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Proceedings Paper

Statistical investigations of multiscale image structure (Proceedings Only)
Author(s): James M. Coggins
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Paper Abstract

This artificial visual system (AVS) is a computational framework for computer vision based on spatial filtering and statistical pattern recognition. Computer vision tasks are often poorly defined; the AVS clarifies the kinds of visual tasks that can be defined and what constitutes a well-defined task. `Segmentation'' is not a well-defined task. Edge detection is revealed to be an absurd task. A filter set composed of multiscale Gaussians alone captures the structure of Koenderink''s generic neighborhood operators when a pattern is constructed from the responses at a pixel and neighboring locations, where the distance to the selected neighbors increases with larger scale. Prior studies of the feature space formed by multiscale Gaussians reveal surprising power in the multiscale Gaussians alone. New studies support this observation. Contrary to common belief, we show how nonlocal, spatial, geometric structure can be captured using statistical pattern recognition operations in the AVS framework. A procedure is defined for deriving a single composite filter providing optimal separation of two clusters in feature space.

Paper Details

Date Published: 22 September 1992
PDF: 14 pages
Proc. SPIE 1808, Visualization in Biomedical Computing '92, (22 September 1992); doi: 10.1117/12.131074
Show Author Affiliations
James M. Coggins, Univ. of North Carolina/Chapel Hill (United States)

Published in SPIE Proceedings Vol. 1808:
Visualization in Biomedical Computing '92
Richard A. Robb, Editor(s)

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