Classification of Point Clouds with Neural Networks and Continuum-Type Memories

Stefan Reitmann, Elena V. Kudryashova, Bernhard Jung, Volker Reitmann

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationArtificial Intelligence Applications and Innovations - 17th IFIP WG 12.5 International Conference, AIAI 2021, Proceedings
EditorsIlias Maglogiannis, John Macintyre, Lazaros Iliadis
PublisherSpringer Nature
Pages505-517
Number of pages13
ISBN (Print)9783030791490
DOIs
StatePublished - 2021
Event17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021 - Virtual, Online
Duration: 25 Jun 202127 Jun 2021

Publication series

NameIFIP Advances in Information and Communication Technology
Volume627
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Conference

Conference17th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2021
CityVirtual, Online
Period25/06/2127/06/21

Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management

Keywords

  • Classification
  • Hysteretic memory
  • Neural network
  • Point clouds

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