IP Library Granted Patent US 12,625,091
Granted Patent B2
US 12,625,091 · App. 18/639,217 · Granted May 12, 2026

Systems and methods for automatically generating synthetic x-ray scan data of objects in a plurality of orientations

Inventors: Mark Procter (Wilmslow, GB); James Ollier (Huyton, GB)
Assignee: Rapiscan Holdings, Inc.
G01N23/04G01N2223/639
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,625,091
App. No.
18/639,217
Granted
May 12, 2026
Kind
B2
Abstract

Systems and methods of automatically generating synthetic X-ray scan data include generating scan data corresponding to a frame holding or supporting an object, wherein the frame and hence the object is manipulated, without manual intervention, to be positioned in a plurality of orientations in three dimensional space. Subsequently, X-ray scan data corresponding to the object is isolated and extracted from the X-ray scan data corresponding to the frame, the X-ray scan data corresponding to the object is adjusted and finally each of the adjusted X-ray scan data corresponding to the object is inserted into X-ray scan data of a cargo container in order to generate a plurality of X-ray scan data of the cargo container embedded with the object.

Claims (81)

1 . A system for automatically generating a plurality of X-ray scan data of a cargo container embedded with an object, wherein the object is embedded in a plurality of orientations in three dimensional space within the cargo container, and wherein the three dimensional space is defined by first, second and third mutually orthogonal axes, comprising:

a frame for holding the object;

a base plate for supporting the frame, wherein the frame is positioned in an initial orientation with respect to the first, second and third axes;

a first table for supporting the base plate;

a second table for supporting the first table, wherein the second table is capable of imparting linear motion to the frame, and wherein the first table is capable of imparting rotational motion to the frame around the first axis independent of the second table;

first and second robotic arms and associated cameras configured to locate and rotate the frame around the second and third axes respectively;

an X-ray source for generating an X-ray beam that impinges on the frame and a detector array for capturing resultant X-ray scan data; and

a computing device having a memory and a processor, wherein the computing device controls movements of the first table, second table and the first and second robotic arms, and wherein the memory stores a plurality of programmatic instructions which when executed cause the processor to:

sequentially implement first, second, third, fourth and fifth set of steps in order to generate X-ray scan data corresponding to the frame;

isolate and extract X-ray scan data corresponding to the object from the X-ray scan data corresponding to the frame;

adjust the X-ray scan data corresponding to the object; and

insert each of the adjusted X-ray scan data corresponding to the object into X-ray scan data of the cargo container in order to generate the plurality of X-ray scan data of the cargo container embedded with the object.

2 . The system of claim 1 , wherein the second set of steps is implemented only after completion of the first set of steps, wherein the third set of steps is implemented only after completion of the second set of steps, and wherein the fourth and fifth set of steps are implemented only after completion of the third set of steps.

3 . The system of claim 2 , wherein the first set of steps includes causing the first table to incrementally rotate the frame around the first axis by a predetermined first angle until one full rotation around the first axis is completed, wherein for each unique incremental rotational orientation of the frame around the first axis the second table moves the frame through the X-ray beam in first and second mutually opposing directions in order to generate a pair of scan image data.

4 . The system of claim 3 , wherein the second set of steps includes causing the first robotic arm to incrementally rotate the frame around the second axis by a predetermined second angle until one full rotation around the second axis is completed, wherein for each unique incremental rotational orientation of the frame around the second axis the first set of steps are repeated.

5 . The system of claim 4 , wherein the third set of steps includes causing the second robotic arm to incrementally rotate the frame around the third axis by a predetermined third angle until one full rotation around the third axis is completed, wherein for each unique incremental rotational orientation of the frame around the third axis the first set of steps are repeated.

6 . The system of claim 5 , wherein the fifth set of steps includes causing the first table to incrementally rotate the frame around the second axis by the predetermined second angle until one full rotation around the second axis is completed, wherein for each unique incremental rotational orientation of the frame around the second axis the second table moves the frame through the X-ray beam in first and second mutually opposing directions in order to generate a pair of scan image data.

