Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combin...

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Detalles Bibliográficos
Autores principales: Rai, Rebika, Das, Arunita, Dhal, Krishna Gopal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8859498/
https://www.ncbi.nlm.nih.gov/pubmed/37520044
http://dx.doi.org/10.1007/s12530-022-09425-5
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author Rai, Rebika
Das, Arunita
Dhal, Krishna Gopal
author_facet Rai, Rebika
Das, Arunita
Dhal, Krishna Gopal
author_sort Rai, Rebika
collection PubMed
description Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019–2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.
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spelling pubmed-88594982022-02-22 Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review Rai, Rebika Das, Arunita Dhal, Krishna Gopal Evolving Systems Review Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019–2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA. Springer Berlin Heidelberg 2022-02-21 2022 /pmc/articles/PMC8859498/ /pubmed/37520044 http://dx.doi.org/10.1007/s12530-022-09425-5 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Review
Rai, Rebika
Das, Arunita
Dhal, Krishna Gopal
Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review
title Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review
title_full Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review
title_fullStr Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review
title_full_unstemmed Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review
title_short Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review
title_sort nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8859498/
https://www.ncbi.nlm.nih.gov/pubmed/37520044
http://dx.doi.org/10.1007/s12530-022-09425-5
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AT dhalkrishnagopal natureinspiredoptimizationalgorithmsandtheirsignificanceinmultithresholdingimagesegmentationaninclusivereview

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