Ensuring color fidelity in image-based 3D modeling of heritage scenarios is nowadays still an open research matter. Image colors are important during the data processing as they affect algorithm outcomes, therefore their correct treatment, reduction and enhancement is fundamental. In this contribution, we present an automated solution developed to improve the radiometric quality of an image datasets and the performances of two main steps of the photogrammetric pipeline (camera orientation and dense image matching). The suggested solution aims to achieve a robust automatic color balance and exposure equalization, stability of the RGB-to-gray image conversion and faithful color appearance of a digitized artifact. The innovative aspects of the article are: complete automation, better color target detection, a MATLAB implementation of the ACR scripts created by Fraser and the use of a specific weighted polynomial regression. A series of tests are presented to demonstrate the efficiency of the developed methodology and to evaluate color accuracy ('color characterization').
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http://dx.doi.org/10.3390/s17112437 | DOI Listing |
PeerJ
January 2025
Departamento de Biodiversidad y Biología Evolutiva, Museo Nacional de Ciencias Naturales (MNCN-CSIC), Madrid, Spain.
The flat-headed frog, , is a poorly known, riverine species, endemic to the province of Palawan in the Philippines. We applied capture-mark-recapture (CMR) methods to follow individuals at two sites (Malbato and San Rafael) in the island of Busuanga over 10 months in 2022-2023. We used passive internal transponders (PITs) to mark adult and subadults and single-colored visual internal elastomers (VIEs) for cohorts of juveniles.
View Article and Find Full Text PDFComput Biol Med
January 2025
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150080, China. Electronic address:
With the advent of the deep learning-based colonoscopy system, the need for a vast amount of high-quality colonoscopy image datasets for training is crucial. However, the generalization ability of deep learning models is challenged by the limited availability of colonoscopy images due to regulatory restrictions and privacy concerns. In this paper, we propose a method for rendering high-fidelity 3D colon models and synthesizing diversified colonoscopy images with abnormalities such as polyps, bleeding, and ulcers, which can be used to train deep learning models.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Department of Epidemiology and Biostatistics, School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
Background: Public health programs and policies can positively influence food environments. In 2016, a voluntary National Healthy Food and Drink Policy was released in New Zealand to improve the healthiness of food and drinks for hospital staff and visitors. However, no resources were developed to support policy implementation.
View Article and Find Full Text PDFJ Spinal Cord Med
January 2025
Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, USA.
Context: Clinical Practice Guidelines from the Consortium for Spinal Cord Injury (SCI) Medicine recommend daily self-screening of at-risk skin surfaces, but many Veterans with SCI describe challenges using the standard issue long-handled self-inspection mirror (LSIM).
Objective: The objective of this project was to compare the LSIM to a recently developed camera-based self-inspection system (CSIS). User feedback guided iterative engineering to improve and develop the new technology in preparation for transfer to industry.
Biol Imaging
December 2024
Visual Information Laboratory, University of Bristol, Bristol, UK.
Optical coherence tomography (OCT) and confocal microscopy are pivotal in retinal imaging, offering distinct advantages and limitations. OCT offers rapid, noninvasive imaging but can suffer from clarity issues and motion artifacts, while confocal microscopy, providing high-resolution, cellular-detailed color images, is invasive and raises ethical concerns. To bridge the benefits of both modalities, we propose a novel framework based on unsupervised 3D CycleGAN for translating unpaired OCT to confocal microscopy images.
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