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

Burl, Michael C. and Weber, Markus and Perona, Pietro (1998) A probabilistic approach to object recognition using local photometry and global geometry. In: Computer Vision — ECCV’98. Lecture Notes in Computer Science. Vol.II. No.1407. Springer , Berlin, Heidelberg, pp. 628-641. ISBN 9783540646136.

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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.

Item Type:Book Section
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URLURL TypeDescription ReadCube access
Perona, Pietro0000-0002-7583-5809
Additional Information:© Springer-Verlag Berlin Heidelberg 1998. The research described in this paper has been carried out in part by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. It was funded in part by the NSF Center for Neuromorphic Systems Engineering at Caltech.
Funding AgencyGrant Number
Center for Neuromorphic Systems Engineering, CaltechUNSPECIFIED
Subject Keywords:Object Class; Matched Filter; Part Position; Part Match; Optimal Detector
Series Name:Lecture Notes in Computer Science
Issue or Number:1407
Record Number:CaltechAUTHORS:20190328-144424617
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94257
Deposited By: George Porter
Deposited On:28 Mar 2019 23:01
Last Modified:08 May 2020 23:13

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