Nonlinear synthetic discriminant function filters for illumination-invariant pattern recognition

Saul Martinez-Diaz; Vitaly I. Kober

doi:10.1117/1.2940371

1 June 2008 Nonlinear synthetic discriminant function filters for illumination-invariant pattern recognition

Saul Martinez-Diaz, Vitaly I. Kober

Author Affiliations +

Optical Engineering, Vol. 47, Issue 6, 067201 (June 2008). https://doi.org/10.1117/1.2940371

Abstract

Novel nonlinear adaptive composite filters for illumination-invariant pattern recognition are presented. Pattern recognition is carried out with space-variant nonlinear correlation. The information about objects to be recognized, false objects, and a background to be rejected is utilized in an iterative training procedure to design a nonlinear adaptive correlation filter with a given discrimination capability. The designed filter during recognition process adapts its parameters to local statistics of the input image. Computer simulation results obtained with the proposed filters in nonuniformly illuminated test scenes are discussed and compared with those of linear composite correlation filters with respect to discrimination capability, robustness to input additive and impulsive noise, and tolerance to small geometric image distortions.

©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Saul Martinez-Diaz and Vitaly I. Kober "Nonlinear synthetic discriminant function filters for illumination-invariant pattern recognition," Optical Engineering 47(6), 067201 (1 June 2008). https://doi.org/10.1117/1.2940371

Published: 1 June 2008

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