Object segmentation and recognition in 3D point cloud with language model

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

We proposed a recognition algorithm based on feature extraction and Bag-of-words with language model. Object recognition in 3D rang data of big scene has become an increasingly popular research topic in intelligent vehicle area. Firstly we introduce a fast ground filtering and clustering method and a convenient method to generation training samples. Then we proposed a feature extraction method based on 3D SIFT and FPFH to get descriptors with scale-invariant and rotationinvariant in 3D range data. Furthermore, we proposed a recognition algorithm based on bag-of-words and language model by add spatial semantic information to bag-of-words model to make up the shortcoming of ignoring the relationships between the visual words. At last, experimental results on real laser data depicting rural scenes are presented.

Original languageEnglish
Title of host publicationProceedings of 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479967322
DOIs
Publication statusPublished - 23 Dec 2014
Event2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014 - Beijing, China
Duration: 28 Sept 201430 Sept 2014

Publication series

NameProceedings of 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014

Conference

Conference2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014
Country/TerritoryChina
CityBeijing
Period28/09/1430/09/14

Keywords

  • 3D sift
  • feature extraction
  • object recognition
  • point cloud segmentation

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