Pattern-Based Phase-Separation of Tracer and Dispersed Phase Particles in Two-Phase Defocusing Particle Tracking Velocimetry

• URL: http://arxiv.org/abs/2506.18157v1
• Date: Sun, 22 Jun 2025 20:10:45 GMT
• Title: Pattern-Based Phase-Separation of Tracer and Dispersed Phase Particles in Two-Phase Defocusing Particle Tracking Velocimetry
• Authors: Christian Sax, Jochen Kriegseis,
• Abstract summary: This work investigates the feasibility of a post-processing-based approach for phase separation in defocusing particle tracking velocimetry for two-phase flows.<n>The method enables the simultaneous 3D localization determination of both tracer particles and particles of the dispersed phase, using a single-camera setup.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This work investigates the feasibility of a post-processing-based approach for phase separation in defocusing particle tracking velocimetry for dispersed two-phase flows. The method enables the simultaneous 3D localization determination of both tracer particles and particles of the dispersed phase, using a single-camera setup. The distinction between phases is based on pattern differences in defocused particle images, which arise from distinct light scattering behaviors of tracer particles and bubbles or droplets. Convolutional neural networks, including Faster R-CNN and YOLOv4 variants, are trained to detect and classify particle images based on these pattern features. To generate large, labeled training datasets, a generative adversarial network based framework is introduced, allowing the generation of auto-labeled data that more closely reflects experiment-specific visual appearance. Evaluation across six datasets, comprising synthetic two-phase and real single- and two-phase flows, demonstrates high detection precision and classification accuracy (95-100%), even under domain shifts. The results confirm the viability of using CNNs for robust phase separation in disperse two-phase DPTV, particularly in scenarios where traditional wavelength-, size-, or ensemble correlation-based methods are impractical.

Related papers

• DiffusionSfM: Predicting Structure and Motion via Ray Origin and Endpoint Diffusion [53.70278210626701]
  We propose a data-driven multi-view reasoning approach that directly infers 3D scene geometry and camera poses from multi-view images.<n>Our framework, DiffusionSfM, parameterizes scene geometry and cameras as pixel-wise ray origins and endpoints in a global frame.<n>We empirically validate DiffusionSfM on both synthetic and real datasets, demonstrating that it outperforms classical and learning-based approaches.
  arXiv  Detail & Related papers  (2025-05-08T17:59:47Z)
• Multi-camera orientation tracking method for anisotropic particles in particle-laden flows [0.0]
  This paper describes an algorithm which tracks the location and orientation of multiple anisotropic particles over time.<n>The robustness and error of this method is quantified, and we explore the effects of noise, image size, the number of used cameras, and the camera arrangement.<n>The proposed method is shown to work for widely different particle shapes, successfully tracks multiple particles simultaneously, and the method can distinguish between different types of particles.
  arXiv  Detail & Related papers  (2025-03-04T14:56:29Z)
• Diffraction of correlated biphotons through transparent samples [0.0]
  We show that a phase object placed in the image plane of the source affects the free space propagation of the SPDC.<n>Our findings offer applications for non-interferometric, quantum-enhanced phase imaging.
  arXiv  Detail & Related papers  (2024-10-30T02:00:02Z)
• PCF-Lift: Panoptic Lifting by Probabilistic Contrastive Fusion [80.79938369319152]
  We design a new pipeline coined PCF-Lift based on our Probabilis-tic Contrastive Fusion (PCF) Our PCF-lift not only significantly outperforms the state-of-the-art methods on widely used benchmarks including the ScanNet dataset and the Messy Room dataset (4.4% improvement of scene-level PQ)
  arXiv  Detail & Related papers  (2024-10-14T16:06:59Z)
• Quantitative phase gradient microscopy with spatially entangled photons [0.0]
  We present an entanglement-based quantitative phase gradient microscopy technique that employs principles from quantum ghost imaging and ghost diffraction.<n>In this method, a transparent sample is illuminated by both photons of an entangled pair - one detected in the near-field (position) and the other in the far-field (momentum)<n>We demonstrate quantitative phase and amplitude imaging with a spatial resolution of 2.76 $mu$m and a phase sensitivity of $lambda/100$ using femtowatts of illuminating power.
  arXiv  Detail & Related papers  (2024-06-10T15:39:32Z)
• Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes [1.534667887016089]
  We leverage training data to discriminatively learn particle-based representations of uncertainty in latent object states. Our approach achieves dramatic improvements in accuracy, while also showing much greater stability across multiple training runs.
  arXiv  Detail & Related papers  (2024-04-12T19:33:52Z)
• DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model [46.68717345017946]
  Hyperspectral image change detection (HSI-CD) has emerged as a crucial research area in remote sensing. We propose a novel unsupervised HSI-CD with semantic correlation diffusion model (DiffUCD) Our method can achieve comparable results to those fully supervised methods requiring numerous samples.
  arXiv  Detail & Related papers  (2023-05-21T09:21:41Z)
• SinDiffusion: Learning a Diffusion Model from a Single Natural Image [159.4285444680301]
  We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image. It is based on two core designs. First, SinDiffusion is trained with a single model at a single scale instead of multiple models with progressive growing of scales. Second, we identify that a patch-level receptive field of the diffusion network is crucial and effective for capturing the image's patch statistics.
  arXiv  Detail & Related papers  (2022-11-22T18:00:03Z)
• 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)
• Observation-dependent suppression and enhancement of two-photon coincidences by tailored losses [68.8204255655161]
  Hong-Ou-Mandel (HOM) effect can lead to a perfect suppression of two-particle coincidences between the output ports of a balanced beam splitter. In this work, we demonstrate experimentally that the two-particle coincidence statistics of two bosons can instead be seamlessly tuned to substantial enhancement. Our findings reveal a new approach to harnessing non-Hermitian settings for the manipulation of multi-particle quantum states.
  arXiv  Detail & Related papers  (2021-05-12T06:47:35Z)

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.