DOI

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 оригиналаанглийский
Название основной публикацииArtificial Intelligence Applications and Innovations - 17th IFIP WG 12.5 International Conference, AIAI 2021, Proceedings
РедакторыIlias Maglogiannis, John Macintyre, Lazaros Iliadis
ИздательSpringer Nature
Страницы505-517
Число страниц13
ISBN (печатное издание)9783030791490
DOI
СостояниеОпубликовано - 2021
Событие17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021 - Virtual, Online
Продолжительность: 25 июн 202127 июн 2021

Серия публикаций

НазваниеIFIP Advances in Information and Communication Technology
Том627
ISSN (печатное издание)1868-4238
ISSN (электронное издание)1868-422X

конференция

конференция17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021
ГородVirtual, Online
Период25/06/2127/06/21

    Предметные области Scopus

  • Информационные системы
  • Компьютерные сети и коммуникации
  • Информационные системы и управление

ID: 86422203