Reproducible Research in Pattern Recognition, Kartoniert / Broschiert
Reproducible Research in Pattern Recognition
- Fifth International Workshop, RRPR 2024, Kolkata, India, December 1, 2024, Revised Selected Papers
(soweit verfügbar beim Lieferanten)
- Herausgeber:
- Bertrand Kerautret, Federico Bolelli, Miguel Colom, Daniel Lopresti
- Verlag:
- Springer, 09/2025
- Einband:
- Kartoniert / Broschiert
- Sprache:
- Englisch
- ISBN-13:
- 9783031978210
- Artikelnummer:
- 12466081
- Umfang:
- 176 Seiten
- Gewicht:
- 277 g
- Maße:
- 235 x 155 mm
- Stärke:
- 10 mm
- Erscheinungstermin:
- 1.9.2025
- Hinweis
-
Achtung: Artikel ist nicht in deutscher Sprache!
Weitere Ausgaben von Reproducible Research in Pattern Recognition |
Preis |
---|
Klappentext
.- 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.
