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20 October 1997 Histogram methods for scientific curve classification
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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.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James R. Parker "Histogram methods for scientific curve classification", Proc. SPIE 3168, Vision Geometry VI, (20 October 1997); https://doi.org/10.1117/12.279678
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