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Fred / De Marsico / Castrillón-Santana

Pattern Recognition Applications and Methods

14th International Conference, ICPRAM 2025, Porto, Portugal, February 23-25, 2025, Revised Selected Papers

Medium: Buch
ISBN: 978-3-032-28660-4
Verlag: Springer Nature Switzerland AG
Erscheinungstermin: 26.07.2026
vorbestellbar, Erscheinungstermin ca. Juli 2026

This book constitutes extended and revised versions of selected papers from the 14th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2025, held in Porto, Portugal, during February 23-25, 2025.

The 7 full papers and 4 short papers presented in this book were carefully reviewed and selected from 132 submissions. They were organized into the following topical sections: Theory and Methods; Applications.


Produkteigenschaften


  • Artikelnummer: 9783032286604
  • Medium: Buch
  • ISBN: 978-3-032-28660-4
  • Verlag: Springer Nature Switzerland AG
  • Erscheinungstermin: 26.07.2026
  • Sprache(n): Englisch
  • Auflage: Erscheinungsjahr 2026
  • Serie: Lecture Notes in Computer Science
  • Produktform: Kartoniert
  • Seiten: 219
  • Format (B x H): 155 x 235 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Herausgeber

.- Theory and Methods.

.- ImageNet Accuracy and Model Popularity Don’t Always Select the Best Pretrained Model for Transfer Learning.

.- Meta-Learned Implicit Neural Representations for Scalable and Fast Hyperspectral Image Compression.

.- Beyond the Pixels: Robust Cattle Segmentation for Enhanced Livestock Monitoring.

.- XPD-MelVAE: A Hybrid Conditional ß-VAE and ML Framework for Explainable Early Parkinson’s Disease Detection from Mel-Spectrograms.

.- Generative Sparse Data Augmentation Dealing with Performance Evaluation.

.- Sparse Machine Learning Models for Predicting Malnutrition in Children Under Five.

.- Principal Direction-Based 2-Gaussian Fitting Method.

.- On the Use of Union and Intersection for K-Fold Feature Selection.

.- Applications.

.- Multi-Object 2DGS: Occlusion-Robust Background Removal for Multiple Object Models.

.- Enhancing Real-Time Object Detection on NVIDIA Jetson Nano via Class-Specific Dataset Splitting for YOLO Models.

.- Spatio-Temporal Transformers for Long-Term NDVI Forecasting.