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Proceedings Paper

Using expected localization in segmentation
Author(s): Stephen Shemlon; Kyugon Cho; Stanley M. Dunn
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Paper 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).

Paper Details

Date Published: 1 February 1992
PDF: 12 pages
Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); doi: 10.1117/12.57064
Show Author Affiliations
Stephen Shemlon, Rutgers Univ. (United States)
Kyugon Cho, Rutgers Univ. (United States)
Stanley M. Dunn, Rutgers Univ. (United States)


Published in SPIE Proceedings Vol. 1607:
Intelligent Robots and Computer Vision X: Algorithms and Techniques
David P. Casasent, Editor(s)

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