The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 06, 2023

Filed:

Jul. 14, 2020
Applicant:

Lawrence Livermore National Security, Llc, Livermore, CA (US);

Inventors:

David W. Paglieroni, Pleasanton, CA (US);

Harry E. Martz, Jr., Livermore, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 15/08 (2011.01); G06K 9/62 (2022.01); G06T 7/00 (2017.01); G06T 15/00 (2011.01); G06V 10/25 (2022.01); G01V 5/00 (2006.01);
U.S. Cl.
CPC ...
G06T 15/08 (2013.01); G06K 9/6256 (2013.01); G06K 9/6267 (2013.01); G06T 7/0002 (2013.01); G06T 15/005 (2013.01); G06T 2207/10081 (2013.01);
Abstract

An automatic threat recognition system and method is disclosed for scanning the x-ray CT image of an article to identify the objects of interest (OOIs) contained within the article, which are otherwise not always quickly apparent or discernable to an individual. The system uses a computer to receive information from two-dimensional (2D) image slices from a reconstructed computed tomography (CT) scan image and to produce a plurality of voxels for each slice of the 2D image. The computer analyzes the voxels to create a likelihood map (LM) representing likelihoods that voxels making up the CT image are associated with a material of interest (MOI). The computer further analyzes the LM to construct neighborhoods of voxels within the LM, and classifies each voxel neighborhood based on its features, thereby decluttering the LM to facilitate the process of connecting voxels of a like MOI together to form segments. The computer classifies each candidate segment based on its features, thereby identifying those segments that correspond to objects of interest.


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