Indoor place categorization using co-occurrences of LBPs in gray and depth images from RGB-D sensors

Hojung Jung, Oscar Martinez Mozos, Yumi Iwashita, Ryo Kurazume

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

4 Citations (Scopus)

Abstract

Indoor place categorization is an important capability for service robots working and interacting in human environments. This paper presents a new place categorization method which uses information about the spatial correlation between the different image modalities provided by RGB-D sensors. Our approach applies co-occurrence histograms of local binary patterns (LBPs) from gray and depth images that correspond to the same indoor scene. The resulting histograms are used as feature vectors in a supervised classifier. Our experimental results show the effectiveness of our method to categorize indoor places using RGB-D cameras.

Original languageEnglish
Title of host publicationProceedings - 2014 International Conference on Emerging Security Technologies, EST 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages40-45
Number of pages6
ISBN (Electronic)9781479970070
DOIs
Publication statusPublished - Dec 11 2014
Externally publishedYes
Event5th International Conference on Emerging Security Technologies, EST 2014 - Alcala de Henares, Spain
Duration: Sep 10 2014Sep 12 2014

Publication series

NameProceedings - 2014 International Conference on Emerging Security Technologies, EST 2014

Other

Other5th International Conference on Emerging Security Technologies, EST 2014
CountrySpain
CityAlcala de Henares
Period9/10/149/12/14

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Control and Systems Engineering
  • Artificial Intelligence
  • Safety, Risk, Reliability and Quality

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