Amplifying Imperceptible Variations in Video Sequences for Time-Varying Process Analysis
Bibliographic record
Abstract
In the dynamic world we inhabit, countless time-varying processes occur, many of which can be recorded using conventional digital cameras.Often, these processes harbor subtle color and motion variations that remain imperceptible to the human eye in the resultant footage.These minute variations, however, may encapsulate crucial information pertinent to the process under observation.Amplifying these variations can unveil valuable insights, thereby facilitating process monitoring and analysis across a multitude of applications.Existing methodologies predominantly focus on amplifying the entire scene or frame of the video, disregarding its intended application.Such an approach demands significant computational time and resources.This paper introduces an innovative and efficient video processing technique, the Modified Eulerian video magnification (MEVM).The MEVM technique identifies and amplifies variations within the region of interest (ROI) in the input video, tailoring it to the specific application.This targeted approach notably reduces computation time and resources -by over 45% compared to conventional methods.Moreover, the amplified variations can be utilized to ascertain the vibration frequency of mechanical systems, such as car engine vibrations, with an accuracy exceeding 98%.The MEVM technique's potential applications span numerous fields, including healthcare, mechanical engineering, civil structures, security, and military.This novel technique offers a significant advancement in video processing, paving the way for more efficient and targeted analysis of time-varying processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".