A Fast Specular Highlight Removal Method for Smooth Liquor Bottle Surface Combined with U

Shaojie Guo, Xiaogang Wang, Jiayi Zhou, Zewei Lian

Open access · goldAbstract 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. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

0.6field-weighted citation impact, top 32% of its field

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, 6 citations in OpenAlex.

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

4 authors at 1 institution in 1 country.

Shaojie GuoSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, No. 188 University Road, Cuiping District, Yibin 644000, China.ORCID 0000-0003-4554-3845

Xiaogang WangSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, No. 188 University Road, Cuiping District, Yibin 644000, China.

Jiayi ZhouSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, No. 188 University Road, Cuiping District, Yibin 644000, China.

Zewei LianSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, No. 188 University Road, Cuiping District, Yibin 644000, China.

Yibin University · CN

Funding

Graduate innovation fund of Sichuan University of Science and Engineering y2021072National Natural Science Foundation of China 61902268Sichuan Science and Technology Program 19ZDZX0037Sichuan Science and Technology Program 2019YFSY0045Sichuan Science and Technology Program 2020YFH0124Sichuan Science and Technology Program 2021YFSY0060Sichuan Science and Technology Program 20ZDYF0919Sichuan Science and Technology Program 21ZDYF4052

6 · The paper itself

Abstract

Highlight removal is a critical and challenging problem. In view of the complex highlight phenomenon on the surface of smooth liquor bottles in natural scenes, the traditional highlight removal algorithms cannot semantically disambiguate between all-white or near-white materials and highlights, and the recent highlight removal algorithms based on deep learning lack flexibility in network architecture, have network training difficulties and have insufficient object applicability. As a result, they cannot accurately locate and remove highlights in the face of some small sample highlight datasets with strong pertinence, which reduces the performance of some tasks. Therefore, this paper proposes a fast highlight removal method combining U

Indexed as

AlgorithmsImage Processing, Computer-AssistedLaMasmall sample highlight datasetsmooth liquor bottle surfacespecular highlight detectionspecular highlight removalU2-Net

Identifiers

PMID36560201

DOI10.3390/s22249834

PMCPMC9782029

OpenAlexW4311793851

What OpenQuestion holds

Textmetadata

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.