• DocumentCode
    2795516
  • Title

    A non-myopic approach to visual search

  • Author

    Vogel, Julia ; Murphy, Kevin

  • Author_Institution
    Univ. of British Columbia, Vancouver
  • fYear
    2007
  • fDate
    28-30 May 2007
  • Firstpage
    227
  • Lastpage
    234
  • Abstract
    We show how a greedy approach to visual search - i.e., directly moving to the most likely location of the target - can be suboptimal, if the target object is hard to detect. Instead it is more efficient and leads to higher detection accuracy to first look for other related objects, that are easier to detect. These provide contextual priors for the target that make it easier to find. We demonstrate this in simulation using POMDP models, focussing on two special cases: where the target object is contained within the related object, and where the target object is spatially adjacent to the related object.
  • Keywords
    Markov processes; control engineering computing; greedy algorithms; object detection; robot vision; greedy approach; nonmyopic approach; partially observed Markov decision process; robot; target object detection; visual search; Buildings; Computational modeling; Computer displays; Computer science; Computer vision; Detectors; Object detection; Robot sensing systems; Robot vision systems; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Robot Vision, 2007. CRV '07. Fourth Canadian Conference on
  • Conference_Location
    Montreal, Que.
  • Print_ISBN
    0-7695-2786-8
  • Type

    conf

  • DOI
    10.1109/CRV.2007.5
  • Filename
    4228543