Face recognition based on the proximity measure clustering; Компьютерная оптика; Т. 40, № 5
| Parent link: | Компьютерная оптика: научный журнал/ Институт систем обработки изображений Российской академии наук.— , 1987- Т. 40, № 5.— 2016.— [P. 740-745] |
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| Autore principale: | |
| Ente Autore: | |
| Altri autori: | , |
| Riassunto: | Title Screen In this paper problems of featureless face recognition are considered. The recognition is based on clustering the proximity measures between the distributions of brightness clusters cardinality for segmented images. As a proximity measure three types of distances are used in this work: the Euclidean, cosine and Kullback-Leibler distances. Image segmentation and proximity measure clustering are carried out by means of a software model of the recurrent neural network. Results of the experimental studies of the proposed approach are presented. Режим доступа: по договору с организацией-держателем ресурса |
| Lingua: | inglese |
| Pubblicazione: |
2016
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| Serie: | Image Processing, Pattern Recognition |
| Soggetti: | |
| Accesso online: | http://elibrary.ru/item.asp?id=27425383 http://earchive.tpu.ru/handle/11683/36151 |
| Natura: | MixedMaterials Elettronico Capitolo di libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=652454 |
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| 200 | 1 | |a Face recognition based on the proximity measure clustering |f V. B. Nemirovskiy, A. K. Stoyanov, D. S. Goremykina | |
| 203 | |a Text |c electronic | ||
| 225 | 1 | |a Image Processing, Pattern Recognition | |
| 300 | |a Title Screen | ||
| 320 | |a [References.: 18 tit.] | ||
| 330 | |a In this paper problems of featureless face recognition are considered. The recognition is based on clustering the proximity measures between the distributions of brightness clusters cardinality for segmented images. As a proximity measure three types of distances are used in this work: the Euclidean, cosine and Kullback-Leibler distances. Image segmentation and proximity measure clustering are carried out by means of a software model of the recurrent neural network. Results of the experimental studies of the proposed approach are presented. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t Компьютерная оптика |o научный журнал |f Институт систем обработки изображений Российской академии наук |d 1987- | ||
| 463 | |t Т. 40, № 5 |v [P. 740-745] |d 2016 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
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| 610 | 1 | |a расстояние Кульбака-Лейблера | |
| 610 | 1 | |a распознавание лиц | |
| 700 | 1 | |a Nemirovskiy |b V. B. |c specialist in the field of informatics and computer engineering |c associate professor of Tomsk Polytechnic University, candidate of physico-mathematical sciences |f 1945- |g Viktor Borisovich |3 (RuTPU)RU\TPU\pers\33683 | |
| 701 | 1 | |a Stoyanov |b A. K. |c specialist in the field of Informatics and computer engineering |c Associate Professor of Tomsk Polytechnic University, candidate of technical sciences |f 1946- |g Aleksandr Kirillovich |3 (RuTPU)RU\TPU\pers\33684 | |
| 701 | 1 | |a Goremykina |b D. S. |g Darjya Sergeevna | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет (ТПУ) |b Институт социально-гуманитарных технологий (ИСГТ) |b Кафедра истории и регионоведения (ИСТ) |3 (RuTPU)RU\TPU\col\18372 |
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