Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation
Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machi...
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| Vydané v: | Scientific reports Ročník 14; číslo 1; s. 7642 - 19 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Nature Publishing Group UK
01.04.2024
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2045-2322, 2045-2322 |
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| Abstract | Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics. |
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| AbstractList | Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics. Abstract Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics. Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics.Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics. |
| ArticleNumber | 7642 |
| Author | Liu, Mengzhu Nie, Fangyan Zhang, Pingfeng |
| Author_xml | – sequence: 1 givenname: Fangyan surname: Nie fullname: Nie, Fangyan email: niefyan@vip.163.com organization: Computer and Information Engineering College, Guizhou University of Commerce – sequence: 2 givenname: Mengzhu surname: Liu fullname: Liu, Mengzhu organization: Computer and Information Engineering College, Guizhou University of Commerce – sequence: 3 givenname: Pingfeng surname: Zhang fullname: Zhang, Pingfeng organization: College of Marxism, Guizhou University of Commerce |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38561478$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_autcon_2025_106423 crossref_primary_10_3390_e27050544 crossref_primary_10_1016_j_ijdrr_2025_105358 crossref_primary_10_20965_jaciii_2025_p1077 crossref_primary_10_1038_s41598_024_81075_w crossref_primary_10_1093_jcde_qwae081 crossref_primary_10_3390_rs16193603 crossref_primary_10_3390_biomimetics10040218 crossref_primary_10_1063_5_0244948 crossref_primary_10_1016_j_compbiomed_2024_109272 crossref_primary_10_1016_j_heliyon_2024_e40698 |
| Cites_doi | 10.1007/s10489-022-04064-4 10.1016/j.knosys.2023.110587 10.1016/j.engappai.2023.107624 10.1016/j.bspc.2022.104373 10.1109/4235.985692 10.1016/j.neucom.2019.01.036 10.1016/j.compag.2022.107488 10.1007/s00500-023-09283-6 10.1016/j.eswa.2021.115286 10.1016/j.compbiomed.2022.105542 10.1016/j.patrec.2014.11.009 10.1016/j.amc.2006.06.057 10.1016/j.eswa.2021.114636 10.1016/j.autcon.2023.104939 10.1016/j.autcon.2023.104929 10.1016/j.asoc.2021.107905 10.1016/j.conbuildmat.2021.126162 10.1016/j.compbiomed.2023.106950 10.1109/TIP.2011.2109730 10.1016/j.engappai.2022.104960 10.1109/ACCESS.2019.2891632 10.1007/s12530-022-09443-3 10.1016/j.asoc.2023.110130 10.1016/j.asoc.2020.106588 10.1016/j.eswa.2023.122316 10.1016/0031-3203(93)90115-D 10.1016/j.ins.2020.05.033 10.1016/j.eswa.2021.115003 10.1016/j.eswa.2021.116145 10.1007/s11760-021-02123-w 10.48550/arXiv.math/0505204 10.1016/j.autcon.2023.105014 10.1038/s41598-023-36066-8 10.1109/TIP.2003.819861 10.1109/ICNN.1995.488968 10.1016/j.matpr.2022.11.356 |
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| Keywords | Crack detection Multilevel image thresholding Minimum arithmetic-geometric divergenc Local stochastic perturbation Particle swarm optimization |
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| References | Abdel-Basset, Mohamed, AbdelAziz, Abouhawwash (CR19) 2022; 190 Ma, Yue (CR21) 2022; 113 Weng, Huang, Li, Yang, Yu (CR1) 2023; 153 Chakraborty, Mali (CR10) 2023 Sarkar, Das, Chaudhuri (CR28) 2015; 54 Wu, Zhou, Ji, Yin, Shen (CR23) 2020; 533 Taneja (CR26) 2005; 5 Ahilan (CR33) 2019; 7 Qiao, Liu, Xue, Tang, Salehnia (CR31) 2024; 241 Yang (CR12) 2023; 80 Kumar, Kumar, Vishwakarma, Singh (CR14) 2022; 203 Mousavirad, Schaefer, Zhou, Moghadam (CR20) 2023; 272 Ding, Yang, Yu, Shu (CR3) 2023; 152 Chen, Seo, Jun, Zhao (CR7) 2022; 16 Clerc, Kennedy (CR30) 2002; 6 Song (CR2) 2024; 129 Yin (CR15) 2007; 184 Houssein, Mohamed, Ibrahim, Wazery (CR32) 2023; 13 Li, Lee (CR27) 1993; 26 Liu, Yao, Lu, Xie, Li (CR34) 2019; 338 Wang, Bovik, Sheikh, Simoncelli (CR35) 2004; 13 Zhang, Zhang, Mou, Zhang (CR36) 2011; 20 Lei, Fan (CR25) 2020; 96 CR5 He (CR4) 2023; 154 CR29 Shi (CR11) 2023; 160 Anitha, Immanuel Alex Pandian, Akila Agnes (CR18) 2021; 178 Wang, Bei, Song, Zhang, Zhang (CR24) 2023; 137 Sowjanya, Injeti (CR16) 2021; 182 Abualigah, Almotairi, Elaziz (CR8) 2022; 53 Sathya, Kalyani, Sakthivel (CR17) 2021; 172 Kheradmandi, Mehranfar (CR6) 2022; 321 Eisham (CR9) 2023; 14 Zhang, Xie, Sun, Zhang (CR22) 2022; 146 Xing, He (CR13) 2021; 113 M Shi (58456_CR11) 2023; 