Histogram methods for scientific curve classification

James R. Parker

doi:10.1117/12.279678

20 October 1997 Histogram methods for scientific curve classification

James R. Parker

Proceedings Volume 3168, Vision Geometry VI; (1997) https://doi.org/10.1117/12.279678
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available