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Proceedings Paper

Advanced model-based distortion-invariant filters allowing peak variations
Author(s): Gregory P. House; David P. Casasent
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Paper Abstract

We consider a new detection application for distortion-invariant filters. Several new advances to the MINACE filter are considered. These include: improved object modeling (this provides scene correlation plane peaks near the specified values), improved clutter modeling (no false class training is used), a new trade-off parameter c definition (with the energy of each spectra normalized to one), use of a smaller filter size and a prime factor FFT (this reduces noise effects), zero-mean filters (these allow detection of hot and cold contrast objects), etc. We advance a new peak variance degree of freedom distortion-invariance filter (PVDDF) with many attractive new properties. These advantages include: insurance that the correlation peak values for all distorted objects are close to a given value without requiring that each object have a given correlation peak value (in practice one does not want all distorted object inputs to give the same exact correlation peak value), use of more training images NT without an associated drop in the required threshold and thus better object modeling, and better object function energy E minimization (E reduces as NT increase compared to other filters where E increase as NT increases). This new filter thus achieves both better correlation peak values and better energy minimization (prior filters cannot achieve both goals) by using degrees of freedom.

Paper Details

Date Published: 28 March 1995
PDF: 11 pages
Proc. SPIE 2490, Optical Pattern Recognition VI, (28 March 1995); doi: 10.1117/12.205806
Show Author Affiliations
Gregory P. House, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 2490:
Optical Pattern Recognition VI
David P. Casasent; Tien-Hsin Chao, Editor(s)

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