7 . The system of claim 6 , wherein the fourth set of steps includes causing the second robotic arm to incrementally rotate the frame around the third axis by the predetermined third angle until one full rotation around the third axis is completed, wherein for each unique incremental rotational orientation of the frame around the third axis the fifth set of steps are repeated.

8 . The system of claim 7 , wherein each of the first, second and third angles is the same.

9 . The system of claim 7 , wherein each of the first, second and third angles is 15 degrees.

10 . The system of claim 7 , wherein each of the first, second and third angles ranges from 1 to 90 degrees.

11 . The system of claim 1 , wherein the frame is positioned at a first height of a plurality of predefined heights in order to generate the X-ray scan data corresponding to the frame.

12 . The system of claim 11 , wherein the frame is positioned at a second height of the plurality of predefined heights and the first, second, third, fourth and fifth set of steps are sequentially implemented again in order to generate another set of X-ray scan data corresponding to the frame at the second height.

13 . The system of claim 1 , wherein adjustment of the X-ray scan data corresponding to the object comprises one or more of the introduction of salt and pepper noise to mimic the noise distribution of the X-ray scan data of the cargo container, modulating the intensity level to align with the intensity scaling of the X-ray scan data of the cargo container, dimensional scaling to account for a change in magnification for near and far positions within the X-ray scan data of the cargo container, or ensuring that the X-ray scan data corresponding to the object resides within the boundaries of the cargo container in the X-ray scan data of the cargo container.

14 . The system of claim 1 , wherein a shape of the frame is one of spherical, cubical, regular polygon or a cylindrical tube with or without hemispherical ends.

15 . The system of claim 1 , wherein the frame is made from polystyrene.

16 . The system of claim 1 , wherein each of a plurality of scintillating crystals of the detector array has different vertical and horizontal crystal resolutions.

17 . A system for automatically generating a plurality of X-ray scan data of a cargo container embedded with an object, wherein the object is embedded in a plurality of orientations in three dimensional space within the cargo container, and wherein the three dimensional space is defined by first, second and third mutually orthogonal axes, comprising:

a frame for holding the object;

a base plate for supporting the frame, wherein the frame is positioned in an initial orientation with respect to the first, second and third axes;

a first table for supporting the base plate;

a second table for supporting the first table, wherein the second table is capable of imparting linear motion to the frame, and wherein the first table is capable of imparting rotational motion to the frame around the first axis independent of the second table;

a robotic arm and associated camera configured to locate and rotate the frame around the second axis;

an X-ray source for generating an X-ray beam that impinges on the frame and a detector array for capturing resultant X-ray scan data; and

a computing device having a memory and a processor, wherein the computing device controls movements of the first table, second table and the robotic arm, and wherein the memory stores a plurality of programmatic instructions which when executed cause the processor to:

sequentially implement first and second set of steps in order to generate X-ray scan data corresponding to the frame;

isolate and extract X-ray scan data corresponding to the object from the X-ray scan data corresponding to the frame;

adjust the X-ray scan data corresponding to the object; and

insert each of the adjusted X-ray scan data corresponding to the object into X-ray scan data of the cargo container in order to generate the plurality of X-ray scan data of the cargo container embedded with the object.

18 . The system of claim 17 , wherein the second set of steps is implemented only after completion of the first set of steps.

19 . The system of claim 18 , wherein the first set of steps includes causing the first table to incrementally rotate the frame around the first axis by a predetermined first angle until one full rotation around the first axis is completed, wherein for each unique incremental rotational orientation of the frame around the first axis the second table moves the frame through the X-ray beam in first and second mutually opposing directions in order to generate a pair of scan image data.

20 . The system of claim 19 , wherein the second set of steps includes causing the robotic arm to incrementally rotate the frame around the second axis by a predetermined second angle until one full rotation around the second axis is completed, wherein for each unique incremental rotational orientation of the frame around the second axis the first set of steps are repeated.

21 . The system of claim 20 , wherein adjustment of the X-ray scan data corresponding to the object includes incrementally rotating the X-ray scan data corresponding to the object around the third axis by a predefined third angle.

