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

Yuichiro Toda, Zhaojie Ju, Hui Yu, Naoyuki Takesue, Kazuyoshi Wada, Naoyuki Kubota

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

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 2016 9th International Conference on Human System Interactions, HSI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages418-422
Number of pages5
ISBN (Electronic)9781509017294
DOIs
Publication statusPublished - Aug 2 2016
Externally publishedYes
Event9th International Conference on Human System Interactions, HSI 2016 - Portsmouth, United Kingdom
Duration: Jul 6 2016Jul 8 2016

Other

Other9th International Conference on Human System Interactions, HSI 2016
CountryUnited Kingdom
CityPortsmouth
Period7/6/167/8/16

Fingerprint

Feature extraction
Gases
Color

Keywords

  • component
  • formatting
  • insert (key words)
  • style
  • styling

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Human-Computer Interaction

Cite this

Toda, Y., Ju, Z., Yu, H., Takesue, N., Wada, K., & Kubota, N. (2016). Real-time 3D point cloud segmentation using Growing Neural Gas with Utility. In Proceedings - 2016 9th International Conference on Human System Interactions, HSI 2016 (pp. 418-422). [7529667] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HSI.2016.7529667

Real-time 3D point cloud segmentation using Growing Neural Gas with Utility. / Toda, Yuichiro; Ju, Zhaojie; Yu, Hui; Takesue, Naoyuki; Wada, Kazuyoshi; Kubota, Naoyuki.

Proceedings - 2016 9th International Conference on Human System Interactions, HSI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 418-422 7529667.

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

Toda, Y, Ju, Z, Yu, H, Takesue, N, Wada, K & Kubota, N 2016, Real-time 3D point cloud segmentation using Growing Neural Gas with Utility. in Proceedings - 2016 9th International Conference on Human System Interactions, HSI 2016., 7529667, Institute of Electrical and Electronics Engineers Inc., pp. 418-422, 9th International Conference on Human System Interactions, HSI 2016, Portsmouth, United Kingdom, 7/6/16. https://doi.org/10.1109/HSI.2016.7529667
Toda Y, Ju Z, Yu H, Takesue N, Wada K, Kubota N. Real-time 3D point cloud segmentation using Growing Neural Gas with Utility. In Proceedings - 2016 9th International Conference on Human System Interactions, HSI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 418-422. 7529667 https://doi.org/10.1109/HSI.2016.7529667
Toda, Yuichiro ; Ju, Zhaojie ; Yu, Hui ; Takesue, Naoyuki ; Wada, Kazuyoshi ; Kubota, Naoyuki. / Real-time 3D point cloud segmentation using Growing Neural Gas with Utility. Proceedings - 2016 9th International Conference on Human System Interactions, HSI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 418-422
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