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Motion Magnification

Motion magnification is a technique that acts like a microscope for visual motion. It can amplify subtle motions in a video sequence, allowing for visualization of deformations that would otherwise be invisible. To achieve motion magnification, we need to accurately measure visual motions, and group the pixels to be modified.

There are different approaches to motion magnification, such as Lagrangian and Eulerian methods. Lagrangian methods track the trajectories of moving objects and exaggerate them, while Eulerian methods manipulate the motions at fixed positions. Eulerian methods can be further divided into linear and phase-based methods. Linear methods apply a temporal bandpass filter to boost the linear term of a Taylor series expansion of the displacement function, while phase-based methods use complex wavelet transforms to manipulate the phase of the signal.

Motion magnification has various applications, such as measuring the human pulse, visualizing the heat plume of candles, revealing the oscillations of a wine glass, and detecting structural defects.

Papers

Showing 1120 of 38 papers

TitleStatusHype
Multi Domain Learning for Motion MagnificationCode0
Using phase instead of optical flow for action recognitionCode0
Autonomous Apex Detection and Micro-Expression Recognition using Proposed Diagonal Planes0
AMMSM: Adaptive Motion Magnification and Sparse Mamba for Micro-Expression Recognition0
Higher Order of Motion Magnification for Vessel Localisation in Surgical Video0
How Do Deepfakes Move? Motion Magnification for Deepfake Source Detection0
Image Processing for Motion Magnification0
Unsupervised Magnification of Posture Deviations Across Subjects0
Learning-based Axial Video Motion Magnification0
Alzheimer's Disease Diagnosis Based on Cognitive Methods in Virtual Environments and Emotions Analysis0
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