Using expected localization in segmentation

Stephen Shemlon; Kyugon Cho; Stanley M. Dunn

doi:10.1117/12.57064

1 February 1992 Using expected localization in segmentation

Stephen Shemlon, Kyugon Cho, Stanley M. Dunn

Proceedings Volume 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques; (1992) https://doi.org/10.1117/12.57064
Event: Robotics '91, 1991, Boston, MA, United States

Abstract

Segmentation paradigms are based on manipulating some distinguishing characteristic of the various objects that are present in the image being analyzed. These characteristics are often well known for given objects in a given class of images. The intelligent use of this knowledge can simplify the segmentation process without necessarily targeting it for a particular class of images. This paper outlines a segmentation paradigm that uses models which characterize the expected presentation of possible image objects. It explains how knowledge of the expected localization of certain objects can be used to refine the segmentation process, to optimize object extraction and identification, and to learn some invariant characteristics of the objects and their surroundings, for use by high level intelligent processes. We present results of experiments with MRI human brain scans, dental radiographs, and transmission electron microscope (TEM) serial sections of hemocytes (insect blood cells).

Citation Download Citation

Stephen Shemlon, Kyugon Cho, and Stanley M. Dunn "Using expected localization in segmentation", Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); https://doi.org/10.1117/12.57064

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