@inproceedings{8159c915d9f346b498d9469f9799785c,
title = "Real-time 3D point cloud segmentation using Growing Neural Gas with Utility",
abstract = "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.",
keywords = "component, formatting, insert (key words), style, styling",
author = "Yuichiro Toda and Zhaojie Ju and Hui Yu and Naoyuki Takesue and Kazuyoshi Wada and Naoyuki Kubota",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 9th International Conference on Human System Interactions, HSI 2016 ; Conference date: 06-07-2016 Through 08-07-2016",
year = "2016",
month = aug,
day = "2",
doi = "10.1109/HSI.2016.7529667",
language = "English",
series = "Proceedings - 2016 9th International Conference on Human System Interactions, HSI 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "418--422",
booktitle = "Proceedings - 2016 9th International Conference on Human System Interactions, HSI 2016",
}