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This paper deals with the issue of evaluating and analyzing geometric point sets in three-dimensional space. Point sets or point clouds are often the product of 3D scanners and depth sensors, which are used in the field of autonomous movement for robots and vehicles. Therefore, for the classification of point sets within an active motion, not fully generated point clouds can be used, but knowledge can be extracted from the raw impulses of the respective time points. Attractors consisting of a continuum of stationary states and hysteretic memories can be used to couple multiple inputs over time given non-independent output quantities of a classifier and applied to suitable neural networks. In this paper, we show a way to assign input point clouds to sets of classes using hysteretic memories, which are transferable to neural networks.
Язык оригинала | английский |
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Название основной публикации | Artificial Intelligence Applications and Innovations - 17th IFIP WG 12.5 International Conference, AIAI 2021, Proceedings |
Редакторы | Ilias Maglogiannis, John Macintyre, Lazaros Iliadis |
Издатель | Springer Nature |
Страницы | 505-517 |
Число страниц | 13 |
ISBN (печатное издание) | 9783030791490 |
DOI | |
Состояние | Опубликовано - 2021 |
Событие | 17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021 - Virtual, Online Продолжительность: 25 июн 2021 → 27 июн 2021 |
Название | IFIP Advances in Information and Communication Technology |
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Том | 627 |
ISSN (печатное издание) | 1868-4238 |
ISSN (электронное издание) | 1868-422X |
конференция | 17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021 |
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Город | Virtual, Online |
Период | 25/06/21 → 27/06/21 |
ID: 86422203