Image Shadow Detection and Removal Based on Region Matching of Intelligent Computing.


Journal

Computational intelligence and neuroscience
ISSN: 1687-5273
Titre abrégé: Comput Intell Neurosci
Pays: United States
ID NLM: 101279357

Informations de publication

Date de publication:
2022
Historique:
received: 08 03 2022
revised: 17 03 2022
accepted: 02 04 2022
entrez: 2 5 2022
pubmed: 3 5 2022
medline: 4 5 2022
Statut: epublish

Résumé

Shadow detection and removal play an important role in the field of computer vision and pattern recognition. Shadow will cause some loss and interference to the information of moving objects, resulting in the performance degradation of subsequent computer vision tasks such as moving object detection or image segmentation. In this paper, each image is regarded as a small sample, and then a method based on material matching of intelligent computing between image regions is proposed to detect and remove image shadows. In shadow detection, the proposed method can be directly used for detection without training and ensures the consistency of similar regions to a certain extent. In shadow removal, the proposed method can minimize the influence of shadow removal operation on other features in the shadow region. The experiments on the benchmark dataset demonstrate that the proposed approach achieves a promising performance, and its improvement is more than 6% in comparison with several advanced shadow detection methods.

Identifiants

pubmed: 35498207
doi: 10.1155/2022/7261551
pmc: PMC9045973
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

7261551

Informations de copyright

Copyright © 2022 Junying Feng et al.

Déclaration de conflit d'intérêts

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Références

IEEE Trans Pattern Anal Mach Intell. 2011 Jun;33(6):1202-16
pubmed: 20733214
IEEE Trans Image Process. 2010 Oct;19(10):2749-60
pubmed: 20483686
IEEE Trans Pattern Anal Mach Intell. 2016 Mar;38(3):431-46
pubmed: 27046489
IEEE Trans Pattern Anal Mach Intell. 2018 Mar;40(3):682-695
pubmed: 28410096
IEEE Trans Pattern Anal Mach Intell. 2013 Dec;35(12):2956-67
pubmed: 24136433
IEEE Trans Pattern Anal Mach Intell. 2004 May;26(5):530-49
pubmed: 15460277

Auteurs

Junying Feng (J)

School of Intelligent Manufacturing, Weifang University of Science and Technology, Shandong, Weifang 261000, China.
Department of Information and Communication Engineering, Hoseo University, Chungcheongnam-do, Asan, 31499, Republic of Korea.

Yong Kwan Kim (YK)

Department of Information and Communication Engineering, Hoseo University, Chungcheongnam-do, Asan, 31499, Republic of Korea.

Peng Liu (P)

School of Intelligent Manufacturing, Weifang University of Science and Technology, Shandong, Weifang 261000, China.

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Classifications MeSH