Maximum-Likelihood Image Classification

Miles N. Wernick; G. Michael Morris

doi:10.1117/12.976607

22 August 1988 Maximum-Likelihood Image Classification

Miles N. Wernick, G. Michael Morris

Proceedings Volume 0938, Digital and Optical Shape Representation and Pattern Recognition; (1988) https://doi.org/10.1117/12.976607
Event: 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, 1988, Orlando, FL, United States

Abstract

An essential feature of a practical automatic image recognition system is the ability to tolerate certain types of variations within images. The recognition of images subject to intrinsic variations can be treated as a sorting task in which an image is identified as a member of some class of images. Herein, the maximum-likelihood strategy, an important tool in the field of statistical decision theory, is applied to the image classification problem. We show that the strategy can be implemented in a standard image correlation system and that excellent classification results can be obtained.

Citation Download Citation

Miles N. Wernick and G. Michael Morris "Maximum-Likelihood Image Classification", Proc. SPIE 0938, Digital and Optical Shape Representation and Pattern Recognition, (22 August 1988); https://doi.org/10.1117/12.976607

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