Abstract
Phase-based motion processing and the associated Motion Magnification that it enables has become popular not only for the striking videos that it can produce of traditionally stiff structures visualized with very large deflections, but also for its ability to pull information out of the noise floor of images so that they can be processed with more traditional optical techniques such as digital image correlation or feature tracking. While the majority of papers in the literature have utilized the Phase-based Image Processing approach as a pre-processor for more quantitative analyses, the technique itself can be used directly to extract modal parameters from an image, noting that the extracted phases are proportional to displacements in the image. Once phases are extracted, they can be fit using traditional experimental modal analysis techniques. This produces a mode “shape” where the degrees of freedom are phases instead of physical motions. These phases can be scaled to produce on-image visualizations of the mode shapes, rather than operational shapes produced by bandpass filtering. Modal filtering techniques can also be used to visualize motions from an environment on an image using the modal phases as a basis for the expansion.
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Python Package Index URL: https://pypi.org/project/netCDF4/
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Rohe, D., Reu, P. Experimental Modal Analysis Using Phase Quantities from Phase-Based Motion Processing and Motion Magnification. Exp Tech 45, 297–312 (2021). https://doi.org/10.1007/s40799-020-00392-7
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DOI: https://doi.org/10.1007/s40799-020-00392-7