Neural network technologies for image classification; Proceedings of SPIE; Vol. 9680 : Atmospheric and Ocean Optics: Atmospheric Physics
| Parent link: | Proceedings of SPIE Vol. 9680 : Atmospheric and Ocean Optics: Atmospheric Physics.— 2015.— [968023, 4 p.] |
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| Resumo: | Title screen We analyze the classes of problems with an objective necessity to use neural network technologies, i.e. representation and resolution problems in the neural network logical basis. Among these problems, image recognition takes an important place, in particular the classification of multi-dimensional data based on information about textural characteristics. These problems occur in aerospace and seismic monitoring, materials science, medicine and other. We reviewed different approaches for the texture description: statistical, structural, and spectral. We developed a neural network technology for resolving a practical problem of cloud image classification for satellite snapshots from the spectroradiometer MODIS. The cloud texture is described by the statistical characteristics of the GLCM (Gray Level Co- Occurrence Matrix) method. From the range of neural network models that might be applied for image classification, we chose the probabilistic neural network model (PNN) and developed an implementation which performs the classification of the main types and subtypes of clouds. Also, we chose experimentally the optimal architecture and parameters for the PNN model which is used for image classification. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. Режим доступа: по договору с организацией-держателем ресурса |
| Idioma: | inglês |
| Publicado em: |
2015
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| Acesso em linha: | http://dx.doi.org/10.1117/12.2205896 |
| Formato: | Recurso Eletrônico Capítulo de Livro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=646764 |
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| 200 | 1 | |a Neural network technologies for image classification |f A. M. Korikov, A. V. Tungusova | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 7 tit.] | ||
| 330 | |a We analyze the classes of problems with an objective necessity to use neural network technologies, i.e. representation and resolution problems in the neural network logical basis. Among these problems, image recognition takes an important place, in particular the classification of multi-dimensional data based on information about textural characteristics. These problems occur in aerospace and seismic monitoring, materials science, medicine and other. We reviewed different approaches for the texture description: statistical, structural, and spectral. We developed a neural network technology for resolving a practical problem of cloud image classification for satellite snapshots from the spectroradiometer MODIS. The cloud texture is described by the statistical characteristics of the GLCM (Gray Level Co- Occurrence Matrix) method. From the range of neural network models that might be applied for image classification, we chose the probabilistic neural network model (PNN) and developed an implementation which performs the classification of the main types and subtypes of clouds. Also, we chose experimentally the optimal architecture and parameters for the PNN model which is used for image classification. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | 1 | |0 (RuTPU)RU\TPU\network\12028 |t Proceedings of SPIE | |
| 463 | 0 | |0 (RuTPU)RU\TPU\network\12443 |t Vol. 9680 : Atmospheric and Ocean Optics: Atmospheric Physics |o 21st International Symposium, 22–26 June 2015, Tomsk, Russian Federation |o [proceedings] |f Institute of Atmospheric Optics SB RAS ; ed. G. G. Matvienko, O. A. Romanovskii |v [968023, 4 p.] |d 2015 | |
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| 610 | 1 | |a труды учёных ТПУ | |
| 700 | 1 | |a Korikov |b A. M. |c radiophysicist, specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University, doctor of technical sciences |f 1942- |g Anatoly Mikhailovich |3 (RuTPU)RU\TPU\pers\35166 | |
| 701 | 1 | |a Tungusova |b A. V. |g Anna Vladimirovna | |
| 712 | 0 | 2 | |a Томский политехнический университет |b Институт кибернетики, ИК |c 2010-2017 |9 27025 |
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| 856 | 4 | |u http://dx.doi.org/10.1117/12.2205896 | |
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