160 G Ma (58456_CR21) 2022; 113 C Li (58456_CR27) 1993; 26 A Kumar (58456_CR14) 2022; 203 M Abdel-Basset (58456_CR19) 2022; 190 K Sowjanya (58456_CR16) 2021; 182 58456_CR29 P-Y Yin (58456_CR15) 2007; 184 B Lei (58456_CR25) 2020; 96 W Ding (58456_CR3) 2023; 152 B Wu (58456_CR23) 2020; 533 X Yang (58456_CR12) 2023; 80 Q Song (58456_CR2) 2024; 129 N Kheradmandi (58456_CR6) 2022; 321 P Sathya (58456_CR17) 2021; 172 ZK Eisham (58456_CR9) 2023; 14 X He (58456_CR4) 2023; 154 Z Xing (58456_CR13) 2021; 113 IJ Taneja (58456_CR26) 2005; 5 Y Zhang (58456_CR22) 2022; 146 X Weng (58456_CR1) 2023; 153 L Abualigah (58456_CR8) 2022; 53 S Chakraborty (58456_CR10) 2023 C Chen (58456_CR7) 2022; 16 Z Wang (58456_CR35) 2004; 13 J Anitha (58456_CR18) 2021; 178 L Zhang (58456_CR36) 2011; 20 M Clerc (58456_CR30) 2002; 6 L Qiao (58456_CR31) 2024; 241 A Ahilan (58456_CR33) 2019; 7 SJ Mousavirad (58456_CR20) 2023; 272 Y Liu (58456_CR34) 2019; 338 EH Houssein (58456_CR32) 2023; 13 J Wang (58456_CR24) 2023; 137 58456_CR5 S Sarkar (58456_CR28) 2015; 54 |
| References_xml | – volume: 53 start-page: 11654 year: 2022 end-page: 11704 ident: CR8 article-title: Multilevel thresholding image segmentation using meta-heuristic optimization algorithms: Comparative analysis, open challenges and new trends publication-title: Appl. Intell. doi: 10.1007/s10489-022-04064-4 – volume: 272 year: 2023 ident: CR20 article-title: How effective are current population-based metaheuristic algorithms for variance-based multi-level image thresholding? publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2023.110587 – volume: 129 year: 2024 ident: CR2 article-title: A three-stage pavement image crack detection framework with positive sample augmentation publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2023.107624 – volume: 80 year: 2023 ident: CR12 article-title: Multi-level threshold segmentation framework for breast cancer images using enhanced differential evolution publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2022.104373 – volume: 6 start-page: 58 year: 2002 end-page: 73 ident: CR30 article-title: The particle swarm - explosion, stability, and convergence in a multidimensional complex space publication-title: IEEE Trans. Evolut. Comput. doi: 10.1109/4235.985692 – volume: 338 start-page: 139 year: 2019 end-page: 153 ident: CR34 article-title: Deepcrack: A deep hierarchical feature learning architecture for crack segmentation publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.01.036 – volume: 203 year: 2022 ident: CR14 article-title: Multilevel thresholding for crop image segmentation based on recursive minimum cross entropy using a swarm-based technique publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2022.107488 – year: 2023 ident: CR10 article-title: A multilevel biomedical image thresholding approach using the chaotic modified cuckoo search publication-title: Soft Comput. doi: 10.1007/s00500-023-09283-6 – volume: 182 year: 2021 ident: CR16 article-title: Investigation of butterfly optimization and gases Brownian motion optimization algorithms for optimal multilevel image thresholding publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.115286 – volume: 146 year: 2022 ident: CR22 article-title: An efficient multi-level encryption scheme for stereoscopic medical images based on coupled chaotic system and otsu threshold segmentation publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2022.105542 – volume: 54 start-page: 27 year: 2015 end-page: 35 ident: CR28 article-title: A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2014.11.009 – ident: CR29 – volume: 184 start-page: 503 year: 2007 end-page: 513 ident: CR15 article-title: Multilevel minimum cross entropy threshold selection based on particle swarm optimization publication-title: Appl. Math. Comput. doi: 10.1016/j.amc.2006.06.057 – volume: 172 year: 2021 ident: CR17 article-title: Color image segmentation using Kapur, Otsu and minimum cross entropy functions based on exchange market algorithm publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.114636 – volume: 153 year: 2023 ident: CR1 article-title: Unsupervised domain adaptation for crack detection publication-title: Autom. Constr. doi: 10.1016/j.autcon.2023.104939 – volume: 152 year: 2023 ident: CR3 article-title: Crack detection and quantification for concrete structures using UAV and transformer publication-title: Autom. Constr. doi: 10.1016/j.autcon.2023.104929 – volume: 113 year: 2021 ident: CR13 article-title: Many-objective multilevel