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Learning-based Axial Video Motion Magnification
March 27, 2024, 4:47 a.m. | Kwon Byung-Ki, Oh Hyun-Bin, Kim Jun-Seong, Hyunwoo Ha, Tae-Hyun Oh
cs.CV updates on arXiv.org arxiv.org
Abstract: Video motion magnification amplifies invisible small motions to be perceptible, which provides humans with a spatially dense and holistic understanding of small motions in the scene of interest. This is based on the premise that magnifying small motions enhances the legibility of motions. In the real world, however, vibrating objects often possess convoluted systems that have complex natural frequencies, modes, and directions. Existing motion magnification often fails to improve legibility since the intricate motions still …
abstract arxiv cs.cv eess.iv however humans objects small type understanding video world
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