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

Fingerprint

Information use
Classifiers
Cameras
Robots
Sensors

All Science Journal Classification (ASJC) codes

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

Cite this

Jung, H., Mozos, O. M., Iwashita, Y., & Kurazume, R. (2014). Indoor place categorization using co-occurrences of LBPs in gray and depth images from RGB-D sensors. In Proceedings - 2014 International Conference on Emerging Security Technologies, EST 2014 (pp. 40-45). [6982772] (Proceedings - 2014 International Conference on Emerging Security Technologies, EST 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EST.2014.23

Indoor place categorization using co-occurrences of LBPs in gray and depth images from RGB-D sensors. / Jung, Hojung; Mozos, Oscar Martinez; Iwashita, Yumi; Kurazume, Ryo.

Proceedings - 2014 International Conference on Emerging Security Technologies, EST 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 40-45 6982772 (Proceedings - 2014 International Conference on Emerging Security Technologies, EST 2014).

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

Jung, H, Mozos, OM, Iwashita, Y & Kurazume, R 2014, Indoor place categorization using co-occurrences of LBPs in gray and depth images from RGB-D sensors. in Proceedings - 2014 International Conference on Emerging Security Technologies, EST 2014., 6982772, Proceedings - 2014 International Conference on Emerging Security Technologies, EST 2014, Institute of Electrical and Electronics Engineers Inc., pp. 40-45, 5th International Conference on Emerging Security Technologies, EST 2014, Alcala de Henares, Spain, 9/10/14. https://doi.org/10.1109/EST.2014.23
Jung H, Mozos OM, Iwashita Y, Kurazume R. Indoor place categorization using co-occurrences of LBPs in gray and depth images from RGB-D sensors. In Proceedings - 2014 International Conference on Emerging Security Technologies, EST 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 40-45. 6982772. (Proceedings - 2014 International Conference on Emerging Security Technologies, EST 2014). https://doi.org/10.1109/EST.2014.23
Jung, Hojung ; Mozos, Oscar Martinez ; Iwashita, Yumi ; Kurazume, Ryo. / Indoor place categorization using co-occurrences of LBPs in gray and depth images from RGB-D sensors. Proceedings - 2014 International Conference on Emerging Security Technologies, EST 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 40-45 (Proceedings - 2014 International Conference on Emerging Security Technologies, EST 2014).
@inproceedings{fac8d9465e89473d9cf4fdd1712dc56f,
title = "Indoor place categorization using co-occurrences of LBPs in gray and depth images from RGB-D sensors",
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.",
author = "Hojung Jung and Mozos, {Oscar Martinez} and Yumi Iwashita and Ryo Kurazume",
year = "2014",
month = "12",
day = "11",
doi = "10.1109/EST.2014.23",
language = "English",
series = "Proceedings - 2014 International Conference on Emerging Security Technologies, EST 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "40--45",
booktitle = "Proceedings - 2014 International Conference on Emerging Security Technologies, EST 2014",
address = "United States",

}

TY - GEN

T1 - Indoor place categorization using co-occurrences of LBPs in gray and depth images from RGB-D sensors

AU - Jung, Hojung

AU - Mozos, Oscar Martinez

AU - Iwashita, Yumi

AU - Kurazume, Ryo

PY - 2014/12/11

Y1 - 2014/12/11

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84921271890&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84921271890&partnerID=8YFLogxK

U2 - 10.1109/EST.2014.23

DO - 10.1109/EST.2014.23

M3 - Conference contribution

AN - SCOPUS:84921271890

T3 - Proceedings - 2014 International Conference on Emerging Security Technologies, EST 2014

SP - 40

EP - 45

BT - Proceedings - 2014 International Conference on Emerging Security Technologies, EST 2014

PB - Institute of Electrical and Electronics Engineers Inc.

ER -