Adaptive resonance theory and self-organizing morphological kernels

John P. Sharpe; Nilgun Sungar; Kristina M. Johnson

doi:10.1117/12.179210

30 June 1994 Adaptive resonance theory and self-organizing morphological kernels

John P. Sharpe, Nilgun Sungar, Kristina M. Johnson

Proceedings Volume 2300, Image Algebra and Morphological Image Processing V; (1994) https://doi.org/10.1117/12.179210
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

In this paper we describe our recent work developing automated methods for generation of kernels or structuring elements for use in the hit-or-miss transform. We show how a neural network algorithm (Fuzzy Adaptive Resonance Theory) generates hit and miss structuring elements that can be used with a fuzzy morphology to detect a class of objects and we illustrate with computer simulations.

Citation Download Citation

John P. Sharpe, Nilgun Sungar, and Kristina M. Johnson "Adaptive resonance theory and self-organizing morphological kernels", Proc. SPIE 2300, Image Algebra and Morphological Image Processing V, (30 June 1994); https://doi.org/10.1117/12.179210

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