Automatic threat recognition system and method using material disambiguation informed by physics in x-ray CT images of baggage
Abstract
An automatic threat recognition (ATR) system is disclosed for scanning an article to recognize contraband items or items of interest contained within the article. The ATR system uses a CAT scanner to obtain a CT image scan of objects within the article, representing a plurality of 2D image slices of the article and its contents. Each 2D image slice includes information forming a plurality of voxels. The ATR system includes a computer and determines which voxels have a likelihood of representing materials of interest. It then aggregates those voxels to produce detected objects. The detected objects are further classified as items of interest vs. not of interest. The ATR system is based on learned parameters for a novel interaction of global and object context mechanisms. ATR system performance may be optimized by using jointly optimal global and object context parameters learned during training. The global context parameters may apply to the article as a whole and facilitate object detection. The object context parameters may apply to the individual object detections.
- Inventors:
- Issue Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1924946
- Patent Number(s):
- 11403765
- Application Number:
- 16/540,610
- Assignee:
- Lawrence Livermore National Security, LLC (Livermore, CA)
- Patent Classifications (CPCs):
-
G - PHYSICS G01 - MEASURING G01V - GEOPHYSICS
G - PHYSICS G06 - COMPUTING G06T - IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- DOE Contract Number:
- AC52-07NA27344
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 08/14/2019
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Paglieroni, David W., Pechard, Christian T., and Martz, Jr., Harry E. Automatic threat recognition system and method using material disambiguation informed by physics in x-ray CT images of baggage. United States: N. p., 2022.
Web.
Paglieroni, David W., Pechard, Christian T., & Martz, Jr., Harry E. Automatic threat recognition system and method using material disambiguation informed by physics in x-ray CT images of baggage. United States.
Paglieroni, David W., Pechard, Christian T., and Martz, Jr., Harry E. Tue .
"Automatic threat recognition system and method using material disambiguation informed by physics in x-ray CT images of baggage". United States. https://www.osti.gov/servlets/purl/1924946.
@article{osti_1924946,
title = {Automatic threat recognition system and method using material disambiguation informed by physics in x-ray CT images of baggage},
author = {Paglieroni, David W. and Pechard, Christian T. and Martz, Jr., Harry E.},
abstractNote = {An automatic threat recognition (ATR) system is disclosed for scanning an article to recognize contraband items or items of interest contained within the article. The ATR system uses a CAT scanner to obtain a CT image scan of objects within the article, representing a plurality of 2D image slices of the article and its contents. Each 2D image slice includes information forming a plurality of voxels. The ATR system includes a computer and determines which voxels have a likelihood of representing materials of interest. It then aggregates those voxels to produce detected objects. The detected objects are further classified as items of interest vs. not of interest. The ATR system is based on learned parameters for a novel interaction of global and object context mechanisms. ATR system performance may be optimized by using jointly optimal global and object context parameters learned during training. The global context parameters may apply to the article as a whole and facilitate object detection. The object context parameters may apply to the individual object detections.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Aug 02 00:00:00 EDT 2022},
month = {Tue Aug 02 00:00:00 EDT 2022}
}
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