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Proceedings Paper

Segmentation using models of expected structure
Author(s): Stephen Shemlon; Tajen Liang; Kyugon Cho; Stanley M. Dunn
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Paper 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.

Paper Details

Date Published: 1 February 1991
PDF: 12 pages
Proc. SPIE 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques, (1 February 1991); doi: 10.1117/12.25177
Show Author Affiliations
Stephen Shemlon, Rutgers Univ. (United States)
Tajen Liang, Rutgers Univ. (United States)
Kyugon Cho, Rutgers Univ. (United States)
Stanley M. Dunn, Rutgers Univ. (United States)


Published in SPIE Proceedings Vol. 1381:
Intelligent Robots and Computer Vision IX: Algorithms and Techniques
David P. Casasent, Editor(s)

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