ArticleSensors (Basel, Switzerland)2020
Strain Measurement Based on Speeded-up Robust Feature Algorithm Applied to Microimages from a Smartphone-Based Microscope.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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.
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Who cites it
3 citing papers in PubMed.
- Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review.Sensors (Basel, Switzerland) · 2024Review
- Micro-Image Strain Sensing Method for Displacement and Strain Measurement in One United Sensor.Sensors (Basel, Switzerland) · 2022Article
- A Task-Learning Strategy for Robotic Assembly Tasks from Human Demonstrations.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The objective of this study is to evaluate and improve the accuracy and stability of a strain measurement method that uses the speeded-up robust feature (SURF) method to trace the displacement of feature points in microimages and obtain the strain in objects. The microimages were acquired using a smartphone with a portable microscope, which has a broad prospect of application. An experiment was performed using an unpacked optical fiber as the experimental carrier. The matching effect of the SURF method was analyzed in the microimage, and the M-estimator sample consensus (MSAC) algorithm was used to reject outliers generated by SURF. The results indicated that the accuracy of strain measurement using the proposed method is improved by modifying the feature point tracking method and measurement method. When compared with the fiber Bragg grating (FBG) data, the maximum standard error corresponded to 2.5 με, which satisfies the requirement of structural health monitoring (SHM) in practical engineering.
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Registered trials
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