thresholding image segmentation for infrared images of power equipment with boost marine predators algorithm publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2021.107905 – volume: 321 year: 2022 ident: CR6 article-title: A critical review and comparative study on image segmentation-based techniques for pavement crack detection publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2021.126162 – volume: 160 year: 2023 ident: CR11 article-title: A grade-based search adaptive random slime mould optimizer for lupus nephritis image segmentation publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2023.106950 – volume: 20 start-page: 2378 year: 2011 end-page: 2386 ident: CR36 article-title: FSIM: A feature similarity index for image quality assessment publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2011.2109730 – volume: 113 year: 2022 ident: CR21 article-title: An improved whale optimization algorithm based on multilevel threshold image segmentation using the Otsu method publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2022.104960 – volume: 7 start-page: 89570 year: 2019 end-page: 89580 ident: CR33 article-title: Segmentation by fractional order Darwinian particle swarm optimization based multilevel thresholding and improved lossless prediction based compression algorithm for medical images publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2891632 – volume: 14 start-page: 605 year: 2023 end-page: 648 ident: CR9 article-title: Chimp optimization algorithm in multilevel image thresholding and image clustering publication-title: Evolv. Syst. doi: 10.1007/s12530-022-09443-3 – volume: 137 year: 2023 ident: CR24 article-title: A whale optimization algorithm with combined mutation and removing similarity for global optimization and multilevel thresholding image segmentation publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2023.110130 – volume: 96 year: 2020 ident: CR25 article-title: Multilevel minimum cross entropy thresholding: A comparative study publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.106588 – volume: 241 year: 2024 ident: CR31 article-title: A multi-level thresholding image segmentation method using hybrid arithmetic optimization and Harris Hawks optimizer algorithms publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2023.122316 – volume: 26 start-page: 617 year: 1993 end-page: 625 ident: CR27 article-title: Minimum cross entropy thresholding publication-title: Pattern Recognit. doi: 10.1016/0031-3203(93)90115-D – volume: 533 start-page: 72 year: 2020 end-page: 107 ident: CR23 article-title: An ameliorated teaching-learning-based optimization algorithm based study of image segmentation for multilevel thresholding using kapur’s entropy and otsu’s between class variance publication-title: Inf. Sci. doi: 10.1016/j.ins.2020.05.033 – ident: CR5 – volume: 178 year: 2021 ident: CR18 article-title: An efficient multilevel color image thresholding based on modified whale optimization algorithm publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.115003 – volume: 190 year: 2022 ident: CR19 article-title: Hwoa: A hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.116145 – volume: 16 start-page: 1673 year: 2022 end-page: 1681 ident: CR7 article-title: A potential crack region method to detect crack using image processing of multiple thresholding publication-title: Signal Image Video Process. doi: 10.1007/s11760-021-02123-w – volume: 5 start-page: 145 year: 2005 end-page: 168 ident: CR26 article-title: Relative divergence measures and information inequalities publication-title: Inequal. Theory Appl. doi: 10.48550/arXiv.math/0505204 – volume: 154 year: 2023 ident: CR4 article-title: UAV-based road crack object-detection algorithm publication-title: Autom. Constr. doi: 10.1016/j.autcon.2023.105014 – volume: 13 start-page: 9094 year: 2023 ident: CR32 article-title: An efficient multilevel image thresholding method based on improved heap-based optimizer publication-title: Sci. Rep. doi: 10.1038/s41598-023-36066-8 – volume: 13 start-page: 600 year: 2004 end-page: 612 ident: CR35 article-title: Image quality assessment: From error visibility to structural similarity publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2003.819861 – volume: 533 start-page: 72 year: 2020 ident: 58456_CR23 publication-title: Inf. Sci. doi: 10.1016/j.ins.2020.05.033 – volume: 203 year: 2022 ident: 58456_CR14 publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2022.107488 – ident: 58456_CR29 doi: 10.1109/ICNN.1995.488968 – volume: 20 start-page: 2378 year: 2011 ident: 58456_CR36 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2011.2109730 – volume: 153 year: 2023 ident: 58456_CR1 publication-title: Autom. Constr. doi: 10.1016/j.autcon.2023.104939 – volume: 272 year: 2023 ident: 58456_CR20 publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2023.110587 – volume: 16 start-page: 1673 year: 2022 ident: 58456_CR7 publication-title: Signal Image Video Process. doi: 10.1007/s11760-021-02123-w – volume: 190 year: 2022 ident: 58456_CR19 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.116145 – volume: 5 start-page: 145 year: 2005 ident: 58456_CR26 publication-title: Inequal. Theory Appl. doi: 10.48550/arXiv.math/0505204 – year: 2023 ident: 58456_CR10 publication-title: Soft Comput. doi: 10.1007/s00500-023-09283-6 – volume: 96 year: 2020 ident: 58456_CR25 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.106588 – volume: 154 year: 2023 ident: 58456_CR4 publication-title: Autom. Constr. doi: 10.1016/j.autcon.2023.105014 – volume: 113 year: 2021 ident: 58456_CR13 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2021.107905 – ident: 58456_CR5 doi: 10.1016/j.matpr.2022.11.356 – volume: 14 start-page: 605 year: 2023 ident: 58456_CR9 publication-title: Evolv. Syst. doi: 10.1007/s12530-022-09443-3 – volume: 178 year: 2021 ident: 58456_CR18 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.115003 – volume: 241 year: 2024 ident: 58456_CR31 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2023.122316 – volume: 53 start-page: 11654 year: 2022 ident: 58456_CR8 publication-title: Appl. Intell. doi: 10.1007/s10489-022-04064-4 – volume: 13 start-page: 600 year: 2004 ident: 58456_CR35 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2003.819861 – volume: 6 start-page: 58 year: 2002 ident: 58456_CR30 publication-title: IEEE Trans. Evolut. Comput. doi: 10.1109/4235.985692 – volume: 137 year: 2023 ident: 58456_CR24 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2023.110130 – volume: 146 year: 2022 ident: 58456_CR22 publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2022.105542 – volume: 80 year: 2023 ident: 58456_CR12 publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2022.104373 – volume: 321 year: 2022 ident: 58456_CR6 publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2021.126162 – volume: 13 start-page: 9094 year: 2023 ident: 58456_CR32 publication-title: Sci. Rep. doi: 10.1038/s41598-023-36066-8 – volume: 160 year: 2023 ident: 58456_CR11 publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2023.106950 – volume: 113 year: 2022 ident: 58456_CR21 publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2022.104960 – volume: 26 start-page: 617 year: 1993 ident: 58456_CR27 publication-title: Pattern Recognit. doi: 10.1016/0031-3203(93)90115-D – volume: 54 start-page: 27 year: 2015 ident: 58456_CR28 publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2014.11.009 – volume: 182 year: 2021 ident: 58456_CR16 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.115286 – volume: 172 year: 2021 ident: 58456_CR17 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.114636 – volume: 184 start-page: 503 year: 2007 ident: 58456_CR15 publication-title: Appl. Math. Comput. doi: 10.1016/j.amc.2006.06.057 – volume: 152 year: 2023 ident: 58456_CR3 publication-title: Autom. Constr. doi: 10.1016/j.autcon.2023.104929 – volume: 129 year: 2024 ident: 58456_CR2 publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2023.107624 – volume: 338 start-page: 139 year: 2019 ident: 58456_CR34 publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.01.036 – volume: 7 start-page: 89570 year: 2019 ident: 58456_CR33 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2891632 |
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| Snippet | Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important... Abstract Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An... |
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| SubjectTerms | 639/166/987 639/705/117 639/705/258 Algorithms Crack detection Divergence Humanities and Social Sciences Image processing Local stochastic perturbation Minimum arithmetic-geometric divergenc multidisciplinary Multilevel image thresholding Optimization algorithms Particle swarm optimization Science Science (multidisciplinary) Visual thresholds |
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| Title | Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation |
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