Boosting Marine Predators Algorithm by Salp Swarm Algorithm for Multilevel Thresholding Image Segmentation; Multimedia Tools and Applications; Vol. 81, iss. 12
| Parent link: | Multimedia Tools and Applications Vol. 81, iss. 12.— 2022.— [P. 16707–16742] |
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| Korporativní autor: | |
| Další autoři: | , , , |
| Shrnutí: | Title screen Pixel rating is considered one of the commonly used critical factors in digital image processing that depends on intensity. It is used to determine the optimal image segmentation threshold. In recent years, the optimum threshold has been selected with great interest due to its many applications. Several methods have been used to find the optimum threshold, including the Otsu and Kapur methods. These methods are appropriate and easy to implement to define a single or bi-level threshold. However, when they are extended to multiple levels, they will cause some problems, such as long time-consuming, the high computational cost, and the needed improvement in their accuracy. To avoid these problems and determine the optimal multilevel image segmentation threshold, we proposed a hybrid Marine Predators Algorithm (MPA) with Salp Swarm Algorithm (SSA) to determine the optimal multilevel threshold image segmentation MPASSA. The obtained solutions of the proposed method are represented using the image histogram. Several standard evaluation measures, such as (the fitness function, time consumer, Peak Signal-to-Noise Ratio, Structural Similarity Index, etc.…) are employed to evaluate the proposed segmentation method’s effectiveness. Several benchmark images are used to validate the proposed algorithm’s performance (MPASSA). The results showed that the proposed MPASSA got better results than other well-known optimization algorithms published in the literature. Режим доступа: по договору с организацией-держателем ресурса |
| Jazyk: | angličtina |
| Vydáno: |
2022
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| Témata: | |
| On-line přístup: | https://doi.org/10.1007/s11042-022-12001-3 |
| Médium: | Elektronický zdroj Kapitola |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=668724 |
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| 200 | 1 | |a Boosting Marine Predators Algorithm by Salp Swarm Algorithm for Multilevel Thresholding Image Segmentation |f A. Laith, Kh. Nada, A. M. Mokhamed Elsaed (Mohamed Abd Elaziz), H. Essam | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 53 tit.] | ||
| 330 | |a Pixel rating is considered one of the commonly used critical factors in digital image processing that depends on intensity. It is used to determine the optimal image segmentation threshold. In recent years, the optimum threshold has been selected with great interest due to its many applications. Several methods have been used to find the optimum threshold, including the Otsu and Kapur methods. These methods are appropriate and easy to implement to define a single or bi-level threshold. However, when they are extended to multiple levels, they will cause some problems, such as long time-consuming, the high computational cost, and the needed improvement in their accuracy. To avoid these problems and determine the optimal multilevel image segmentation threshold, we proposed a hybrid Marine Predators Algorithm (MPA) with Salp Swarm Algorithm (SSA) to determine the optimal multilevel threshold image segmentation MPASSA. The obtained solutions of the proposed method are represented using the image histogram. Several standard evaluation measures, such as (the fitness function, time consumer, Peak Signal-to-Noise Ratio, Structural Similarity Index, etc.…) are employed to evaluate the proposed segmentation method’s effectiveness. Several benchmark images are used to validate the proposed algorithm’s performance (MPASSA). The results showed that the proposed MPASSA got better results than other well-known optimization algorithms published in the literature. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t Multimedia Tools and Applications | ||
| 463 | |t Vol. 81, iss. 12 |v [P. 16707–16742] |d 2022 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a image segmentation | |
| 610 | 1 | |a multilevel thresholding | |
| 610 | 1 | |a meta-heuristic algorithms | |
| 610 | 1 | |a marine predator algorithm | |
| 610 | 1 | |a salp swarm algorith | |
| 610 | 1 | |a сегментация | |
| 610 | 1 | |a изображения | |
| 610 | 1 | |a многоуровневость | |
| 610 | 1 | |a пороговые значения | |
| 610 | 1 | |a метаэвристические алгоритмы | |
| 701 | 1 | |a Laith |b A. |g Abualigah | |
| 701 | 1 | |a Nada |b Kh. |g Khalil Al-Okbi | |
| 701 | 1 | |a Mokhamed Elsaed (Mohamed Abd Elaziz) |b A. M. |c Specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University |f 1987- |g Akhmed Mokhamed |3 (RuTPU)RU\TPU\pers\46943 | |
| 701 | 1 | |a Essam |b H. |g Houssein | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |c 2017- |x TPU |7 ca |8 rus |9 28330 |
| 801 | 0 | |a RU |b 63413507 |c 20230119 |g RCR | |
| 856 | 4 | |u https://doi.org/10.1007/s11042-022-12001-3 | |
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