Segmentation using models of expected structure

Stephen Shemlon; Tajen Liang; Kyugon Cho; Stanley M. Dunn

doi:10.1117/12.25177

1 February 1991 Segmentation using models of expected structure

Stephen Shemlon, Tajen Liang, Kyugon Cho, Stanley M. Dunn

Proceedings Volume 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques; (1991) https://doi.org/10.1117/12.25177
Event: Advances in Intelligent Robotics Systems, 1990, Boston, MA, United States

Abstract

This paper outlines the framework of an image segmentation system based on the expected presenialion of objects in an image. The paradigm uses models that best characterize those objects that are likely to be present in a scene as captured by a given image formation process. We present the parameters for describing the expected presentations and show how they can be developed into a regionbased image algebra that is a generalized mechanism for reasoning and planning image segmentation and subsequent machine learning tasks. We present results of experiments with Transmission Electron Microscope (TEM) serial sections aerial photographs of urban scenes Mill brain scans and dental radiographs.

Citation Download Citation

Stephen Shemlon, Tajen Liang, Kyugon Cho, and Stanley M. Dunn "Segmentation using models of expected structure", Proc. SPIE 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques, (1 February 1991); https://doi.org/10.1117/12.25177

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