A probabilistic approach to object recognition using local photometry and global geometry
- Creators
- Burl, Michael C.
- Weber, Markus
-
Perona, Pietro
- Others:
- Burkhardt, Hans
- Neumann, Bernd
Abstract
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.
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.Additional details
- Eprint ID
- 94257
- DOI
- 10.1007/bfb0054769
- Resolver ID
- CaltechAUTHORS:20190328-144424617
- NASA/JPL/Caltech
- NSF
- Center for Neuromorphic Systems Engineering, Caltech
- Created
-
2019-03-28Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field
- Series Name
- Lecture Notes in Computer Science
- Series Volume or Issue Number
- 1407