Orientation tracking method for anisotropic particles
- URL: http://arxiv.org/abs/2503.08694v1
- Date: Tue, 04 Mar 2025 14:56:29 GMT
- Title: Orientation tracking method for anisotropic particles
- Authors: Mees M. Flapper, Elian Bernard, Sander G. Huisman,
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A method for particle orientation tracking is developed and demonstrated specifically for anisotropic particles. Using (high-speed) multi-camera recordings of anisotropic particles from different viewpoints, we reconstruct the 3D location and orientation of these particles using their known shape. This paper describes an algorithm which tracks the location and orientation of multiple anisotropic particles over time, enabling detailed investigations of location, orientation, and rotation statistics. 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 by applying the algorithm to synthetic images. We showcase several use-cases of this method in several experiments (in both quiescent and turbulent fluids), demonstrating the effectiveness and broad applicability of the described tracking method. 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.
Related papers
- Multiple-Particle Autofocusing Algorithm Using Axial Resolution and Morphological Analyses Based on Digital Holography [3.441301007103367]
We propose an autofocusing algorithm to obtain, relatively accurately, the 3D position of each particle.
Based on the mean intensity and equivalent diameter of each candidate focused particle, all focused particles are eventually secured.
arXiv Detail & Related papers (2025-03-23T11:53:14Z) - Diffusing Differentiable Representations [60.72992910766525]
We introduce a novel, training-free method for sampling differentiable representations (diffreps) using pretrained diffusion models.<n>We identify an implicit constraint on the samples induced by the diffrep and demonstrate that addressing this constraint significantly improves the consistency and detail of the generated objects.
arXiv Detail & Related papers (2024-12-09T20:42:58Z) - Optimal displacement detection of arbitrarily-shaped levitated dielectric objects using optical radiation [7.584203078337655]
We numerically implement a method based on Fisher information that is applicable to suspended particles of arbitrary geometry.
We demonstrate the agreement between our method and prior methods employed for spherical particles, both in the Rayleigh and Lorentz-Mie regimes.
arXiv Detail & Related papers (2024-09-01T17:14:52Z) - Toward Efficient Visual Gyroscopes: Spherical Moments, Harmonics Filtering, and Masking Techniques for Spherical Camera Applications [83.8743080143778]
A visual gyroscope estimates camera rotation through images.
The integration of omnidirectional cameras, offering a larger field of view compared to traditional RGB cameras, has proven to yield more accurate and robust results.
Here, we address these challenges by introducing a novel visual gyroscope, which combines an Efficient Multi-Mask-Filter Rotation Estor and a Learning based optimization.
arXiv Detail & Related papers (2024-04-02T13:19:06Z) - Curved Diffusion: A Generative Model With Optical Geometry Control [56.24220665691974]
The influence of different optical systems on the final scene appearance is frequently overlooked.
This study introduces a framework that intimately integrates a textto-image diffusion model with the particular lens used in image rendering.
arXiv Detail & Related papers (2023-11-29T13:06:48Z) - Deep-learning-based decomposition of overlapping-sparse images: application at the vertex of neutrino interactions [2.5521723486759407]
This paper presents a solution that leverages the power of deep learning to accurately extract individual objects within multi-dimensional overlapping-sparse images.
It is a direct application in high-energy physics with decomposition of overlaid elementary particles obtained from imaging detectors.
arXiv Detail & Related papers (2023-10-30T16:12:25Z) - Evaluation of particle motions in stabilized specimens of transparent
sand using deep learning segmentation [1.8047694351309205]
Individual particle rotation and displacement were measured in triaxial tests on transparent sand stabilized with geogrid simulants.
The Cellpose U-Net model, originally developed to segment biological cells, was trained to segment images of fused quartz particles.
arXiv Detail & Related papers (2022-12-06T12:53:22Z) - 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) - Incorporating Texture Information into Dimensionality Reduction for
High-Dimensional Images [65.74185962364211]
We present a method for incorporating neighborhood information into distance-based dimensionality reduction methods.
Based on a classification of different methods for comparing image patches, we explore a number of different approaches.
arXiv Detail & Related papers (2022-02-18T13:17:43Z) - Visualizing spinon Fermi surfaces with time-dependent spectroscopy [62.997667081978825]
We propose applying time-dependent photo-emission spectroscopy, an established tool in solid state systems, in cold atom quantum simulators.
We show in exact diagonalization simulations of the one-dimensional $t-J$ model that the spinons start to populate previously unoccupied states in an effective band structure.
The dependence of the spectral function on the time after the pump pulse reveals collective interactions among spinons.
arXiv Detail & Related papers (2021-05-27T18:00:02Z) - Event-based Motion Segmentation with Spatio-Temporal Graph Cuts [51.17064599766138]
We have developed a method to identify independently objects acquired with an event-based camera.
The method performs on par or better than the state of the art without having to predetermine the number of expected moving objects.
arXiv Detail & Related papers (2020-12-16T04:06:02Z) - Dense Pixel-wise Micro-motion Estimation of Object Surface by using Low
Dimensional Embedding of Laser Speckle Pattern [4.713575447740915]
This paper proposes a method of estimating micro-motion of an object at each pixel that is too small to detect under a common setup of camera and illumination.
The approach is based on speckle pattern, which is produced by the mutual interference of laser light on object's surface and continuously changes its appearance according to the out-of-plane motion of the surface.
To compensate such micro- and large motion, the method estimates the motion parameters up to scale at each pixel by nonlinear embedding of the speckle pattern into low-dimensional space.
arXiv Detail & Related papers (2020-10-31T03:03:00Z)
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.