Del 15705 - Lecture Notes in Computer Science

Reproducible Research in Pattern Recognition

Fifth International Workshop, RRPR 2024, Kolkata, India, December 1, 2024, Revised Selected Papers

Häftad, Engelska, 2025

AvBertrand Kerautret,Federico Bolelli,Miguel Colom,Daniel Lopresti

1 539 kr

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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.

Produktinformation

Utgivningsdatum2025-09-01
Mått155 x 235 x 10 mm
Vikt277 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor163
FörlagSpringer International Publishing AG
ISBN9783031978210

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Hårdvara inom Data och IT
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Systemvetenskap och AI inom Data och IT
Informationsteknik: allmänt inom Data och IT

.- 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.