation.- Deep learning models for inference on compressed signals with known or unknown measurement matrix.- Training point-based deep learning networks for forest segmentation with synthetic data.- Brain Age Estimation using Self-attention based Convolutional Neural Network.- IFSENet : Harnessing Sparse Iterations for Interactive Few-shot Segmentation Excellence.- Interpretable Deep Graph-level Clustering: A Prototype-based Approach.- A Saliency-Aware NR-IQA Method by Fusing Distortion Class Information.- A Guided Input Sampling-based Perturbative Approach for Explainable AI in Image-based Application.- Multi-target Attention Dispersion Adversarial Attack against Aerial Object Detector.- Mask-TS Net: Mask Temperature Scaling Uncertainty Calibration for Polyp Segmentation.- Label-expanded Feature Debiasing for Single Domain Generalization.- Infrared and Visible Image Fusion Based on CNN and Transformer Cross-Interaction with Semantic Modulations.- Mining Long Short-Term Evolution Patterns for Temporal Knowledge Graph Reasoning.- Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging.- A Weighted Discrete Wavelet Transform-based Capsule Network for Malware Classification.- Data-driven Spatiotemporal Aware Graph Hybrid-hop Transformer Network for Traffic Flow Forecasting.- Automatic Diagnosis Model of Gastrointestinal Diseases Based on Tongue Images.- TinyConv-PVT: A Deeper Fusion Model of CNN and Transformer for Tiny Dataset.- SCAD-Net: Spatial-Channel Attention and Depth-map Analysis Network for Face Anti-Spoofing.- Next Generation Loss Function for Image Classification.- NAOL: NeRF-Assisted Omnidirectional Localization.- EdgeConvFormer: an unsupervised anomaly detection method for multivariate time series.- Lighten CARAFE: Dynamic Lightweight Upsampling with Guided Reassemble Kernels.- Hand over face gesture classification with feature driven vision transformer and supervised contrastive learning.- TabSeq: A Framework for Deep Learning on Tabular Data via Sequential Ordering.- GraFix: A Graph Transformer with Fixed Attention based on the WL Kernel.- Multi-Modal Deep Emotion-Cause Pair Extraction for Video Corpus.
Erscheint lt. Verlag | 9.1.2025 |
---|---|
Reihe/Serie | Lecture Notes in Computer Science |
Zusatzinfo | XXXVII, 468 p. 149 illus., 139 illus. in color. |
Verlagsort | Cham |
Sprache | englisch |
Original-Titel | Pattern Recognition |
Maße | 155 x 235 mm |
Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
Schlagworte | Artificial Intelligence • Bioinformatics • Biomedical Imaging • biometrics • computer vision • Document Analysis • document recognition • Human-Computer Interaction (HCI) • Image Processing • machine learning • Machine vision • pattern recognition • Robot vision • Signal Processing • Speech processing • Video Processing |
ISBN-10 | 3-031-78127-9 / 3031781279 |
ISBN-13 | 978-3-031-78127-8 / 9783031781278 |
Zustand | Neuware |
Haben Sie eine Frage zum Produkt? |
aus dem Bereich