Exact geometric calibration of optical devices like projectors or cameras is the basis for utilizing them in quantitative metrological applications. The common state-of-the-art photogrammetric pinhole-imaging-based models with supplemental polynomial corrections fail in the presence of nonsymmetric or high-spatial-frequency distortions and in describing caustics efficiently. These problems are solved by our vision ray calibration (VRC), which is proposed in this paper. The VRC takes an optical mapping system modeled as a black box and directly delivers corresponding vision rays for each mapped pixel. The underlying model, the calibration process, and examples are visualized and reviewed, demonstrating the potential of the VRC.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1364/AO.49.005851 | DOI Listing |
Micromachines (Basel)
December 2024
Northwest Institute of Nuclear Technology, Xi'an 710024, China.
In this paper, a silicon carbide (SiC) phototransistor based on an open-base structure was fabricated and used as a radiation detector. In contrast to the exposed and thin sensitive region of traditional photo detectors, the sensitive region of the radiation detector was much thicker (30 μm), ensuring the high energy deposition of radiation particles. The response properties of the fabricated SiC npn radiation detector were characterized by high-energy X-ray illumination with a maximum X-ray photon energy of 30 keV.
View Article and Find Full Text PDFBMC Oral Health
January 2025
Center of Digital Dentistry, Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, No.22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, PR China.
Background: Establishing accurate, reliable, and convenient methods for enamel segmentation and analysis is crucial for effectively planning endodontic, orthodontic, and restorative treatments, as well as exploring the evolutionary patterns of mammals. However, no mature, non-destructive method currently exists in clinical dentistry to quickly, accurately, and comprehensively assess the integrity and thickness of enamel chair-side. This study aims to develop a deep learning work, 2.
View Article and Find Full Text PDFAdv Ther
January 2025
Bristol Myers Squibb, 1-2-1 Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan.
Introduction: This retrospective claims analysis characterized contemporary ulcerative colitis (UC) treatment patterns and investigated the economic burden of UC in Japan.
Methods: This study used anonymized claims data in the Medical Data Vision database. Patients were included if they had a confirmed UC diagnosis and ≥ 1 claim of systemic treatment for UC (index date) between June 2018 and December 2022, in addition to continuous enrollment for ≥ 6 months before and ≥ 12 months after the index date.
Indian J Ophthalmol
February 2025
Department of Orbit, Oculoplasty, Reconstructive and Aesthetic Services, Sankara Nethralaya, Medical Research Foundation, Chennai, Tamil Nadu, India.
Purpose: To present the clinical features and management outcomes in a series of patients with orbital and adnexal sarcoidosis.
Methods: This was a retrospective analysis of 19 histopathologically proven cases of orbital and adnexal sarcoidosis over the past ten years. The data analyzed included demographic details, clinical and imaging features, and management outcomes.
Tomography
January 2025
Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Objectives: Accurate kidney and tumor segmentation of computed tomography (CT) scans is vital for diagnosis and treatment, but manual methods are time-consuming and inconsistent, highlighting the value of AI automation. This study develops a fully automated AI model using vision transformers (ViTs) and convolutional neural networks (CNNs) to detect and segment kidneys and kidney tumors in Contrast-Enhanced (CECT) scans, with a focus on improving sensitivity for small, indistinct tumors.
Methods: The segmentation framework employs a ViT-based model for the kidney organ, followed by a 3D UNet model with enhanced connections and attention mechanisms for tumor detection and segmentation.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!