Level set based particle filter driven by optical flow: an application to track the salt boundary from X-ray CT time-series

• URL: http://arxiv.org/abs/2202.08717v1
• Date: Thu, 17 Feb 2022 15:46:26 GMT
• Title: Level set based particle filter driven by optical flow: an application to track the salt boundary from X-ray CT time-series
• Authors: Karim Makki and Jean Fran\c{c}ois Lecomte and Lukas Fuchs and Sylvie Schueller and Etienne M\'emin
• Abstract summary: This research aims to determine the non-linear deformation of the salt boundary over time using a parallelized, filtering approach from x-ray computed tomography (CT) image time series. This work represents a first step towards bringing together physical modeling and advanced image processing methods where model uncertainty is taken into account.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Image-based computational fluid dynamics have long played an important role in leveraging knowledge and understanding of several physical phenomena. In particular, probabilistic computational methods have opened the way to modelling the complex dynamics of systems in purely random turbulent motion. In the field of structural geology, a better understanding of the deformation and stress state both within the salt and the surrounding rocks is of great interest to characterize all kinds of subsurface long-terms energy-storage systems. The objective of this research is to determine the non-linear deformation of the salt boundary over time using a parallelized, stochastic filtering approach from x-ray computed tomography (CT) image time series depicting the evolution of salt structures triggered by gravity and under differential loading. This work represents a first step towards bringing together physical modeling and advanced stochastic image processing methods where model uncertainty is taken into account.

Related papers

• Learning to Approximate Particle Smoothing Trajectories via Diffusion Generative Models [16.196738720721417]
  Learning systems from sparse observations is critical in numerous fields, including biology, finance, and physics. We introduce a method that integrates conditional particle filtering with ancestral sampling and diffusion models. We demonstrate the approach in time-series generation and tasks, including vehicle tracking and single-cell RNA sequencing data.
  arXiv  Detail & Related papers  (2024-06-01T21:54:01Z)
• Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution [49.45309818782329]
  Flow image super-resolution (FISR) aims at recovering high-resolution turbulent velocity fields from low-resolution flow images. Existing FISR methods mainly process the flow images in natural image patterns. We propose the first flow visual property-informed FISR algorithm.
  arXiv  Detail & Related papers  (2024-01-29T06:48:16Z)
• Single-shot Tomography of Discrete Dynamic Objects [1.1407697960152927]
  We present a novel method for the reconstruction of high-resolution temporal images in dynamic tomographic imaging. The implications of this research extend to improved visualization and analysis of dynamic processes in tomographic imaging.
  arXiv  Detail & Related papers  (2023-11-09T10:52:02Z)
• Prediction of Diblock Copolymer Morphology via Machine Learning [0.0]
  A machine learning approach is presented to accelerate the computation of block polymer morphology evolution over long timescales. In contrast to empirical annihilation models, the proposed approach learns on the continuumally driven defect processes directly from particle-based simulations. This work has implications for directed self-assembly and materials design in micro-electronics, battery materials, and membranes.
  arXiv  Detail & Related papers  (2023-08-31T17:45:34Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines [116.33694183176617]
  We use a neural network based on the Recurrent Inference Machine to reconstruct an undistorted image of the background source and the lens mass density distribution as pixelated maps. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions.
  arXiv  Detail & Related papers  (2023-01-10T19:00:12Z)
• NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields [65.07940731309856]
  Deep learning has shown great potential for modeling the physical dynamics of complex particle systems such as fluids. In this paper, we consider a partially observable scenario known as fluid dynamics grounding. We propose a differentiable two-stage network named NeuroFluid. It is shown to reasonably estimate the underlying physics of fluids with different initial shapes, viscosity, and densities.
  arXiv  Detail & Related papers  (2022-03-03T15:13:29Z)
• Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data [137.47124933818066]
  We propose an automated pipeline for the analysis of X-ray diffraction images based on the Faster R-CNN deep learning architecture. We demonstrate our method on real-time tracking of organic-inorganic perovskite structure crystallization and test it on two applications.
  arXiv  Detail & Related papers  (2022-02-22T15:39:00Z)
• Dynamic multi feature-class Gaussian process models [0.0]
  This study presents a statistical modelling method for automatic learning of shape, pose and intensity features in medical images. A DMFC-GPM is a Gaussian process (GP)-based model with a shared latent space that encodes linear and non-linear variation. The model performance results suggest that this new modelling paradigm is robust, accurate, accessible, and has potential applications.
  arXiv  Detail & Related papers  (2021-12-08T15:12:47Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Spatiotemporal tomography based on scattered multiangular signals and its application for resolving evolving clouds using moving platforms [0.0]
  We derive computed tomography (CT) of a time-varying translucent volumetric object, using a small number of moving cameras. We demonstrate the approach on dynamic clouds, as clouds have a major effect on Earth's climate.
  arXiv  Detail & Related papers  (2020-12-06T09:22:08Z)

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.