Learning-based Axial Video Motion Magnification
Kwon Byung-Ki; Oh Hyun-Bin; Kim Jun-Seong; Hyunwoo Ha; Tae-Hyun Oh · 2023 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
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 retain complex characteristics even after being magnified, which may di
Excerpt shown for reference under fair use — read the full paper at the publisher.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Aggregation Kinetics of Graphene Oxides in Aqueous Solutions: Experiments, Mechanisms, and Modeling
Negative / Null Result ReportThe reaction between metakaolin and limestone and its effect in porosity refinement and mechanical properties
Negative / Null Result ReportTuning Alginate Bioink Stiffness and Composition for Controlled Growth Factor Delivery and to Spatially Direct MSC Fate within Bioprinted Tissues
Negative / Null Result ReportEx-situ characterisation of gas diffusion layers for proton exchange membrane fuel cells
Negative / Null Result ReportCobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network
Negative / Null Result ReportDeterminants of residential water consumption: Evidence and analysis from a 10‐country household survey
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: arXiv