22 . The system of claim 21 , wherein adjustment of the X-ray scan data corresponding to the object further comprises one or more of the introduction of salt and pepper noise to mimic the noise distribution of the X-ray scan data of the cargo container, modulating the intensity level to align with the intensity scaling of the X-ray scan data of the cargo container, dimensional scaling to account for a change in magnification for near and far positions within the X-ray scan data of the cargo container, or ensuring that the X-ray scan data corresponding to the object resides within the boundaries of the cargo container in the X-ray scan data of the cargo container.

23 . The system of claim 22 , wherein each of the first, second and third angles is the same.

24 . The system of claim 22 , wherein each of the first, second and third angles is 15 degrees.

25 . The system of claim 22 , wherein each of the first, second and third angles ranges from 1 to 90 degrees.

26 . The system of claim 17 , wherein a shape of the frame is one of spherical, cubical, regular polygon or a cylindrical tube with or without hemispherical ends.

27 . The system of claim 17 , wherein the frame is made from polystyrene.

28 . The system of claim 17 , wherein each of a plurality of scintillating crystals of the detector array has similar vertical and horizontal crystal resolutions.

29 . A method for automatically generating a plurality of X-ray scan data of a cargo container embedded with an object, wherein the object is embedded in a plurality of orientations in three dimensional space within the cargo container, wherein the three dimensional space is defined by first, second and third mutually orthogonal axes, wherein the object is held in a frame supported on a base plate, wherein the base plate is supported on a first table, wherein the first table is supported on a second table such that the second table is capable of imparting linear motion to the frame and the first table is capable of imparting rotational motion to the frame around the first axis independent of the second table, and wherein a robotic arm and associated camera is configured to locate and rotate the frame around the second axis, the method comprising:

executing a first set of steps, wherein the first set of steps include causing the first table to incrementally rotate the frame around the first axis by a predetermined first angle until one full rotation around the first axis is completed, and wherein for each unique incremental rotational orientation of the frame around the first axis the second table moves the frame through the X-ray beam in first and second mutually opposing directions in order to generate a pair of scan image data;

executing a second set of steps after completion of the first set of steps, wherein the second set of steps include causing the robotic arm to incrementally rotate the frame around the second axis by a predetermined second angle until one full rotation around the second axis is completed, wherein for each unique incremental rotational orientation of the frame around the second axis the first set of steps are repeated, and wherein execution of the first and second set of steps results in generation of X-ray scan data corresponding to the frame;

isolating and extracting X-ray scan data corresponding to the object from the X-ray scan data corresponding to the frame;

adjusting the X-ray scan data corresponding to the object; and

inserting each of the adjusted X-ray scan data corresponding to the object into X-ray scan data of the cargo container in order to generate the plurality of X-ray scan data of the cargo container embedded with the object.

30 . The method of claim 29 , wherein adjustment of the X-ray scan data corresponding to the object includes incrementally rotating the X-ray scan data corresponding to the object around the third axis by a predefined third angle.

31 . The method of claim 30 , wherein adjustment of the X-ray scan data corresponding to the object further comprises one or more of the introduction of salt and pepper noise to mimic the noise distribution of the X-ray scan data of the cargo container, modulating the intensity level to align with the intensity scaling of the X-ray scan data of the cargo container, dimensional scaling to account for a change in magnification for near and far positions within the X-ray scan data of the cargo container, or ensuring that the X-ray scan data corresponding to the object resides within the boundaries of the cargo container in the X-ray scan data of the cargo container.

32 . The method of claim 29 , wherein each of the first, second and third angles is the same.

33 . The method of claim 29 , wherein each of the first, second and third angles is 15 degrees.

34 . The method of claim 29 , wherein each of the first, second and third angles ranges from 1 to 90 degrees.

35 . The method of claim 29 , wherein a shape of the frame is one of spherical, cubical, regular polygon or a cylindrical tube with or without hemispherical ends.

36 . The method of claim 29 , wherein the frame is made from polystyrene.

37 . The method of claim 29 , wherein each of a plurality of scintillating crystals of the detector array has similar vertical and horizontal crystal resolutions.

