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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/RLado/STB-VMM
공식 코드https://github.com/acceleration-magnification/sources
공식 코드https://github.com/dianyehuang/robpmepasnn
공식 코드Motion Magnification in Robotic Sonography: Enabling Pulsation-Aware Artery Segmentation
https://github.com/guanghaozhu663/skd-tstsan
공식 코드https://github.com/jiafei127/fd4mm
공식 코드Frequency Decoupling for Motion Magnification via Multi-Level Isomorphic Architecture
https://github.com/phflot/dsd_momag
공식 코드Lagrangian Motion Magnification with Double Sparse Optical Flow Decomposition
https://github.com/tarun005/FLAVR
공식 코드FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation
https://github.com/vut-hfut/eulermormer
공식 코드EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within Transformer
https://github.com/12dmodel/deep_motion_mag
https://github.com/ZhengPeng7/motion_magnification_learning-based
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.