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

Fingerprint

Feature extraction
Robots
Unsupervised learning
Gases
Virtual reality
Sensor networks
Neural networks

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

Feature extraction based on hierarchical growing neural gas for informationally structured space. / Toda, Yuichiro; Kubota, Naoyuki.

2013 International Joint Conference on Neural Networks, IJCNN 2013. 2013. 6706825.

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

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, 2013 International Joint Conference on Neural Networks, IJCNN 2013, Dallas, TX, United States, 8/4/13. https://doi.org/10.1109/IJCNN.2013.6706825
Toda Y, Kubota N. Feature extraction based on hierarchical growing neural gas for informationally structured space. In 2013 International Joint Conference on Neural Networks, IJCNN 2013. 2013. 6706825 https://doi.org/10.1109/IJCNN.2013.6706825
Toda, Yuichiro ; Kubota, Naoyuki. / Feature extraction based on hierarchical growing neural gas for informationally structured space. 2013 International Joint Conference on Neural Networks, IJCNN 2013. 2013.
@inproceedings{ac60304291734671a91a66d6fc0f33e4,
title = "Feature extraction based on hierarchical growing neural gas for informationally structured space",
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.",
author = "Yuichiro Toda and Naoyuki Kubota",
year = "2013",
month = "12",
day = "1",
doi = "10.1109/IJCNN.2013.6706825",
language = "English",
isbn = "9781467361293",
booktitle = "2013 International Joint Conference on Neural Networks, IJCNN 2013",

}

TY - GEN

T1 - Feature extraction based on hierarchical growing neural gas for informationally structured space

AU - Toda, Yuichiro

AU - Kubota, Naoyuki

PY - 2013/12/1

Y1 - 2013/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84893581819&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893581819&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2013.6706825

DO - 10.1109/IJCNN.2013.6706825

M3 - Conference contribution

AN - SCOPUS:84893581819

SN - 9781467361293

BT - 2013 International Joint Conference on Neural Networks, IJCNN 2013

ER -