OBJECT RECOGNITION AND LEARNING OF KNOWN AND UNKNOWN TYPES.

M. Ben-Bassat, M. Newhouse, E. I. Bailis

Research output: Contribution to conferencePaperpeer-review

Abstract

A system for real-time object recognition and learning from known and unknown classes is presented. A three-level algorithm is described. Level I does not classify patterns. Using deterministic rules, it acts as a prefilter to eliminate obvious classes from further consideration. Level II algorithm is a modified version of the (k,k prime ) nearest neighbor algorithm designed to deal with objects that do not belong to any of the known classes. Level III algorithm is clustering-oriented. When a new pattern arrives, a clustering algorithm is activated to decide whether to assign this pattern to one of the known classes or to classify it as a new type. Experimental results with the system are reported.

Original languageEnglish
Pages66-71
Number of pages6
StatePublished - 1978
EventProc IEEE Comput Soc Conf Pattern Recognition Image Process - Chicago, IL, USA
Duration: 31 May 19782 Jun 1978

Conference

ConferenceProc IEEE Comput Soc Conf Pattern Recognition Image Process
CityChicago, IL, USA
Period31/05/782/06/78

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