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Proceedings Paper

Distortion parameter estimation using complex distortion-invariant correlation filter bank responses
Author(s): Laurence G. Hassebrook; Michael E. Lhamon; Mao Wang; Jyoti P. Chatterjee
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Paper Abstract

We present an approach to estimating the distortion parameters of distorted target objects. We first detect the presence and position of the targets with a bank of distortion-invariant correlation filters. The filter set we use, known as hybrid composite filters, yields complex responses at the target locations. The phase angles at the target locations are linearly related to the distortion parameter. In the case of in-plane or out-of-plane distortion, the distortion parameter is the rotation angle of the object. We assume a finite number of values for the distortion parameter so an M-ary classification algorithm is used to determine the input distortion parameter. The classification algorithm is a Maximum Likelihood M-ary classification algorithm, more commonly known as 'largest of' filtering, which determines the most likely distortion parameter value. A set of complex signatures is formed with weighted linear combinations of the different filter target responses. The signature with the highest correlation with the set of known signatures indicates the associated distortion parameter. In practice, we do not use all the hybrid filters and the ones we do use are optimally selected and combined for detection and discrimination. Since we know the target location, we can use a different set of filters, optimized for angle estimation, and implement them with an inner product operation. Inner product filter responses are complex so estimation ambiguities may arise. We present a selection criterion, optimal for signal-to-noise ratio. We combine knowledge of the ambiguities with this optimal selection criterion to prevent ambiguities and achieve minimal estimation error. Numerical simulations are presented to demonstrate this new approach to parameter estimation.

Paper Details

Date Published: 28 March 1995
PDF: 13 pages
Proc. SPIE 2490, Optical Pattern Recognition VI, (28 March 1995); doi: 10.1117/12.205807
Show Author Affiliations
Laurence G. Hassebrook, Univ. of Kentucky (United States)
Michael E. Lhamon, Univ. of Kentucky (United States)
Mao Wang, Univ. of Kentucky (United States)
Jyoti P. Chatterjee, Univ. of Kentucky (United States)

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

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