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Proceedings Paper

Localized Noise Propagation Effects In Parameter Transforms
Author(s): Andrea Califano; Ruud M. Bolle
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Paper Abstract

A parameter transform produces a density function on a parameter space. Ideally each instance of a parametric shape in the input would contribute to the density with a delta function. Due to noise these delta functions will be broadened. However, depending on the location and orientation of the parametric shapes in the input, differently shaped peaks will result. The reason for this is twofold: (1) In general a parameter transform is a nonlinear operation; (2) A parameter transform may also be a function of the location of the parametric shape in the input. We present a general framework that deals with both the above mentioned problems. By weighing the response of the transform by the determinant of a matrix, we obtain a more homogeneous response. This response preserves heights instead of volumes in the parameter space. We briefly touch upon the usefulness of these techniques for organizing the behavior of connectionist networks. Illustrative examples of parameter transform responses are given.

Paper Details

Date Published: 19 February 1988
PDF: 9 pages
Proc. SPIE 0848, Intelligent Robots and Computer Vision VI, (19 February 1988); doi: 10.1117/12.942724
Show Author Affiliations
Andrea Califano, IBM T.J. Watson Research Center (United States)
Ruud M. Bolle, IBM T.J. Watson Research Center (United States)


Published in SPIE Proceedings Vol. 0848:
Intelligent Robots and Computer Vision VI
David P. Casasent; Ernest L. Hall, Editor(s)

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