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Proceedings Paper

Database management system for fast object recognition using normalized interval vertex descriptors
Author(s): Ramon Parra-Loera; Wiley E. Thompson; Gerald M. Flachs
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Paper Abstract

The development of a vision system for fast recognition of `flat'' rigid 2-D representations of man made objects belonging to a large library is presented here. The library is organized into a relational database where every tuple represents a prototype class. Tuples are made of attributes defined over features characterizing the object. Classification speed is gained by constraining the number of comparisons of the unknown object representations to those in the library of known objects. Comparisons are only made on a `candidate'' set of possible prototypes (usually smaller than the total number of prototypes) derived from the library `object'' set through the application of successive refinement filters (restriction operators) operating on the database attributes. Normalized Interval Vertex Descriptors (NIVD) representation is used to describe objects. NIVDs, a representation derived from the physical characteristic of an object (vertices and sides), not only provides a compact representation, but it also allows the definition of attributes that can be used to define the relation. In addition, since NIVD representation is translation, rotation, and scale invariant, recognition is performed without regard for orientation, position, and scale of the objects. Experimental results of this process are also included.

Paper Details

Date Published: 1 November 1992
PDF: 13 pages
Proc. SPIE 1823, Machine Vision Applications, Architectures, and Systems Integration, (1 November 1992); doi: 10.1117/12.132065
Show Author Affiliations
Ramon Parra-Loera, New Mexico State Univ. (United States)
Wiley E. Thompson, New Mexico State Univ. (United States)
Gerald M. Flachs, New Mexico State Univ. (United States)

Published in SPIE Proceedings Vol. 1823:
Machine Vision Applications, Architectures, and Systems Integration
Bruce G. Batchelor; Susan Snell Solomon; Frederick M. Waltz, Editor(s)

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