38 . A system for automatically generating a plurality of X-ray scan data of a cargo container embedded with an object, wherein the object is embedded in a plurality of orientations in three dimensional space within the cargo container, comprising:

a frame for holding the object;

a base plate for supporting the frame, wherein the frame is positioned in an initial orientation with respect to a vertical axis;

a first table for supporting the base plate;

a second table for supporting the first table, wherein the second table is capable of imparting linear motion to the frame, and wherein the first table is capable of imparting rotational motion to the frame around the vertical axis independent of the second table;

an X-ray source for generating an X-ray beam that impinges on the frame and a detector array for capturing resultant X-ray scan data; and

a computing device having a memory and a processor, wherein the computing device controls movements of the first table and second table, and wherein the memory stores a plurality of programmatic instructions which when executed cause the processor to:

capture X-ray scan data corresponding to the frame by triggering the first table to incrementally rotate the frame around the vertical axis by a predetermined angle until one full rotation around the vertical axis is completed, and wherein for each unique incremental rotational orientation of the frame around the vertical axis the second table moves the frame through the X-ray beam in first and second mutually opposing directions in order to generate a pair of scan image data;

isolate and extract X-ray scan data corresponding to the object from the X-ray scan data corresponding to the frame;

adjust the X-ray scan data corresponding to the object; and

insert each of the adjusted X-ray scan data corresponding to the object into X-ray scan data of the cargo container in order to generate the plurality of X-ray scan data of the cargo container embedded with the object.

39 . The system of claim 38 , wherein the object is a bulk cargo item.

40 . The system of claim 38 , wherein adjustment of the X-ray scan data corresponding to the object comprises one or more of the introduction of salt and pepper noise to mimic the noise distribution of the X-ray scan data of the cargo container, modulating the intensity level to align with the intensity scaling of the X-ray scan data of the cargo container, dimensional scaling to account for a change in magnification for near and far positions within the X-ray scan data of the cargo container, or ensuring that the X-ray scan data corresponding to the object resides within the boundaries of the cargo container in the X-ray scan data of the cargo container.

41 . The system of claim 38 , wherein the predefined angle is 15 degrees.

42 . The system of claim 38 , wherein the predefined angles range from 1 to 90 degrees.

43 . The system of claim 38 , wherein a shape of the frame is one of spherical, cubical, regular polygon or a cylindrical tube with or without hemispherical ends.

44 . The system of claim 38 , wherein the frame is made from polystyrene.

