Automated feature detection and identification in digital point-ordered signals
Abstract
A computer-based automated method to detect and identify features in digital point-ordered signals. The method is used for processing of non-destructive test signals, such as eddy current signals obtained from calibration standards. The signals are first automatically processed to remove noise and to determine a baseline. Next, features are detected in the signals using mathematical morphology filters. Finally, verification of the features is made using an expert system of pattern recognition methods and geometric criteria. The method has the advantage that standard features can be, located without prior knowledge of the number or sequence of the features. Further advantages are that standard features can be differentiated from irrelevant signal features such as noise, and detected features are automatically verified by parameters extracted from the signals. The method proceeds fully automatically without initial operator set-up and without subjective operator feature judgement.
- Inventors:
-
- Burnt Hills, NY
- Clifton Park, NY
- Albany, NY
- Schenectady, NY
- Issue Date:
- Research Org.:
- Knolls Atomic Power Laboratory (KAPL), Niskayuna, NY (United States)
- OSTI Identifier:
- 871465
- Patent Number(s):
- 5737445
- Assignee:
- United States of America as represented by United States (Washington, DC)
- Patent Classifications (CPCs):
-
G - PHYSICS G01 - MEASURING G01N - INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- DOE Contract Number:
- AC12-76SN00052
- Resource Type:
- Patent
- Country of Publication:
- United States
- Language:
- English
- Subject:
- automated; feature; detection; identification; digital; point-ordered; signals; computer-based; method; detect; identify; features; processing; non-destructive; eddy; current; obtained; calibration; standards; automatically; processed; remove; noise; determine; baseline; detected; mathematical; morphology; filters; finally; verification; expert; pattern; recognition; methods; geometric; criteria; advantage; standard; located; prior; knowledge; sequence; advantages; differentiated; irrelevant; signal; verified; parameters; extracted; proceeds; initial; operator; set-up; subjective; judgement; digital point-ordered; signal features; automated method; current signals; pattern recognition; eddy current; current signal; automatically processed; point-ordered signal; point-ordered signals; feature detection; calibration standard; /382/324/702/
Citation Formats
Oppenlander, Jane E, Loomis, Kent C, Brudnoy, David M, and Levy, Arthur J. Automated feature detection and identification in digital point-ordered signals. United States: N. p., 1998.
Web.
Oppenlander, Jane E, Loomis, Kent C, Brudnoy, David M, & Levy, Arthur J. Automated feature detection and identification in digital point-ordered signals. United States.
Oppenlander, Jane E, Loomis, Kent C, Brudnoy, David M, and Levy, Arthur J. Thu .
"Automated feature detection and identification in digital point-ordered signals". United States. https://www.osti.gov/servlets/purl/871465.
@article{osti_871465,
title = {Automated feature detection and identification in digital point-ordered signals},
author = {Oppenlander, Jane E and Loomis, Kent C and Brudnoy, David M and Levy, Arthur J},
abstractNote = {A computer-based automated method to detect and identify features in digital point-ordered signals. The method is used for processing of non-destructive test signals, such as eddy current signals obtained from calibration standards. The signals are first automatically processed to remove noise and to determine a baseline. Next, features are detected in the signals using mathematical morphology filters. Finally, verification of the features is made using an expert system of pattern recognition methods and geometric criteria. The method has the advantage that standard features can be, located without prior knowledge of the number or sequence of the features. Further advantages are that standard features can be differentiated from irrelevant signal features such as noise, and detected features are automatically verified by parameters extracted from the signals. The method proceeds fully automatically without initial operator set-up and without subjective operator feature judgement.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 1998},
month = {Thu Jan 01 00:00:00 EST 1998}
}