Motion Magnification
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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.
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AMMSM: Adaptive Motion Magnification and Sparse Mamba for Micro-Expression Recognition
Image Processing for Motion Magnification
Non-Invasive Qualitative Vibration Analysis using Event Camera
Three-Stream Temporal-Shift Attention Network Based on Self-Knowledge Distillation for Micro-Expression Recognition
SpikeMM: Flexi-Magnification of High-Speed Micro-Motions
Frequency Decoupling for Motion Magnification via Multi-Level Isomorphic Architecture
Revisiting Learning-based Video Motion Magnification for Real-time Processing
Event-Based Motion Magnification
Learning-based Axial Video Motion Magnification
EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within Transformer
Self-Supervised Motion Magnification by Backpropagating Through Optical Flow
3D Motion Magnification: Visualizing Subtle Motions with Time Varying Radiance Fields
Motion Magnification in Robotic Sonography: Enabling Pulsation-Aware Artery Segmentation
Deformation Monitoring of Tunnel using Phase-based Motion Magnification and Optical Flow
STB-VMM: Swin Transformer Based Video Motion Magnification
How Do Deepfakes Move? Motion Magnification for Deepfake Source Detection
Eulerian Phase-based Motion Magnification for High-Fidelity Vital Sign Estimation with Radar in Clinical Settings
Motion Magnification Algorithms for Video-Based Breathing Monitoring
Lagrangian Motion Magnification with Double Sparse Optical Flow Decomposition
FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation
Surgical Video Motion Magnification with Suppression of Instrument Artefacts
Little Motion, Big Results: Using Motion Magnification to Reveal Subtle Tremors in Infants
A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework
Enhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecture
Alzheimer's Disease Diagnosis Based on Cognitive Methods in Virtual Environments and Emotions Analysis
Using phase instead of optical flow for action recognition
DeepMag: Source Specific Motion Magnification Using Gradient Ascent
Higher Order of Motion Magnification for Vessel Localisation in Surgical Video
Learning-based Video Motion Magnification
Vibration-Based Damage Detection in Wind Turbine Blades using Phase-Based Motion Estimation and Motion Magnification
Multi-point Vibration Measurement for Mode Identification of Bridge Structures using Video-based Motion Magnification
Video Acceleration Magnification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.