Evidence map›Paper›PMID 38877063›Full record

ArticleScientific reports2024

Enhancing computer image recognition with improved image algorithms.

Lanqing Huang, Cheng Yao, Lingyan Zhang, Shijian Luo, Fangtian Ying, Weiqiang Ying

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Lanqing HuangZhejiang University, Hangzhou, 310027, China.
Cheng Yao *Zhejiang University, Hangzhou, 310027, China.
Lingyan ZhangZhejiang University, Hangzhou, 310027, China.
Shijian Luo *Zhejiang University, Hangzhou, 310027, China.
Fangtian Ying *Macau University of Science and Technology, Macau, 519020, China.
Weiqiang YingZhejiang University, Hangzhou, 310027, China. 11921165@zju.edu.cn.

Funding

Fundamental Research Funds for the Central Universities 226-2023-00086National Social Science Foundation of China 21AZD056Research Center of Computer Aided Product Innovation Design, Ministry of Education, National Natural Science Foundation of China 52075478
6 · The paper itself

Abstract

Advances in computer image recognition have significantly impacted many industries, including healthcare, security and autonomous systems. This paper aims to explore the potential of improving image algorithms to enhance computer image recognition. Specifically, we will focus on regression methods as a means to improve the accuracy and efficiency of identifying images. In this study, we will analyze various regression techniques and their applications in computer image recognition, as well as the resulting performance improvements through detailed examples and data analysis. This paper deals with the problems related to visual image processing in outdoor unstructured environment. Finally, the heterogeneous patterns are converted into the same pattern, and the heterogeneous patterns are extracted from the fusion features of data modes. The simulation results show that the perception ability and recognition ability of outdoor image recognition in complex environment are improved.

Indexed as

Feature extractionImage recognitionImproving imageOptimizing the methodsVisual data

Identifiers

PMID38877063
PMCPMC11178774

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.