Nature and Biologically Inspired Image Segmentation Techniques; Archives of Computational Methods in Engineering; Vol. XX, iss. X
| Parent link: | Archives of Computational Methods in Engineering Vol. XX, iss. X.— 2021.— [28 p.] |
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| Institution som forfatter: | |
| Andre forfattere: | , , , , , |
| Summary: | Title screen Image processing is among the signifcant areas of growth in the current scenario. It consist of a set of techniques typically used to enhance the raw image obtained from diferent scenes. Segmentation of images is an essential step in image analysis and pre-processing. During the course of the work, standard multilevel thresholding methods are very efective due to their low computational cost, reliability, reduced convergence time, and precision. Nature-inspired methods of optimization play an essential role in the processing of images. Several optimization procedures have been proposed for diferent image processing applications. These optimization techniques can improve the performance of image segmentation, image restoration, edge detection, image enhancement, pattern recognition, image generation, image thresholding, and image fusion algorithms. This paper includes an overview of several metaheuristic frefy algorithm (FA), diferential evolution (DE), particle swarm optimization (PSO), genetic algorithm (GA), artifcial bee colony optimization (ABC), etc. Moreover, artifcial neural networks (ANN) and other machine learning techniques (nature or biological inspired) are discussed in context with image segmentation application and their algorithms. |
| Sprog: | engelsk |
| Udgivet: |
2021
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| Fag: | |
| Online adgang: | https://doi.org/10.1007/s11831-021-09619-1 |
| Format: | MixedMaterials Electronisk Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=666504 |
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| 200 | 1 | |a Nature and Biologically Inspired Image Segmentation Techniques |f S. Singh, N. Mittal, D. Thakur [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 144 tit.] | ||
| 330 | |a Image processing is among the signifcant areas of growth in the current scenario. It consist of a set of techniques typically used to enhance the raw image obtained from diferent scenes. Segmentation of images is an essential step in image analysis and pre-processing. During the course of the work, standard multilevel thresholding methods are very efective due to their low computational cost, reliability, reduced convergence time, and precision. Nature-inspired methods of optimization play an essential role in the processing of images. Several optimization procedures have been proposed for diferent image processing applications. These optimization techniques can improve the performance of image segmentation, image restoration, edge detection, image enhancement, pattern recognition, image generation, image thresholding, and image fusion algorithms. This paper includes an overview of several metaheuristic frefy algorithm (FA), diferential evolution (DE), particle swarm optimization (PSO), genetic algorithm (GA), artifcial bee colony optimization (ABC), etc. Moreover, artifcial neural networks (ANN) and other machine learning techniques (nature or biological inspired) are discussed in context with image segmentation application and their algorithms. | ||
| 461 | |t Archives of Computational Methods in Engineering | ||
| 463 | |t Vol. XX, iss. X |v [28 p.] |d 2021 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a биологические методы | |
| 610 | 1 | |a сегментация | |
| 610 | 1 | |a изображения | |
| 610 | 1 | |a обработка изображений | |
| 610 | 1 | |a искусственные нейронные сети | |
| 610 | 1 | |a машинное обучение | |
| 701 | 1 | |a Singh |b S. |g Simrandeep | |
| 701 | 1 | |a Mittal |b N. |g Nitin | |
| 701 | 1 | |a Thakur |b D. |g Diksha | |
| 701 | 1 | |a Singh |b H. |g Harbinder | |
| 701 | 1 | |a Oliva Navarro |b D. A. |c specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University |f 1983- |g Diego Alberto |3 (RuTPU)RU\TPU\pers\37366 | |
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| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |b Отделение информационных технологий |3 (RuTPU)RU\TPU\col\23515 |
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