Published June 1996
| Version public
Book Section - Chapter
Recognition of planar object classes
Abstract
We present a new framework for recognizing planar object classes, which is based on local feature detectors and a probabilistic model of the spatial arrangement of the features. The allowed object deformations are represented through shape statistics, which are learned from examples. Instances of an object in an image are detected by finding the appropriate features in the correct spatial configuration. The algorithm is robust with respect to partial occlusion, detector false alarms, and missed features. A 94% success rate was achieved for the problem of locating quasi-frontal views of faces in cluttered scenes.
Additional Information
© 1996 IEEE. Date of Current Version: 06 August 2002. The authors wish to thank Thomas Leung of Berkeley for providing the feature detection code and test images used in the face localization experiments. This work is supported in part by the Center for Neuromorphic Systems Engineering as a part of the National Science Foundation Engineering Research Center Program; and by the California Trade and Commerce Agency, Office of Strategic Technology. Generous support of the face localization work was provided by INTEL.Additional details
Identifiers
- Eprint ID
- 29031
- Resolver ID
- CaltechAUTHORS:20120131-084642667
Funding
- Center for Neuromorphic Systems Engineering (CNSE)
- California Trade and Commerce Agency, Office of Strategic Technology
- INTEL
Dates
- Created
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2012-02-09Created from EPrint's datestamp field
- Updated
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2021-11-09Created from EPrint's last_modified field
Caltech Custom Metadata
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- INSPEC Accession Number
- Other Numbering System Identifier
- 5329273