Automated Extraction of Mode Shapes Using Motion Magnified Video and Blind Source Separation

Dorn, Charles; Mancini, Tyler; Talken, Zachary; Yang, Yongchao; Kenyon, Garrett T.; Farrar, Charles R.; Mascareñas, David

doi:10.1007/978-3-319-30249-2_32

Cited by 12 publications

(4 citation statements)

References 9 publications

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“…Video motion magnification techniques have opened up a wealth of important applications. Examples include detecting a heartbeat vibration from tiny head motions [ 1 ] or blood flow [ 2 ], magnifying muscle tremors [ 3 , 4 ] to give an accurate clinical judgement, reconstructing speech information from small visual variations [ 5 ], evaluating material properties by the way it moves [ 6 ], estimating damage information of a building structure by measuring small vibrations in video [ 7 , 8 , 9 ], and lip reading [ 10 ]. However, some essential properties of dynamic objects become evident only when they move.…”

Section: Introductionmentioning

confidence: 99%

Bayesian-Inference Embedded Spline-Kerneled Chirplet Transform for Spectrum-Aware Motion Magnification

Cai

Lin

et al. 2022

Sensors

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The ability to discern subtle image changes over time is useful in applications such as product quality control, civil engineering structure evaluation, medical video analysis, music entertainment, and so on. However, tiny yet useful variations are often combined with large motions, which severely distorts current video amplification methods bounded by external constraints. This paper presents a novel use of spectra to make motion magnification robust to large movements. By exploiting spectra, artificial limitations and the magnification of small motions are avoided at similar frequency levels while ignoring large ones at distinct spectral pixels. To achieve this, this paper constructs spline-kerneled chirplet transform (SCT) into an empirical Bayesian paradigm that applies to the entire time series, giving powerful spectral resolution and robust performance to noise in nonstationary nonlinear signal analysis. The important advance reported is Bayesian-rule embedded SCT (BE-SCT); two numerical experiments show its superiority over current approaches. For applying to spectrum-aware motion magnification, an elaborate analytical framework is established that captures global motion, and use of the proposed BE-SCT for dynamic filtering enables a frequency-based motion isolation. Our approach is demonstrated on real-world and synthetic videos. This approach shows superior qualitative and quantitative results with less visual artifacts and more local details over the state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Bayesian-Inference Embedded Spline-Kerneled Chirplet Transform for Spectrum-Aware Motion Magnification

Cai

Lin

et al. 2022

Sensors

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“…Using image processing algorithms such as digital image correlation, video camera imaging measurements have been successfully used for vibration measurements of civil structures and more recently for full‐field experimental modal analysis where full‐field modal parameters (mode shapes) can be obtained. Lately, phase‐based video motion processing technique has been explored to perform output‐only modal identification without the need of installing markers or speckle paints on a structure's surface . The latest development of this technique enables extraction and visualization of full‐field, high spatial resolution modal parameters in a relatively efficient and automated manner .…”

Section: Introductionmentioning

confidence: 99%

Estimation of full‐field, full‐order experimental modal model of cable vibration from digital video measurements with physics‐guided unsupervised machine learning and computer vision

Yang

Sánchez

Zhang

et al. 2019

Struct Control Health Monit

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Cables are critical components for a variety of structures such as stay cables and suspenders of cable-stayed bridges and suspension bridges. When in operational service, they are vulnerable to cumulative fatigue damage induced by dynamic loads (e.g., the cyclic vehicle loads and wind excitation). To accurately analyze and predict their dynamics behaviors and performance that could be spatially local and temporal transient, it is essential to perform highresolution vibration measurements, from which their dynamics properties are identified and, subsequently, a high spatial resolution, full-modal-order dynamics model of cable vibration can be established. This study develops a physics-guided, unsupervised machine learning-based video processing approach that can blindly and efficiently extract the fullfield (as many points as the pixel number of the video frame) modal parameters of cable vibration using only the video of an operating (output-only) cable. In particular, by incorporating the physics of cable vibration (taut string model), a novel automated modal motion filtering method is proposed to enable autonomous identification of full-order (as many modes as possible) dynamic parameters, including those weakly excited modes that used to be challenging to identify in operational modal analysis. Therefore, a full-field, full-order modal model of cable vibration is established by the proposed method. Furthermore, this new approach provides a low-cost and noncontact technique to estimate the cable tension using only the video of the vibrating cable where the fundamental frequency is automatically and efficiently estimated to compute the cable tension according to the taut string equation. Laboratory experiments on a bench-scale cable are conducted to validate the developed approach.