Assignments (2)
NOTICE OF GRANT OF SECURITY INTEREST IN PATENTS Recorded Jul 1, 2025
From: RAPISCAN HOLDINGS, INC.
To: WELLS FARGO BANK, NATIONAL ASSOCIATION
Reel/Frame 071823/0713 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 14, 2024
From: PROCTER, MARK; OLLIER, JAMES
To: RAPISCAN HOLDINGS, INC.
Reel/Frame 067400/0205 →
Continuity (2)
Provisional Application 63505670 · Jun 1, 2023
Related Publication 20240402097A1 · Dec 5, 2024
References Cited (219)
US 5128365A · Spector · 1992 [cited by applicant]
US 5600303A · Husseiny · 1997 [cited by applicant]
US 5806521A · Morimoto · 1998 [cited by examiner]
US 5838759A · Armistead · 1998 [cited by examiner]
US 5881122A · Crawford · 1999 [cited by examiner]
US 6028955A · Cohen · 2000 [cited by examiner]
US 6128365A · Bechwati · 2000 [cited by applicant]
US 6200024B1 · Negrelli · 2001 [cited by examiner]
US 6345113B1 · Crawford · 2002 [cited by examiner]
US 6418189B1 · Schafer · 2002 [cited by examiner]
US H2110H · Newman · 2004 [cited by applicant]
US 6813374B1 · Karimi · 2004 [cited by examiner]
US 6825854B1 · Beneke · 2004 [cited by applicant]
US 7062011B1 · Tybinkowski · 2006 [cited by examiner]
US 7193515B1 · Roberts · 2007 [cited by applicant]
US 7277577B2 · Ying · 2007 [cited by applicant]
US 7702068B2 · Scheinman · 2010 [cited by applicant]
US 7748900B2 · Maschke · 2010 [cited by examiner]
US 8009864B2 · Linaker · 2011 [cited by applicant]
US 8014493B2 · Roux · 2011 [cited by applicant]
US 8352073B2 · Berti · 2013 [cited by examiner]
US 8494210B2 · Gudmundson · 2013 [cited by applicant]
US 8633823B2 · Armistead, Jr. · 2014 [cited by applicant]
US 8693731B2 · Holz · 2014 [cited by applicant]
US 8875226B1 · Marek · 2014 [cited by applicant]
US 9042511B2 · Peschmann · 2015 [cited by applicant]
US 9042661B2 · Pavlovich · 2015 [cited by applicant]
US 9170212B2 · Bouchard · 2015 [cited by applicant]
US 9240046B2 · Carrell · 2016 [cited by applicant]
US 9911282B2 · Alewine · 2018 [cited by applicant]
US 9996890B1 · Cinnamon · 2018 [cited by applicant]
US 10282902B1 · Mishra · 2019 [cited by examiner]
US 10473811B1 · Bairashewski · 2019 [cited by examiner]
US 10817722B1 · Raguin · 2020 [cited by applicant]
US 10860836B1 · Tyagi · 2020 [cited by examiner]
US 20020159627A1 · Schneiderman · 2002 [cited by applicant]
US 20020186862A1 · McClelland · 2002 [cited by applicant]
US 20040066966A1 · Schneiderman · 2004 [cited by applicant]
US 20040124982A1 · Kovach · 2004 [cited by applicant]
US 20040263379A1 · Keller · 2004 [cited by applicant]
US 20050117700A1 · Peschmann · 2005 [cited by applicant]
US 20050157931A1 · Delashmit, Jr. · 2005 [cited by examiner]
US 20060023835A1 · Seppi · 2006 [cited by examiner]
US 20060070453A1 · Werve · 2006 [cited by examiner]
US 20060088207A1 · Schneiderman · 2006 [cited by applicant]
US 20060197523A1 · Palecki · 2006 [cited by applicant]
US 20070076841A1 · Varadharajan · 2007 [cited by examiner]
US 20070112556A1 · Lavi · 2007 [cited by applicant]
US 20070115123A1 · Roberts · 2007 [cited by applicant]
US 20070121783A1 · Ellenbogen · 2007 [cited by applicant]
US 20070140412A1 · Holt · 2007 [cited by examiner]
US 20070150745A1 · Peirce · 2007 [cited by applicant]
US 20070235652A1 · Smith · 2007 [cited by applicant]
US 20080008353A1 · Park · 2008 [cited by applicant]
US 20080056444A1 · Skatter · 2008 [cited by applicant]
US 20080063140A1 · Awad · 2008 [cited by applicant]
US 20080170655A1 · Bendahan · 2008 [cited by examiner]
US 20080170660A1 · Gudmundson · 2008 [cited by applicant]
US 20080240578A1 · Gudmundson · 2008 [cited by applicant]
US 20080270462A1 · Thomsen · 2008 [cited by applicant]
