A correlation-based algorithm for detecting linearly degraded objects using noisy training images

Victor Karnaukhov; Vitaly Kober

doi:10.1117/12.2319765

17 September 2018 A correlation-based algorithm for detecting linearly degraded objects using noisy training images

Victor Karnaukhov, Vitaly Kober

Proceedings Volume 10752, Applications of Digital Image Processing XLI; 1075220 (2018) https://doi.org/10.1117/12.2319765
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

The paper deals with the design of a composite correlation filter from noisy training images for reliable recognition and localization of distorted targets embedded into cluttered linearly degraded and noisy scenes. We consider the nonoverlapping signal model for the input scene and additive noisy model for the reference. The impulse response of the obtained filter is a linear combination of generalized filters optimized with respect to the peak-to-output energy. The performance of the proposed composite correlation filter is analyzed in terms of discrimination capability and accuracy of target location when the reference objects and input scenes are degraded.

Citation Download Citation

Victor Karnaukhov and Vitaly Kober "A correlation-based algorithm for detecting linearly degraded objects using noisy training images", Proc. SPIE 10752, Applications of Digital Image Processing XLI, 1075220 (17 September 2018); https://doi.org/10.1117/12.2319765

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