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“…Recently, a phase-based video motion processing technique (originally proposed by Fleet and Jepson (1990) and further developed by Gautama and Van Hulle (2002) and Wadhwa et al (2013) has been explored to perform full-field structural dynamics response measurements and output-only modal identification without the need for installing markers or speckle paints on a structure’s surface (Cha et al, 2017; Chen et al, 2015a, 2015b; Dasari et al, 2017; Dorn et al, 2016, 2017; Poozesh et al, 2017; Roeder et al, 2017; Sanchez et al, 2017; Sarrafi et al, 2017; Yang et al, 2017a, 2017b, 2017c, 2017d, 2017e). Furthermore, the phase-based video motion processing is found computationally efficient to extract from the video the local motion that corresponds to the structural vibration (Yang et al, 2017a; Yang et al, 2017c).…”

Section: Introductionmentioning

confidence: 99%

A framework for the identification of full-field structural dynamics using sequences of images in the presence of non-ideal operating conditions

Dasari

Dorn

Yang

et al. 2018

Journal of Intelligent Material Systems and Structures

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Recent developments in the ability to automatically and efficiently extract natural frequencies, damping ratios, and full-field mode shapes from video of vibrating structures has great potential for reducing the resources and time required for performing experimental and operational modal analysis at very high spatial resolution. Furthermore, these techniques have the added advantage that they can be implemented remotely and in a non-contact fashion. Emerging full-field imaging techniques therefore have potential to allow the identification of the modal properties of structures in regimes that used to be challenging. For instance, these techniques suggest that the high spatial resolution structural identification could be performed on an aircraft during flight using a ground or aircraft-based imager. They also have the potential to identify the dynamics of microscopic systems. In order to realize this capability it will be necessary to develop techniques that can extract full-field structural dynamics in the presence of non-ideal operating conditions. In this work, we develop a framework for the deployment of emerging algorithms that allow the automatic extraction of high-resolution, full-field modal parameters in the presence of non-ideal operating conditions. One of the most notable non-ideal operating conditions is the rigid body motion of both the structure being measured as well as the imager performing the measurement. We demonstrate an instantiation of the framework by showing how it can be used to address, in-plane, translational, rigid body motion. The development of a frame-to-frame keypoint–based technique for identifying full-field structural dynamics in the presence of either rigid body motion is presented and demonstrated in the context of the framework for the deployment of full-field structural identification techniques in the presence of non-ideal operating conditions. It is expected that this framework will ultimately help enable the collection of full-field structural dynamics using measurement platforms including unmanned aerial vehicles, robotic telescopes, satellites, imagers mounted in high-vibration environments (seismic, industrial, harsh weather), characterization of microscopic structures, and human-carried imagers. If imager-based structural identification techniques mature to the point that they can be used in non-ideal field conditions, it could open up the possibility that the structural health monitoring community will be able to think beyond monitoring individual structures, to full-field structural integrity monitoring at the city scale.

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Automated Extraction of Mode Shapes Using Motion Magnified Video and Blind Source Separation

Cited by 12 publications

References 9 publications

Bayesian-Inference Embedded Spline-Kerneled Chirplet Transform for Spectrum-Aware Motion Magnification

Bayesian-Inference Embedded Spline-Kerneled Chirplet Transform for Spectrum-Aware Motion Magnification

Estimation of full‐field, full‐order experimental modal model of cable vibration from digital video measurements with physics‐guided unsupervised machine learning and computer vision

A framework for the identification of full-field structural dynamics using sequences of images in the presence of non-ideal operating conditions

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