Model-based vision system for object recognition with synthetic aperture radar data

John W. Betz, Robert W. Pinto, Jerry L. Prince

Research output: Contribution to journalConference articlepeer-review

Abstract

An overview of the architecture and implementation of a model-based vision system developed for object recognition with SAR (synthetic aperture radar) data is presented. The initial implementation of the system is currently being used to develop experimental results to guide refinements and enhancements. The system uses detailed analytic prediction models and capable description algorithms, with all knowledge and uncertainty consistently represented. The prediction component automatically generates integrated hierarchical representations of both structural and appearance information, and represents an important step toward automatic object recognition. The recognition system architecture features modular computational agents that support distributed, localized control, with the ability to extract and use object-specific knowledge from the prediction database. The system will serve as a testbed for model-based vision research to allow experimentation with new algorithms and alternative recognition approaches.

Original languageEnglish (US)
Pages (from-to)1618-1621
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
StatePublished - Dec 1 1989
Externally publishedYes
Event1989 International Conference on Acoustics, Speech, and Signal Processing - Glasgow, Scotland
Duration: May 23 1989May 26 1989

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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