Search Results for author:
Found 6 papers, 2 papers with code
Matrix-Valued Measures and Wishart Statistics for Target Tracking Applications
Ensuring sufficiently accurate models is crucial in target tracking systems.
Gradient-Based Interpretability Methods and Binarized Neural Networks
Binarized Neural Networks (BNNs) have the potential to revolutionize the way that deep learning is carried out in edge computing platforms.
Instant Automated Inference of Perceived Mental Stress through Smartphone PPG and Thermal Imaging
Background: A smartphone is a promising tool for daily cardiovascular measurement and mental stress monitoring.
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments.
DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings
Finally, a data augmentation technique, inspired from solutions for over-fitting problems in deep learning, is applied to allow the CNN to learn with a small-scale dataset from short-term measurements (e. g., up to a few hours).
Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging
In this paper, we propose a novel and robust approach for respiration tracking which compensates for the negative effects of variations in the ambient temperature and motion artifacts and can accurately extract breathing rates in highly dynamic thermal scenes.
