Imaging three-dimensional (3D) objects has been realized by methods such as binocular stereo vision and multi-view imaging. These methods, however, needs multiple cameras or multiple shots to get elemental images. In this paper, we develop a single-shot multi-view imaging technique by utilizing the natural randomness of scattering media. By exploiting the memory effect and uncorrelated point spread functions (PSF) among scattering media, we demonstrate that both stereo imaging with large disparity and up to seven-view imaging of a 3D object can be reconstructed from only one speckle pattern by deconvolution. The elemental images are consistent with 3D object projection and images taken by multi-shot imaging. Our technique provides a feasible method to capture multi-view imaging with short acquisition time and easy calibration.
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http://dx.doi.org/10.1364/OE.27.037164 | DOI Listing |
Endocrinol Metab (Seoul)
January 2025
Division of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules.
Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer.
Brief Bioinform
November 2024
Department of Biology, University at Albany, SUNY, 1400 Washington Ave, Albany, NY 12222, United States.
The accuracy of assigning fluorophore identity and abundance, known as spectral unmixing, in biological fluorescence microscopy images remains a significant challenge due to the substantial overlap in emission spectra among fluorophores. In traditional laser scanning confocal spectral microscopy, fluorophore information is acquired by recording emission spectra with a single combination of discrete excitation wavelengths. However, organic fluorophores possess characteristic excitation spectra in addition to their unique emission spectral signatures.
View Article and Find Full Text PDFSensors (Basel)
December 2024
College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, China.
This paper addresses the challenges of low accuracy and long transfer learning time in small-sample bearing fault diagnosis, which are often caused by limited samples, high noise levels, and poor feature extraction. We propose a method that combines an improved capsule network with a Siamese neural network. Multi-view data partitioning is used to enrich data diversity, and Markov transformation converts one-dimensional vibration signals into two-dimensional images, enhancing the visualization of signal features.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Electrical Engineering, Center for Innovative Research on Aging Society (CIRAS), Advanced Institute of Manufacturing with High-Tech Innovations (AIM-HI), National Chung Cheng University, Chia-Yi 621, Taiwan.
In computer vision, accurately estimating a 3D human skeleton from a single RGB image remains a challenging task. Inspired by the advantages of multi-view approaches, we propose a method of predicting enhanced 2D skeletons (specifically, predicting the joints' relative depths) from multiple virtual viewpoints based on a single real-view image. By fusing these virtual-viewpoint skeletons, we can then estimate the final 3D human skeleton more accurately.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China. Electronic address:
Background And Objective: Neurosurgical navigation is a critical element of brain surgery, and accurate segmentation of brain and scalp blood vessels is crucial for surgical planning and treatment. However, conventional methods for segmenting blood vessels based on statistical or thresholding techniques have limitations. In recent years, deep learning-based methods have emerged as a promising solution for blood vessel segmentation, but the segmentation of small blood vessels and scalp blood vessels remains challenging.
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