2013年
Feature Extraction based on Hierarchical Growing Neural Gas for Informationally Structured Space
2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
- ,
- 開始ページ
- 1
- 終了ページ
- 7
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/IJCNN.2013.6706825
- 出版者・発行元
- IEEE
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.
- リンク情報
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- DOI
- https://doi.org/10.1109/IJCNN.2013.6706825
- DBLP
- https://dblp.uni-trier.de/rec/conf/ijcnn/TodaK13
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000349557200118&DestApp=WOS_CPL
- URL
- http://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2013.html#conf/ijcnn/TodaK13
- ID情報
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- DOI : 10.1109/IJCNN.2013.6706825
- ISSN : 2161-4393
- DBLP ID : conf/ijcnn/TodaK13
- Web of Science ID : WOS:000349557200118