2026-03-04 カリフォルニア工科大学（Caltech）
Beginning with a video of surface wave motion, the new technique, called visual surface wave elastography, estimates the thickness and stiffness of the underlying soft tissue. Here, the method determines the stiffness and three different thicknesses of three highlighted regions on the leg.Credit: Caltech
＜関連情報＞
- https://www.caltech.edu/about/news/using-tiny-ripples-at-skin-level-to-monitor-for-possible-health-problems-below
- https://openaccess.thecvf.com/content/ICCV2025/html/Ogren_Visual_Surface_Wave_Elastography_Revealing_Subsurface_Physical_Properties_via_Visible_ICCV_2025_paper.html
- https://ieeexplore.ieee.org/document/9880380
可視表面波エラストグラフィー：可視表面波による地下物理特性の解明 Visual Surface Wave Elastography: Revealing Subsurface Physical Properties via Visible Surface Waves
Alexander C. Ogren, Berthy T. Feng, Jihoon Ahn, Katherine L. Bouman, Chiara Daraio
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025
Abstract
Wave propagation on the surface of a material contains information about physical properties beneath its surface. We propose a method for inferring the thickness and stiffness of a structure from just a video of waves on its surface. Our method works by extracting a dispersion relation from the video and then solving a physics-based optimization problem to find the best-fitting thickness and stiffness parameters. We validate our method on both simulated and real data, in both cases showing strong agreement with ground-truth measurements. Our technique provides a proof-of-concept for at-home health monitoring of medically-informative tissue properties, and it is further applicable to fields such as human-computer interaction.
視覚振動トモグラフィー：単眼ビデオから室内材料特性を推定する Visual Vibration Tomography: Estimating Interior Material Properties from Monocular Video
Berthy T. Feng; Alexander C. Ogren; Chiara Daraio; Katherine L. Bouman
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Date Added to IEEE Xplore: 27 September 2022
DOI:https://doi.org/10.1109/CVPR52688.2022.01575
Abstract
An object’s interior material properties, while invisible to the human eye, determine motion observed on its surface. We propose an approach that estimates heterogeneous material properties of an object from a monocular video of its surface vibrations. Specifically, we show how to estimate Young’s modulus and density throughout a 3D object with known geometry. Knowledge of how these values change across the object is useful for simulating its motion and characterizing any defects. Traditional nondestructive testing approaches, which often require expensive instruments, generally estimate only homogenized material properties or simply identify the presence of defects. In contrast, our approach leverages monocular video to (1) identify image-space modes from an object’s sub-pixel motion, and (2) directly infer spatially-varying Young’s modulus and density values from the observed modes. We demonstrate our approach on both simulated and real videos.