US 20080283761A1 · Robinson · 2008 [cited by applicant]
US 20080298545A1 · Bueno · 2008 [cited by examiner]
US 20080298546A1 · Bueno · 2008 [cited by examiner]
US 20080309484A1 · Francis · 2008 [cited by applicant]
US 20090005668A1 · West · 2009 [cited by examiner]
US 20090012383A1 · Virtue · 2009 [cited by examiner]
US 20090067575A1 · Seppi · 2009 [cited by examiner]
US 20090180678A1 · Kuduvalli · 2009 [cited by examiner]
US 20090296880A1 · Beets · 2009 [cited by examiner]
US 20090316853A1 · Parazzoli · 2009 [cited by examiner]
US 20100046704A1 · Song · 2010 [cited by applicant]
US 20100098316A1 · Papaioannou · 2010 [cited by examiner]
US 20100166322A1 · Madruga · 2010 [cited by applicant]
US 20100303287A1 · Morton · 2010 [cited by examiner]
US 20100329532A1 · Masuda · 2010 [cited by examiner]
US 20110033024A1 · Dafni · 2011 [cited by examiner]
US 20110077523A1 · Angott · 2011 [cited by examiner]
US 20110186739A1 · Foland · 2011 [cited by examiner]
US 20110206179A1 · Bendahan · 2011 [cited by examiner]
US 20110206240A1 · Hong · 2011 [cited by applicant]
US 20110255663A1 · Roy · 2011 [cited by examiner]
US 20110268247A1 · Shedlock · 2011 [cited by examiner]
US 20120076257A1 · Star-Lack · 2012 [cited by examiner]
US 20120093288A1 · Mastronardi · 2012 [cited by examiner]
US 20120293633A1 · Yamato · 2012 [cited by applicant]
US 20120304085A1 · Kim · 2012 [cited by applicant]
US 20130086674A1 · Horvitz · 2013 [cited by applicant]
US 20130126299A1 · Schoepe · 2013 [cited by applicant]
US 20130156156A1 · Roe · 2013 [cited by examiner]
US 20130163811A1 · Oelke · 2013 [cited by applicant]
US 20130208124A1 · Boghossian · 2013 [cited by applicant]
US 20130211419A1 · Jensen · 2013 [cited by examiner]
US 20130211420A1 · Jensen · 2013 [cited by examiner]
US 20130215114A1 · Cherkashin · 2013 [cited by examiner]
US 20130215264A1 · Soatto · 2013 [cited by applicant]
US 20130294574A1 · Peschmann · 2013 [cited by applicant]
US 20130322742A1 · Walton · 2013 [cited by applicant]
US 20140054465A1 · Berke · 2014 [cited by examiner]
US 20140119604A1 · Mai · 2014 [cited by applicant]
US 20140198899A1 · Ziskin · 2014 [cited by applicant]
US 20140233692A1 · Case · 2014 [cited by examiner]
US 20140270059A1 · Suppes · 2014 [cited by examiner]
US 20140270536A1 · Amtrup · 2014 [cited by applicant]
US 20140294147A1 · Chen · 2014 [cited by examiner]
US 20140321710A1 · Robert · 2014 [cited by examiner]
US 20140344533A1 · Liu · 2014 [cited by applicant]
US 20150117603A1 · Keeve · 2015 [cited by examiner]
US 20150168570A1 · Pelc · 2015 [cited by examiner]
US 20150238159A1 · Al Assad · 2015 [cited by examiner]
US 20150325010A1 · Bedford · 2015 [cited by examiner]
US 20160098620A1 · Geile · 2016 [cited by applicant]
US 20160117898A1 · Kuznetsov · 2016 [cited by applicant]
US 20160183899A1 · Vancamberg · 2016 [cited by examiner]
US 20160189509A1 · Malhotra · 2016 [cited by applicant]
US 20160199666A1 · Maurer · 2016 [cited by examiner]
US 20160216398A1 · Bendahan · 2016 [cited by applicant]
US 20160223706A1 · Franco · 2016 [cited by examiner]
US 20160232689A1 · Morton · 2016 [cited by applicant]
US 20160249869A1 · Papalazarou · 2016 [cited by examiner]
US 20160302871A1 · Gregerson · 2016 [cited by examiner]
US 20160356915A1 · Green · 2016 [cited by applicant]
US 20170061625A1 · Estrada · 2017 [cited by applicant]
US 20170083792A1 · Rodríguez-Serrano · 2017 [cited by applicant]
US 20170116511A1 · Kim · 2017 [cited by applicant]
US 20170209111A1 · Choi · 2017 [cited by examiner]
US 20170236232A1 · Morton · 2017 [cited by applicant]
US 20170263019A1 · Song · 2017 [cited by applicant]
