• DocumentCode
    2626589
  • Title

    On a spectral attentional mechanism

  • Author

    Burlina, Philippe ; Lin, Bruce ; Chellappa, Rama

  • Author_Institution
    Comput. Vision Lab., Maryland Univ., College Park, MD, USA
  • fYear
    1996
  • fDate
    18-20 Jun 1996
  • Firstpage
    120
  • Lastpage
    127
  • Abstract
    This paper describes an attentional mechanism based on the interpretation of spectral signatures for detecting regular object configurations in areas of an image delineated using context information. The proposed global operator relies on the spectral analyse´s of edge structure and exploits spatial as well as frequency domain constraints derived from known geometrical models of monitored objects. A decision theoretic method for learning decision regions is presented. Applications of this mechanism are demonstrated for several aerial image interpretation tasks. Specific examples are described for detecting vehicle formations (such as convoys), qualifying the geometry of detected formations, or monitoring the occupancy of regions of interest (such as parking areas, roads, or open areas). Experiments and sensitivity analysis results are reported
  • Keywords
    decision theory; edge detection; object detection; sensitivity analysis; aerial image interpretation tasks; context information; convoys; decision theoretic method; detected formations; frequency domain constraints; geometrical models; global operator; learning decision regions; monitored objects; regular object configurations; sensitivity analysis; spectral attentional mechanism; spectral signatures; vehicle formations; Frequency domain analysis; Geometry; Image edge detection; Monitoring; Object detection; Road vehicles; Sensitivity analysis; Solid modeling; Spectral analysis; Vehicle detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition, 1996. Proceedings CVPR '96, 1996 IEEE Computer Society Conference on
  • Conference_Location
    San Francisco, CA
  • ISSN
    1063-6919
  • Print_ISBN
    0-8186-7259-5
  • Type

    conf

  • DOI
    10.1109/CVPR.1996.517063
  • Filename
    517063