Statistical Segmentation Of Digital Images

Jay B. Jordan; G. M. Flachs

doi:10.1117/12.939988

21 August 1987 Statistical Segmentation Of Digital Images

Jay B. Jordan, G. M. Flachs

Proceedings Volume 0754, Optical and Digital Pattern Recognition; (1987) https://doi.org/10.1117/12.939988
Event: OE LASE'87 and EO Imaging Symposium, 1987, Los Angeles, CA, United States

Abstract

Statistically based models of digital images are used to locate and segment objects of interest from background scenes. Three models are presented and evaluated. These models are based on a Bayesian cost function, a Neyman-Pearson constant false alarm rate function, and a maximum entropy function. Detailed algorithms are presented for separating object regions from background clutter using each of these statistical methods.

Citation Download Citation

Jay B. Jordan and G. M. Flachs "Statistical Segmentation Of Digital Images", Proc. SPIE 0754, Optical and Digital Pattern Recognition, (21 August 1987); https://doi.org/10.1117/12.939988

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