Hybrid composite filter banks for distortion-invariant optical pattern recognition

Laurence G. Hassebrook; Mohammad Rahmati; Bhagavatula Vijaya Kumar

doi:10.1117/12.56166

1 May 1992 Hybrid composite filter banks for distortion-invariant optical pattern recognition

Laurence G. Hassebrook, Mohammad Rahmati, Bhagavatula Vijaya Kumar

Author Affiliations +

Optical Engineering, Vol. 31, Issue 5, (May 1992). https://doi.org/10.1117/12.56166

Abstract

In the past, several different approaches to synthetic discriminant function (SDF) filter design have been proposed, including conventional SDFs, which control the correlation values at the origin; minimum variance SDFs (MVSDFs), which minimize the noise sensitivity of the filters; minimum average correlation energy (MACE) filters, which maximize the peak sharpness; and linear phase coefficient composite (LPCC) filters, which are obtained as the sum of training images weighted by linear phase coefficients. We introduce a new family of SDF filters of which all the above are special cases. Each filter in this family is characterized by two parameters α₁ and α₂. Various choices of (α₁ , α₂) lead to the above special filters. For example, α₁ = 1 and α₂ = 0 leads to MACE LPCC filters, which are hybrid versions of MACE and LPCC filters. This family of filters is evaluated using the minimum probability of error (MPE) criterion and a database of aircraft images. These simulation experiments confirm the superior performance of this filter family. Also, we observe the interesting result that the MPE is at its lowest not for one of the four special filters listed above, but for a combination of them.

Citation Download Citation

Laurence G. Hassebrook, Mohammad Rahmati, and Bhagavatula Vijaya Kumar "Hybrid composite filter banks for distortion-invariant optical pattern recognition," Optical Engineering 31(5), (1 May 1992). https://doi.org/10.1117/12.56166

Published: 1 May 1992

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available