Evidence map›Paper›PMID 35494803›Full record

ArticlePeerJ. Computer science2022

Deep convolutional neural networks for regular texture recognition.

Ni Liu, Mitchell Rogers, Hua Cui, Weiyu Liu, Xizhi Li, Patrice Delmas

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 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. Review
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.

Ni LiuSchool of Information Engineering, Chang'an University, Xi'an, ShaanXi Province, China.
Mitchell RogersDepartment of Computer Science, The University of Auckland, Auckland, New Zealand.
Hua CuiSchool of Information Engineering, Chang'an University, Xi'an, ShaanXi Province, China.
Weiyu LiuSchool of Electronics and Control Engineering, Chang'an University, Xi'an, China.
Xizhi LiHenan Highway Development Co. Ltd. Anxin Branch, Xinxiang City, Henan Province, China.
Patrice DelmasDepartment of Computer Science, The University of Auckland, Auckland, New Zealand.ORCID 0000-0002-0235-4596

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Regular textures are frequently found in man-made environments and some biological and physical images. There are a wide range of applications for recognizing and locating regular textures. In this work, we used deep convolutional neural networks (CNNs) as a general method for modelling and classifying regular and irregular textures. We created a new regular texture database and investigated two sets of deep CNNs-based methods for regular and irregular texture classification. First, the classic CNN models (

Indexed as

Convolutional neural networks (CNNs)Long-range featuresRegular textureRepetitive patternsTexture recognition

Identifiers

PMID35494803
PMCPMC9044313

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.