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Event cameras, inspired by biological vision, offer high dynamic range, excellent temporal resolution, and minimal data redundancy. Precise calibration of event camera systems is essential for applications such as 3D vision. The cessation of extra gray frame production in popular models like the dynamic vision sensor (DVS) poses significant challenges to achieving high-accuracy calibration. Traditional calibration methods, which rely on motion to trigger events, are prone to movement-related errors. This paper introduces a motion-error-free calibration method for event cameras using a flashing target produced by a standard electronic display that elicits high-fidelity events. We propose an improved events-accumulator to reconstruct gray images with distinct calibration features and develop an optimization method that adjusts camera parameters and control point positions simultaneously, enhancing the calibration accuracy of event camera systems. Experimental results demonstrated higher accuracy compared to the traditional motion-based calibration method (reprojection error: 0.03 vs. 0.96 pixels). The 3D reconstruction error remained around 0.15 mm, significantly improving over the motion-based method's 8.00 mm. Additionally, the method's adaptability for hybrid calibration in event-based stereovision systems was verified (e.g., with frame cameras or projectors).
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http://dx.doi.org/10.1364/OE.529263 | DOI Listing |
PLOS Digit Health
September 2025
Laerdal Medical AS, Stavanger, Norway.
Accurate observations at birth and during newborn resuscitation are fundamental for quality improvement initiatives and research. However, manual data collection methods often lack consistency and objectivity, are not scalable, and may raise privacy concerns. The NewbornTime project aims to develop an AI system that generates accurate timelines from birth and newborn resuscitation events by automated video recording and processing, providing a source of objective and consistent data.
View Article and Find Full Text PDFEvent-based sensors (EBS), with their low latency and high dynamic range, are a promising means for tracking unresolved point-objects. Conventional EBS centroiding methods assume the generated events follow a Gaussian distribution and require long event streams ($\gt 1$s) for accurate localization. However, these assumptions are inadequate for centroiding unresolved objects, since the EBS circuitry causes non-Gaussian event distributions, and because using long event streams negates the low-latency advantage of EBS.
View Article and Find Full Text PDFNeurology
September 2025
Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
Background And Objectives: Multiple sclerosis (MS) is common in adults while myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is rare. Our previous machine-learning algorithm, using clinical variables, ≤6 brain lesions, and no Dawson fingers, achieved 79% accuracy, 78% sensitivity, and 80% specificity in distinguishing MOGAD from MS but lacked validation. The aim of this study was to (1) evaluate the clinical/MRI algorithm for distinguishing MS from MOGAD, (2) develop a deep learning (DL) model, (3) assess the benefit of combining both, and (4) identify key differentiators using probability attention maps (PAMs).
View Article and Find Full Text PDFMed Phys
August 2025
QuantIF AIMS, University of Rouen, Rouen, France.
Background: Patient-specific dosimetry in radiopharmaceutical therapy (RPT) offers a promising approach to optimize the balance between treatment efficacy and toxicity. The introduction of 360° CZT gamma cameras enables the development of personalized dosimetry studies using whole-body single photon emission computed tomography and computed tomography (SPECT/CT) data.
Purpose: This study proposes to validate the collapsed-cone superposition (CCS) approach against Monte Carlo (MC) simulations for whole-body dosimetry of [177Lu]Lu-PSMA-617 therapy in patients with metastatic castration resistant prostate cancer (mCRPC).
IEEE Trans Radiat Plasma Med Sci
May 2025
Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA.
An array of photomultiplier tubes (PMTs) provides energy readout for gamma cameras, leading to event selection and positioning. However, operational and environmental changes, such as temperature, can cause PMTs to "drift" away from their nominal energy readouts and, therefore, require a correction procedure to return to their reference energies. We present two methods for determining the energy-scale change of each PMT using data collected on C-SPECT, a dedicated cardiac single photon emission computational tomography (SPECT) scanner.
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