• DocumentCode
    2995267
  • Title

    A Multi-sensor Fusion Framework in 3-D

  • Author

    Jain, Vinesh ; Miller, A.C. ; Mundy, Joseph L.

  • Author_Institution
    Vision Syst. Inc., Providence, RI, USA
  • fYear
    2013
  • fDate
    23-28 June 2013
  • Firstpage
    314
  • Lastpage
    319
  • Abstract
    The majority of existing image fusion techniques operate in the 2-d image domain which perform well for imagery of planar regions but fails in presence of any 3-d relief and provides inaccurate alignment of imagery from different sensors. A framework for multi-sensor image fusion in 3-d is proposed in this paper. The imagery from different sensors, specifically EO and IR, are fused in a common 3-d reference coordinate frame. A dense probabilistic and volumetric 3-d model is reconstructed from each of the sensors. The imagery is registered by aligning the 3-d models as the underlying 3-d structure in the images is the true invariant information. The image intensities are back-projected onto a 3-d model and every discretized location (voxel) of the 3-d model stores an array of intensities from different modalities. This 3-d model is forward-projected to produce a fused image of EO and IR from any viewpoint.
  • Keywords
    image fusion; image registration; infrared imaging; probability; stereo image processing; 2D image domain; 3D model alignment; 3D multisensor fusion framework; 3D relief; EO image; IR image; back-projection; common 3D reference coordinate frame; dense probabilistic model; discretized location; electrooptical image; forward-projection; image 3D structure; image fusion technique; image intensity; image registration; imagery alignment; infrared image; multisensor image fusion; planar region; true invariant information; volumetric 3D model; voxel; Cameras; Computational modeling; Image reconstruction; Image sensors; Sensor fusion; Solid modeling; 3-d model; multi-sensor fusion; probability; region classification; registration; volumetric models;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition Workshops (CVPRW), 2013 IEEE Conference on
  • Conference_Location
    Portland, OR
  • Type

    conf

  • DOI
    10.1109/CVPRW.2013.54
  • Filename
    6595893