In this paper, a compact, cost-effective, and fast rotary speckle projector (RSP) is designed and manufactured for high-precision three-dimensional (3D) face data acquisition. Compared with the common speckle projectors, RSP uses a simple speckle pattern design method and has a good performance in high-speed projection and compact structure, which allows a flexible balance between measurement accuracy and time cost in a real acquisition task. Using a carefully designed rotation angle of the speckle mask, temporally and spatially non-correlative speckle patterns in the measurement volume can be generated. The rotation angle of the speckle mask is carefully checked and optimally selected via detailed theoretical analysis, simulation, and experiments to ensure 3D reconstruction accuracy across the reconstruction area. Subsequently, a binocular 3D face imaging system composed of the RSP and two cameras is constructed. With captured stereo speckle image pairs, we adopted our previously well-established spatial-temporal correlation method to determine the disparity. The accuracy of the 3D face imaging system was verified by using a real face mask, which is standardized by a certified, high-precision industrial 3D scanner. The real face data collection under various expressions has demonstrated that the proposed system also has a good performance for 3D face imaging in dynamic scenes.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1364/AO.430101 | DOI Listing |
Brain Stimul
January 2025
Department of Biomedical Engineering, 36 S Wasatch Dr, Salt Lake City, 84112, UT, United States.
Emerging neurostimulation methods aim to selectively modulate deep brain structures. Guiding these therapies has presented a substantial chal- lenge, since imaging modalities such as MRI limit the spectrum of benefi- ciaries. In this study, we assess the guidance accuracy of a neuronavigation method that does not require taking MRI scans.
View Article and Find Full Text PDFProg Neurobiol
January 2025
Section on Cognitive Neurophysiology and Imaging, National Institute of Mental Health; Bethesda, MD, USA; Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute; Bethesda, MD, USA. Electronic address:
The macaque cerebral cortex contains concentrations of neurons that prefer faces over inanimate objects. Although these so-called face patches are thought to be specialized for the analysis of facial signals, their exact tuning properties remain unclear. For example, what happens when an object by chance resembles a face? Everyday objects can sometimes, through the accidental positioning of their internal components, appear as faces.
View Article and Find Full Text PDFRadiography (Lond)
January 2025
Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health, Berlin, Germany.
Background: Facial recognition technology in medical imaging, particularly with head scans, poses privacy risks due to identifiable facial features. This study evaluates the use of facial recognition software in identifying facial features from head CT scans and explores a defacing pipeline using TotalSegmentator to reduce re-identification risks while preserving data integrity for research.
Methods: 1404 high-quality renderings from the UCLH EIT Stroke dataset, both with and without defacing were analysed.
Trials
January 2025
Center for Research in Neuropsychology and Cognitive and Behavioral Intervention, Faculty of Psychology and Education Sciences, University of Coimbra, Coimbra, Portugal.
Background: Breast cancer is the most diagnosed cancer in women worldwide and carries a considerable psychosocial burden. Interventions based on Acceptance and Commitment Therapy (ACT) and compassion-based approaches show promise in improving adjustment and quality of life in people with cancer. The Mind programme is an integrative ACT and compassion-based intervention tailored for women with breast cancer, which aims to prepare women for survivorship by promoting psychological flexibility and self-compassion.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Information Systems, University of Haifa, Haifa, Israel.
This study explores the question whether Artificial Intelligence (AI) can outperform human experts in animal pain recognition using sheep as a case study. It uses a dataset of N = 48 sheep undergoing surgery with video recordings taken before (no pain) and after (pain) surgery. Four veterinary experts used two types of pain scoring scales: the sheep facial expression scale (SFPES) and the Unesp-Botucatu composite behavioral scale (USAPS), which is the 'golden standard' in sheep pain assessment.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!