A probabilistic approach to object recognition using local photometry and global geometry

  • Michael C. Burl
  • Markus Weber
  • Pietro Perona
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1407)


Many object classes, including human faces, can be modeled as a set of characteristic parts arranged in a variable spatial configuration. We introduce a simplified model of a deformable object class and derive the optimal detector for this model. However, the optimal detector is not realizable except under special circumstances (independent part positions). A cousin of the optimal detector is developed which uses “soft” part detectors with a probabilistic description of the spatial arrangement of the parts. Spatial arrangements are modeled probabilistically using shape statistics to achieve invariance to translation, rotation, and scaling. Improved recognition performance over methods based on “hard” part detectors is demonstrated for the problem of face detection in cluttered scenes.


Object Class Matched Filter Part Position Part Match Optimal Detector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Michael C. Burl
    • 1
  • Markus Weber
    • 2
  • Pietro Perona
    • 2
    • 3
  1. 1.Jet Propulsion LaboratoryPasadenaUSA
  2. 2.California Institute of TechnologyPasadenaUSA
  3. 3.Università di PadovaItaly

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