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Real-time 3D point cloud segmentation using Growing Neural Gas with Utility

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

This paper proposes a real-time feature extraction and segmentation method for a 3D point cloud. First of all, we apply Growing Neural Gas with Utility (GNG-U) to the point cloud for learning a topological structure. However, the standard GNG-U cannot learn the topological structure of 3D space environment and color information simultaneously. To this end, we then modify the GNG-U algorithm by using a weight vector. we propose a surface feature extraction and segmentation method by efficiently utilizing the topological structure. Our segmentation method is based on a region growing method whose similarity value uses the inner value of two normal vectors connected by the topological structure. We show experimental results of the proposed method and discuss the effectiveness of the proposed method.
Original languageEnglish
Title of host publication2016 9th International Conference on Human System Interactions (HSI)
PublisherIEEE
Pages418-422
ISBN (Electronic)978-1-5090-1729-4
ISBN (Print)978-1-5090-1730-0
DOIs
Publication statusPublished - 4 Aug 2016
Event9th International Conference on Human System Interactions: HSI 2016 - University of Portsmouth, Portsmouth, United Kingdom
Duration: 6 Jul 20168 Jul 2016

Conference

Conference9th International Conference on Human System Interactions
Abbreviated titleHSI 2016
CountryUnited Kingdom
CityPortsmouth
Period6/07/168/07/16

Documents

  • Real-time 3D point cloud segmentation using Growing Neural Gas with Utility

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