• DocumentCode
    2028600
  • Title

    Learning spatial relations between objects from 3D scenes

  • Author

    Fichtl, Severin ; Alexander, James ; Guerin, Francois ; Mustafa, W. ; Kraft, Daniel ; Kruger, Norbert

  • Author_Institution
    Comput. Sci., Univ. of Aberdeen, Aberdeen, UK
  • fYear
    2013
  • fDate
    18-22 Aug. 2013
  • Firstpage
    1
  • Lastpage
    2
  • Abstract
    In this work, we learn a limited number of abstractions which can then be used to form preconditions for motor actions. These abstractions take the form of spatial relations amongst objects. We consider three “classes” of spatial relation: The objects either are separated from, on-top of, or inside each other. We have tackled this same problem in previous work (Fichtl et al., 2013). Here we report on recent improved results using a novel application of histograms to visually recognise a spatial relation between objects in the environment. Using this histogram based approach we are able to report a very high rate of success when the system is asked to recognise a spatial relation.
  • Keywords
    learning (artificial intelligence); robot vision; 3D scenes; histogram based approach; histograms; motor actions; object spatial relation; spatial relation learning; spatial relations; visual recognition; Conferences; Histograms; Image color analysis; Machine vision; Robot sensing systems; Three-dimensional displays;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Development and Learning and Epigenetic Robotics (ICDL), 2013 IEEE Third Joint International Conference on
  • Conference_Location
    Osaka
  • Type

    conf

  • DOI
    10.1109/DevLrn.2013.6652552
  • Filename
    6652552