Using expected localization in segmentation

Stephen Shemlon; Kyugon Cho; Stanley M. Dunn

doi:10.1117/12.57064

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

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