Robust object representation through object-relevant use of scale

Bryan S. Morse; Stephen M. Pizer; Daniel S. Fritsch

doi:10.1117/12.175046

11 May 1994 Robust object representation through object-relevant use of scale

Bryan S. Morse, Stephen M. Pizer, Daniel S. Fritsch

Proceedings Volume 2167, Medical Imaging 1994: Image Processing; (1994) https://doi.org/10.1117/12.175046
Event: Medical Imaging 1994, 1994, Newport Beach, CA, United States

Abstract

In previously published papers we have presented an object representation known as a core that represents an object at measurement scales (tolerances) relative to the local size of the object. Such object-relevant scale allows one to be more sensitive to such detail (and, of course, the effects of noise, blurring, and other image degradation) for smaller objects while being less sensitive to such detail (and image degradation) for larger objects. This produces a more robust mechanism that is able to trade off between sensitivity to noise and loss of detail by considering the properties of the object involved. This paper, after briefly reviewing the definition and computation of cores, studies this relationship between noise and object size and shows that the algorithms for computing cores do indeed produce more stable results for larger objects by automatically selecting correspondingly larger, less noise-sensitive scales.

Citation Download Citation

Bryan S. Morse, Stephen M. Pizer, and Daniel S. Fritsch "Robust object representation through object-relevant use of scale", Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); https://doi.org/10.1117/12.175046

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