Computer Vision based Tomography of Structures Using 3D Digital Image Correlation

• URL: http://arxiv.org/abs/2012.10516v1
• Date: Fri, 18 Dec 2020 21:17:00 GMT
• Title: Computer Vision based Tomography of Structures Using 3D Digital Image Correlation
• Abstract summary: Internal properties of a sample can be observed by medical imaging tools. New idea intended at recovering unseen defect distributions within the interior of three-dimensional heterogeneous space.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Internal properties of a sample can be observed by medical imaging tools, such as ultrasound devices, magnetic resonance imaging (MRI) and optical coherence tomography (OCT) which are based on relying on changes in material density or chemical composition [1-21]. As a preliminary investigation, the feasibility to detect interior defects inferred from the discrepancy in elasticity modulus distribution of a three-dimensional heterogeneous sample using only surface full-field measurements and finite element model updating as an inverse optimization algorithm without any assumption about local homogeneities and also the elasticity modulus distribution is investigated. Recently, the authors took advantages of the digital image correlation technique as a full field measurement in constitutive property identification of a full-scale steel component [22-27]. To the extension of previous works, in this brief technical note, the new idea intended at recovering unseen volumetric defect distributions within the interior of three-dimensional heterogeneous space of the structural component using 3D-Digital Image Correlation for structural identification [28-57]. As a proof of concept, the results of this paper illustrate the potential to identify invisible internal defect by the proposed computer vision technique establishes the potential for new opportunities to characterize internal heterogeneous materials for their mechanical property distribution and condition state.

