This companion paper provides a detailed account of the EMPATH framework [8], which integrates Ensemble Learning, MediaPipe Holistic for gesture tracking, and an Attention-based Transformer model, along with practical insights on designing an effective recognition model. It focuses on the architecture, workflow and offers an in-depth explanation of the interpolation method for handling missing hand keypoints. Additionally, the paper highlights ablation studies on augmentation techniques and key limitations encountered during development and implementation, providing valuable insights for enhancing the reproducibility and performance of word-level sign language recognition using EMPATH.
As a companion to the ICPR 2024 accepted paper "SalFoM: Dynamic Saliency Prediction with Video Foundation Models", this work investigates how various model parameters and components impact its performance. Since SalFoM represents the first effort of its kind in this field, the additional experiments presented here are designed to provide insights into the application of video foundation models for dynamic saliency prediction. This is achieved by exploring different aspects of the model's architecture and the use of large video models. Additionally, this work analyzes the impact of various strategies for defining training objectives on the model's learning capabilities and overall performance. The code is available at https://github.com/mr17m/SalFoM-Video-Saliency-Prediction.
This paper provides a detailed description of how to install, set up, and use "BarBeR" (Barcode Benchmark Repository) to reproduce the results presented in the ICPR 2024 paper "BarBeR: A Barcode Benchmarking Repository". The paper details the tests available in the repository and how the configuration parameters affect and influence experimental results.
Automated Vickers hardness measurement methods must be robust and versatile to cope with materials with rough and noisy patterned surfaces. The behaviour of several state-of-the-art algorithms for automated Vickers hardness measurement is evaluated and compared for their respective robustness and accuracy. The evaluation is based on real world industrial data, collected in a Vickers indentation dataset. Addressing the current lack of publicly available (annotated) Vickers indentation data, and in order to provide a benchmark for further investigations and experiments, we release the databases used in this work, together with expert groundtruth covering diagonal measurement and segmentation masks, and make them available on-line for public open access.
This article is a companion paper to our publication for ICPR 2024, "FNOReg: Resolution-Robust Medical Image Registration Method Based on Fourier Neural Operator" [7], in which we proposed an algorithm for medical image registration that offers robustness to input data resolution. This companion paper presents the method's implementation, the methodology for hyperparameter selection, and other details that ensure the reproducibility of our research.
