Reproducible Research in Pattern Recognition (eBook, PDF)

Fifth International Workshop, RRPR 2024, Kolkata, India, December 1, 2024, Revised Selected Papers
Redaktion: Kerautret, Bertrand; Lopresti, Daniel; Colom, Miguel; Bolelli, Federico

• Format: PDF

Produktbeschreibung

This book constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Reproducible Research in Pattern Recognition, RRPR 2024, held in Kolkata, India, on December 1, 2024.

The 5 full papers and 8 short papers included in this book were carefully reviewed and selected from 20 submissions. RRPR 2024 book cover advances in platforms on reproducibility and new reproducible research results.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 163
• Erscheinungstermin: 31. August 2025
• Englisch
• ISBN-13: 9783031978227
• Artikelnr.: 75336013

Inhaltsangabe

.- Reproducible Research Framework and Results.
.- A Benchmark for Automated Vickers Hardness Testing.
.- Scratch Assay Assessment Benchmark.
.- Reducing Run-to-Run Variability in Neural Networks: A Comparative Study of Weight Optimization Methods.
.- A Minimal Neural Network for Reproducible Gesture Recognition on Knitted Capacitive Touch Sensors.
.- Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization.
.- BarBeR - Barcode Benchmark Repository:Implementation and Reproducibility Notes.
.- Research Reproducibility Paper: Learning Neural Networks forMulti-label Medical Image Retrieval Using Hamming Distance Fabricated with Jaccard Similarity Coefficient.
.- Resolution-Robust Medical Image Registration Method Based on Fourier Neural Operator: Implementation and Reproducibility Aspects.
.- MeDiANet Implementation and Reproducibility Details.
.- On Reproducibility of Graph Neural Network for Facial Palsy and Paresis Assessment: Effects of Pose Variability in Dataset.
.- Implementatipn and Reproducibility Notes on GolfSwing Dataset and GolfPose Models.
.- Implementation and Reproducibility Notes on EMPATH: Enhancing Word-Level Sign Language Recognition.
.- Exploring the Impact of Model Parameters and Components on Video Saliency Prediction with Foundation Models.