Related papers

• Integrated Laser Scanning and Image-Based Topology Optimization Techniques for Detection and Quantification of Visible and Subsurface Structural Defects [0.0]
  This study presents two complementary non-contact, vision-based approaches for the detection and quantitative characterization of defects in structural components.<n>The first approach employs high-resolution laser scanning to generate 3D point clouds of damaged steel specimens.<n>The second approach combines full-field surface deformation measurements obtained using 3D-DIC with finite element model updating and topology optimization.
  arXiv  Detail & Related papers  (2026-09-01T19:31:32Z)
• Reconstruction-Shift Discrimination via Mask-Guided Latent Diffusion for Medical Anomaly Detection [58.92871907037662]
  Unsupervised medical anomaly detection learns normal anatomical patterns from healthy training images and identifies deviations at test time.<n>Reconstruction-based and diffusion-based methods commonly use the difference between an input image and its reconstruction as anomaly evidence.<n>We propose discriminative mask-guided diffusion (DMD), a medical anomaly detection framework that complements residual-based localization with reconstruction-shift discrimination.
  arXiv  Detail & Related papers  (2026-08-01T04:56:39Z)
• Data-Driven Automated Identification of Optimal Feature-Representative Images in Infrared Thermography Using Statistical and Morphological Metrics [0.0]
  Infrared thermography (IRT) is a widely used non-destructive testing technique for detecting structural features such as subsurface defects.<n>Most IRT post-processing methods generate image sequences in which defect visibility varies strongly across time, frequency, or coefficient/index domains.<n>Conventional evaluation metrics, such as the signal-to-noise ratio (SNR) or the Tanimoto criterion, often require prior knowledge of defect locations or defect-free reference regions.
  arXiv  Detail & Related papers  (2026-04-09T11:24:26Z)
• Accelerating 3D Photoacoustic Computed Tomography with End-to-End Physics-Aware Neural Operators [74.65171736966131]
  Photoacoustic computed tomography (PACT) combines optical contrast with ultrasonic resolution, achieving deep-tissue imaging beyond the optical diffusion limit.<n>Current implementations require dense transducer arrays and prolonged acquisition times, limiting clinical translation.<n>We introduce Pano, an end-to-end physics-aware model that directly learns the inverse acoustic mapping from sensor measurements to volumetric reconstructions.
  arXiv  Detail & Related papers  (2025-09-11T23:12:55Z)
• Direct Dual-Energy CT Material Decomposition using Model-based Denoising Diffusion Model [105.95160543743984]
  We propose a deep learning procedure called Dual-Energy Decomposition Model-based Diffusion (DEcomp-MoD) for quantitative material decomposition.<n>We show that DEcomp-MoD outperform state-of-the-art unsupervised score-based model and supervised deep learning networks.
  arXiv  Detail & Related papers  (2025-07-24T01:00:06Z)
• 3D Wavelet Latent Diffusion Model for Whole-Body MR-to-CT Modality Translation [13.252652406393205]
  Existing MR-to-CT methods for whole-body imaging often suffer from poor spatial alignment between the generated CT and input MR images.<n>We present a novel 3D Wavelet Latent Diffusion Model (3D-WLDM) that addresses these limitations.<n>By incorporating a Wavelet Residual Module into the encoder-decoder architecture, we enhance the capture and reconstruction of fine-scale features across image and latent spaces.
  arXiv  Detail & Related papers  (2025-07-14T06:17:05Z)
• 3D variational autoencoder for fingerprinting microstructure volume elements [0.5892638927736115]
  We present a 3D variational autoencoder (VAE) for encoding microstructure volume elements (VEs)<n>Crystal symmetries in the orientation space are accounted for by mapping to the crystallographic fundamental zone as a preprocessing step.<n>VAE is then used to encode a training set of VEs with an equiaxed polycrystalline microstructure with random texture.<n>We show that the model generalises well to microstructures with textures, grain sizes and aspect ratios outside the training distribution.
  arXiv  Detail & Related papers  (2025-03-21T11:17:10Z)
• Automated Dynamic Image Analysis for Particle Size and Shape Classification in Three Dimensions [3.8300818830608345]
  Current state-of-the art instruments for dynamic image analysis are largely limited to two-dimensional imaging. Existing three-dimensional imaging technologies, such as computed tomography, laser scanning, and orthophotography, are limited to static objects. OCULAR addresses these challenges by providing a cost-effective solution for imaging continuous particle streams using a synchronised array of optical cameras.
  arXiv  Detail & Related papers  (2024-12-06T15:36:59Z)
• Neural Image Unfolding: Flattening Sparse Anatomical Structures using Neural Fields [6.5082099033254135]
  Tomographic imaging reveals internal structures of 3D objects and is crucial for medical diagnoses. Various organ-specific unfolding techniques exist to map their densely sampled 3D surfaces to a distortion-minimized 2D representation. We deploy a neural field to fit the transformation of the anatomy of interest to a 2D overview image.
  arXiv  Detail & Related papers  (2024-11-27T14:58:49Z)
• Spatial super-resolution in nanosensing with blinking emitters [79.16635054977068]
  We propose a method of spatial resolution enhancement in metrology with blinking fluorescent nanosensors. We believe that blinking fluorescent sensing agents being complemented with the developed image analysis technique could be utilized routinely in the life science sector.
  arXiv  Detail & Related papers  (2024-02-27T10:38:05Z)
• A Generative Machine Learning Model for Material Microstructure 3D Reconstruction and Performance Evaluation [4.169915659794567]
  The dimensional extension from 2D to 3D is viewed as a highly challenging inverse problem from the current technological perspective. A novel generative model that integrates the multiscale properties of U-net with and the generative capabilities of GAN has been proposed. The model's accuracy is further improved by combining the image regularization loss with the Wasserstein distance loss.
  arXiv  Detail & Related papers  (2024-02-24T13:42:34Z)
• Denoising diffusion-based synthetic generation of three-dimensional (3D) anisotropic microstructures from two-dimensional (2D) micrographs [0.0]
  We present a framework for reconstruction of anisotropic microstructures based on conditional diffusion-based generative models (DGMs) The proposed framework involves spatial connection of multiple 2D conditional DGMs, each trained to generate 2D microstructure samples for three different planes. The results demonstrate that the framework is capable of reproducing not only the statistical distribution of material phases but also the material properties in 3D space.
  arXiv  Detail & Related papers  (2023-12-13T01:36:37Z)
• Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI [76.60362295758596]
  Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance.
  arXiv  Detail & Related papers  (2023-11-22T05:44:51Z)
• A Survey on Evaluation Metrics for Synthetic Material Micro-Structure Images from Generative Models [0.0]
  The evaluation of synthetic micro-structure images is an emerging problem as machine learning and materials science research have evolved together. In this study we evaluate a variety of methods on scanning electron microscope (SEM) images of graphene-reinforced polyurethane foams.
  arXiv  Detail & Related papers  (2022-11-03T15:17:42Z)
• Amortized Inference for Heterogeneous Reconstruction in Cryo-EM [36.911133113707045]
  cryo-electron microscopy (cryo-EM) provides insights into the dynamics of proteins and other building blocks of life. The algorithmic challenge of jointly estimating the poses, 3D structure, and conformational heterogeneity of a biomolecule remains unsolved. Our method, cryoFIRE, performs ab initio heterogeneous reconstruction with unknown poses in an amortized framework. We show that our method can provide one order of magnitude speedup on datasets containing millions of images without any loss of accuracy.
  arXiv  Detail & Related papers  (2022-10-13T22:06:38Z)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures [68.8204255655161]
  It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches. It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
  arXiv  Detail & Related papers  (2021-10-26T07:02:14Z)
• 3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks [52.710012864395246]
  We propose a fully automated pipeline for both detection and matching of curvilinear structures in stereo pairs. We mainly focus on 3D reconstruction of dislocations from stereo pairs of TEM images.
  arXiv  Detail & Related papers  (2021-10-14T23:05:47Z)
• Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging [22.690971184202944]
  In this work, we present a new approach for handling this specific type of uncertainty by leveraging the concept of conditional invertible neural networks (cINNs) Specifically, we propose going beyond commonly used point estimates for tissue oxygenation and converting single-pixel initial pressure spectra to the full posterior probability density. Based on the presented architecture, we demonstrate two use cases which leverage this information to not only detect and quantify but also to compensate for uncertainties.
  arXiv  Detail & Related papers  (2020-11-10T14:17:18Z)
• Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
  We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
  arXiv  Detail & Related papers  (2020-10-05T14:18:52Z)
• Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting [86.33696045574692]
  We propose a Cycle Consistency Panoptic Domain Adaptive Mask R-CNN (CyC-PDAM) architecture for unsupervised nuclei segmentation in histopathology images. We first propose a nuclei inpainting mechanism to remove the auxiliary generated objects in the synthesized images. Secondly, a semantic branch with a domain discriminator is designed to achieve panoptic-level domain adaptation.
  arXiv  Detail & Related papers  (2020-05-05T11:08:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.