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Proceedings Paper

Histogram methods for scientific curve classification
Author(s): James R. Parker
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Paper Abstract

Scientific data is frequently classified by using a presumed underlying model. A best fit approach can produce a set of residual values, and the minimum residual gives the classification. What is suggested here is a more visual approach - a characterization of the shape of the input curve, and a comparison against the shapes of the model histograms to collect gross shape information of various types. The example under consideration is that of respirogram curves, data collected from wastewater treatment plants, but the method applies to many other data acquisition processes.

Paper Details

Date Published: 20 October 1997
PDF: 6 pages
Proc. SPIE 3168, Vision Geometry VI, (20 October 1997); doi: 10.1117/12.279678
Show Author Affiliations
James R. Parker, Univ. of Calgary (Canada)

Published in SPIE Proceedings Vol. 3168:
Vision Geometry VI
Robert A. Melter; Angela Y. Wu; Longin Jan Latecki, Editor(s)

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