Feature extraction based on hierarchical growing neural gas for informationally structured space

Yuichiro Toda, Naoyuki Kubota

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper proposes a method of feature extraction from 3D point clouds for informationally structured space including sensor networks and robot partners for co-existing with people. The informationally structured space realizes the quick update and access of valuable and useful information for both people and robots on real and virtual environments. Our method is based on Hierarchical Growing Neural Gas (HGNG). This method is one of self-organizing neural network based on unsupervised learning First, we propose 3D map building method using Kinect in order to acquire the 3D point clouds. Next, we propose the method of the feature extracting method based on HGNG. Finally, we show experimental results of the proposed method and discuss the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2013 International Joint Conference on Neural Networks, IJCNN 2013
DOIs
Publication statusPublished - Dec 1 2013
Externally publishedYes
Event2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX, United States
Duration: Aug 4 2013Aug 9 2013

Other

Other2013 International Joint Conference on Neural Networks, IJCNN 2013
CountryUnited States
CityDallas, TX
Period8/4/138/9/13

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Toda, Y., & Kubota, N. (2013). Feature extraction based on hierarchical growing neural gas for informationally structured space. In 2013 International Joint Conference on Neural Networks, IJCNN 2013 [6706825] https://doi.org/10.1109/IJCNN.2013.6706825