Evidence map›Paper›PMID 34960444›Full record

ArticleSensors (Basel, Switzerland)2021

Operational Deflection Shapes Magnification and Visualization Using Optical-Flow-Based Image Processing.

Adam Machynia, Ziemowit Dworakowski, Kajetan Dziedziech, Paweł Zdziebko, Jarosław Konieczny, Krzysztof Holak

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Adam MachyniaDepartment of Robotics and Mechatronics, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland.ORCID 0000-0003-1638-3678
Ziemowit DworakowskiDepartment of Robotics and Mechatronics, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland.ORCID 0000-0002-5289-9826
Kajetan DziedziechDepartment of Robotics and Mechatronics, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland.ORCID 0000-0001-5703-2642
Paweł ZdziebkoDepartment of Robotics and Mechatronics, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland.ORCID 0000-0001-6845-033X
Jarosław KoniecznyDepartment of Process Control, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland.ORCID 0000-0002-3513-0601
Krzysztof HolakDepartment of Robotics and Mechatronics, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland.ORCID 0000-0002-7107-8126

Funding

National Centre for Research and Development LIDER/26/0103/L- 331 9/17/NCBR/2018
6 · The paper itself

Abstract

Much information can be derived from operational deflection shapes of vibrating structures and the magnification of their motion. However, the acquisition of deflection shapes usually requires a manual definition of an object's points of interest, while general motion magnification is computationally inefficient. We propose easy extraction of operational deflection shapes straight from vision data by analyzing and processing optical flow information from the video and then, based on these graphs, morphing source data to magnify the shape of deflection. We introduce several processing routines for automatic masking of the optical flow data and frame-wise information fusion. The method is tested based on data acquired both in numerical simulations and real-life experiments in which cantilever beams were subjected to excitation around their natural frequencies.

Indexed as

cantilever beamimage segmentationmodal analysismotion magnificationoptical flow

Identifiers

PMID34960444
PMCPMC8705351

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.