1. The encoder compresses the data into a latent space using latent variables which are highly representative of the original data but more difficult to understand for the human observer. Meanwhile, the decoder extracts these variables, in other words, reconstructs the original data. Hence, one ...
Further, the model may be impacted by pathologies related to tumors and artifacts related to arthroplasty since it is unclear how various pathologies manifest as 3DO body shape signals, if at all. Pseudo-DXA images underperformed on some of the DXA compositional values, mainly associated to ...
According to Aidot Winees: AiDot Winees F2/F2 Pro 2K Full HD Floodlight Camera with Dual Lens & 180° Panorama It offers the following specifications: 2K Ultra HD & 180° Panorama & Advanced Color Night VisionAI Intelligent Human/Pet/Vehicle/Package DetectionSmart PIR Motion Detection & ...
Since human activity durations are not always the same, this study proposes the adaptive moving average (AMA) filter in order to improve the reliability and accuracy of the real-time pose estimation. The moving average filter allows signals within a selected range of frequencies and time to be ...
Although normalization is a well-known concept, it has not been consistently used in 3D human pose estimation, especially with the 3D skeletons; We conducted cross-dataset experiments using the method of Martinez et al. [26] (Section 5), showing the negative effect of dataset biases on ...
Recently, monocular 3D human pose estimation (HPE) methods were used to accurately predict 3D pose by solving the ill-pose problem caused by 3D-2D projection. However, monocular 3D HPE still remains challenging owing to the inherent depth ambiguity and occlusions. To address this issue, previous...
In Reference [99], the authors reported the human-level efficiency of 3D CNN in the landmark detection of clinical 3D CT data. In [100], Saleh et al. proposed a 3D CNN based regression models for 3D pose estimation of anatomy using T2 weighted imaging. They showed that the proposed ...
Intelligent Carpet: Inferring 3D Human Pose From Tactile Signals. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 20–25 June 2021; pp. 11255–11265. [Google Scholar] Anzai, E.; Ren, D.; Cazenille, L.; Aubert-Kato, N....
of the sensor in the IMUs global space 𝑞′q′, with respect to its initial orientation, can be computed using equation (2), where 𝑞−1𝑐𝑎𝑙𝑖𝑏qcalib−1 denotes the inverse of the initial orientation of the sensor in the human body pose adopted during the I2S procedure...
ARHPE: Asymmetric Relation-aware Representation Learning for Head Pose Estimation in Industrial Human–machine Interaction. IEEE Trans. Ind. Inform. 2022, 1. [Google Scholar] [CrossRef] Ortiz-Padilla, V.E.; Ramírez-Moreno, M.A.; Presbítero-Espinosa, G.; Ramírez-Mendoza, R.A.; Lozoya-...