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Proceedings Paper

Customizing a similarity filter for object recognition
Author(s): Gregory J. Power
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Paper Abstract

In object recognition, one goal of matched filter design has been to define a matching function that produces an ideal correlation peak when a target object in an image scene precisely matches the pre-defined template object. The benefit of such a function is that it guarantees a precise detection/identification. The ideal correlation-based function that defines the match has been described as a dirac delta in the correlation plane. This paper suggests that if similarity as opposed to precise matching is the goal of the correlation function, then ony using current two-dimensional correlation techniques will result in a non-dirac delta in the correlation plane. This paper suggests basing the design of the function on the object recognition goal. The approach for correlation function design is demonstrated using psychophysical evidence for class differentiation. A function is designed based on psychophysical experimental results for distinguishing between two simple objects and their deformations: a square and a circle.

Paper Details

Date Published: 12 December 2003
PDF: 9 pages
Proc. SPIE 5201, Photonic Devices and Algorithms for Computing V, (12 December 2003); doi: 10.1117/12.504497
Show Author Affiliations
Gregory J. Power, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 5201:
Photonic Devices and Algorithms for Computing V
Khan M. Iftekharuddin; Abdul Ahad S. Awwal, Editor(s)

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