Evidence map›Paper›PMID 35808552›Full record

ArticleSensors (Basel, Switzerland)2022

Innovative Image Processing Method to Improve Autofocusing Accuracy.

Chien-Sheng Liu, Ho-Da Tu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Chien-Sheng LiuDepartment of Mechanical Engineering, National Cheng Kung University, Tainan 70101, Taiwan.ORCID 0000-0002-3537-5828
Ho-Da TuDepartment of Mechanical Engineering, National Cheng Kung University, Tainan 70101, Taiwan.ORCID 0000-0001-9302-7728

Funding

Ministry of Science and Technology of Taiwan MOST 110-2221-E-006-126-MY3 and 106-2628-E-006-010-MY3
6 · The paper itself

Abstract

For automated optical inspection, autofocusing microscopes play an important role in capturing clear images of the measured object. At present, the image processing part of optics-based autofocusing microscopes often has various factors, which makes it impossible to describe the image information of the semicircular (or elliptical) spot with a simple circle-finding method. Accordingly, this study has developed a novel algorithm that can quickly calculate the ideal center of the elliptical spot and effectively compensate the linearity of the focusing characteristic curve. A prototype model was used to characterize and verify the proposed algorithm. The experimental results show that by using the proposed algorithm, the autofocusing accuracy can be effectively improved to less than 1.5 μm.

Indexed as

AlgorithmsImage Processing, Computer-AssistedMicroscopyaccuracyalgorithmautofocusinggeometric fluctuationsimage processing

Identifiers

PMID35808552
PMCPMC9269835

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.