Multimodal data acquisition at <scp>SARS‐CoV</scp>‐2 drive through screening centers: Setup description and experiences in Saarland, Germany

Flotho, Philipp; Bhamborae, Mayur J.; Grün, Tobias; Trenado, Carlos; Thinnes, David; Limbach, Dominik; Strauß, Daniel J.

doi:10.1002/jbio.202000512

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Viral and bacterial, respiratory pathogens were tested using multiplex polymerase chain reaction (Multiplex PCR) assays. The camera array consisted of stereo RGB, near infrared (NIR) and thermal cameras as well as microphones [compare [50]]. A duration of 5-10 min was recorded, where participants answered questions in front of the camera.…”

Section: Methodsmentioning

confidence: 99%

Lagrangian motion magnification with double sparse optical flow decomposition

Flotho,

Heiss,

Steidl

et al. 2023

Front. Appl. Math. Stat.

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Microexpressions are fast and spatially small facial expressions that are difficult to detect. Therefore, motion magnification techniques, which aim at amplifying and hence revealing subtle motion in videos, appear useful for handling such expressions. There are basically two main approaches, namely, via Eulerian or Lagrangian techniques. While the first one magnifies motion implicitly by operating directly on image pixels, the Lagrangian approach uses optical flow (OF) techniques to extract and magnify pixel trajectories. In this study, we propose a novel approach for local Lagrangian motion magnification of facial micro-motions. Our contribution is 3-fold: first, we fine tune the recurrent all-pairs field transforms (RAFT) for OFs deep learning approach for faces by adding ground truth obtained from the variational dense inverse search (DIS) for the OF algorithm applied to the CASME II video set of facial micro expressions. This enables us to produce OFs of facial videos in an efficient and sufficiently accurate way. Second, since facial micro-motions are both local in space and time, we propose to approximate the OF field by sparse components both in space and time leading to a double sparse decomposition. Third, we use this decomposition to magnify micro-motions in specific areas of the face, where we introduce a new forward warping strategy using a triangular splitting of the image grid and barycentric interpolation of the RGB vectors at the corners of the transformed triangles. We demonstrate the feasibility of our approach by various examples.

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Section: Methodsmentioning

confidence: 99%

Lagrangian motion magnification with double sparse optical flow decomposition

Flotho,

Heiss,

Steidl

et al. 2023

Front. Appl. Math. Stat.

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Generalization Ability in Medical Image Analysis with Small-Scale Imbalanced Datasets: Insights from Neural Network Learning

Biloborodova,

Brosnan,

Skarga-Bandurova

et al. 2023

Lecture Notes in Computer Science

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Lagrangian Motion Magnification with Landmark-Prior and Sparse PCA for Facial Microexpressions and Micromovements

Flotho

Heiß

Steidl

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Facial analysis is a key component in a wide range of applications such as security, autonomous driving, entertainment, and healthcare. Despite the availability of various facial RGB datasets, the thermal modality, which plays a crucial role in life sciences, medicine, and biometrics, has been largely overlooked.To address this gap, we introduce the T-FAKE dataset, a new large-scale synthetic thermal dataset with sparse and dense landmarks. To facilitate the creation of the dataset, we propose a novel RGB2Thermal loss function, which enables the transfer of thermal style to RGB faces. By utilizing the Wasserstein distance between thermal and RGB patches and the statistical analysis of clinical temperature distributions on faces, we ensure that the generated thermal images closely resemble real samples. Using RGB2Thermal style transfer based on our RGB2Thermal loss function, we create the T-FAKE dataset, a large-scale synthetic thermal dataset of faces. Leveraging our novel T-FAKE dataset, probabilistic landmark prediction, and label adaptation networks, we demonstrate significant improvements in landmark detection methods on thermal images across different landmark conventions. Our models show excellent performance with both sparse 70-point landmarks and dense 478-point landmark annotations. Our code and models are available at https://github.com/phflot/tfake.

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Multimodal data acquisition at SARS‐CoV‐2 drive through screening centers: Setup description and experiences in Saarland, Germany

Cited by 3 publications

References 16 publications

Lagrangian motion magnification with double sparse optical flow decomposition

Lagrangian motion magnification with double sparse optical flow decomposition

Generalization Ability in Medical Image Analysis with Small-Scale Imbalanced Datasets: Insights from Neural Network Learning

Lagrangian Motion Magnification with Landmark-Prior and Sparse PCA for Facial Microexpressions and Micromovements

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