US 20170278300A1 · Hurter · 2017 [cited by applicant]
US 20170296137A1 · West · 2017 [cited by examiner]
US 20170316285A1 · Ahmed · 2017 [cited by applicant]
US 20170319160A1 · Lu · 2017 [cited by examiner]
US 20170350834A1 · Prado · 2017 [cited by applicant]
US 20180055467A1 · Cox · 2018 [cited by examiner]
US 20180060722A1 · Hwang · 2018 [cited by applicant]
US 20180061090A1 · Jerebko · 2018 [cited by examiner]
US 20180089816A1 · Potter · 2018 [cited by applicant]
US 20180150713A1 · Farooqi · 2018 [cited by applicant]
US 20180214112A1 · Graziani · 2018 [cited by examiner]
US 20180224569A1 · Paresi · 2018 [cited by examiner]
US 20180289982A1 · Sayeh · 2018 [cited by examiner]
US 20180293734A1 · Lim · 2018 [cited by applicant]
US 20180338742A1 · Singh · 2018 [cited by examiner]
US 20180351634A1 · Ryan · 2018 [cited by applicant]
US 20190041341A1 · Paresi · 2019 [cited by examiner]
US 20190200945A1 · Tsuyuki · 2019 [cited by examiner]
US 20190231292A1 · Mertelmeier · 2019 [cited by examiner]
US 20200003703A1 · Zavagno · 2020 [cited by examiner]
US 20200073009A1 · Parikh · 2020 [cited by examiner]
US 20200107794A1 · Mandelkern · 2020 [cited by examiner]
US 20200147869A1 · Muir · 2020 [cited by examiner]
US 20200160598A1 · Manivasagam · 2020 [cited by examiner]
US 20200170598A1 · Shea · 2020 [cited by examiner]
US 20200320722A1 · Jordan · 2020 [cited by examiner]
US 20200363815A1 · Mousavian · 2020 [cited by examiner]
US 20210004589A1 · Turkelson · 2021 [cited by applicant]
US 20210031052A1 · Jordan · 2021 [cited by examiner]
US 20210158561A1 · Park · 2021 [cited by examiner]
US 20210201476A1 · Prasad · 2021 [cited by examiner]
US 20210225088A1 · Sivakumar · 2021 [cited by examiner]
US 20210350532A1 · Kimmel · 2021 [cited by examiner]
US 20220051422A1 · Parian · 2022 [cited by examiner]
US 20220138952A1 · Crawford · 2022 [cited by examiner]
US 20220155440A1 · Kruse · 2022 [cited by examiner]
US 20220225957A1 · Kelly · 2022 [cited by examiner]
US 20220262072A1 · Manivasagam · 2022 [cited by examiner]
US 20220305290A1 · Jordan · 2022 [cited by examiner]
US 20220390391A1 · Bendahan · 2022 [cited by examiner]
US 20220398717A1 · Hébert · 2022 [cited by examiner]
US 20230011644A1 · Zhao · 2023 [cited by examiner]
US 20230147681A1 · Dinca · 2023 [cited by examiner]
US 20230154166A1 · Pauli · 2023 [cited by examiner]
US 20230293131A1 · Bindley · 2023 [cited by examiner]
US 20230355194A1 · Gregerson · 2023 [cited by examiner]
US 20230397957A1 · Crawford · 2023 [cited by examiner]
US 20240016549A1 · Johnson · 2024 [cited by examiner]
US 20240020862A1 · Johnson · 2024 [cited by examiner]
US 20240164730A1 · Mc Carthy · 2024 [cited by examiner]
US 20240245363A1 · Mason · 2024 [cited by examiner]
US 20240264331A1 · Zoboyan · 2024 [cited by examiner]
US 20240394886A1 · Deshpande · 2024 [cited by examiner]
US 20250061620A1 · Salehi · 2025 [cited by examiner]
US 20250078480A1 · Pauli · 2025 [cited by examiner]
CA 2651131 · 2007 [cited by applicant]
CA 2796809 · 2011 [cited by applicant]
CN 104240784 · 2014 [cited by applicant]
JP S643997 · 1989 [cited by applicant]
JP H03103997 · 1991 [cited by applicant]
JP H04103997 · 1992 [cited by applicant]
JP 3103997A · 1992 [cited by applicant]
JP 2010520542A · 2010 [cited by applicant]
JP 2017062781A · 2017 [cited by applicant]
WO 2006119603 · 2006 [cited by applicant]
WO 2008107112 · 2008 [cited by applicant]
WO 2010050952 · 2010 [cited by applicant]
International Search Report for PCT/US24/25152, Aug. 12, 2024. [cited by applicant]
Akcay, et al., “Transfer Learning Using Convolutional Neural Networks for Object Classification Within X-Ray Baggage Security Imagery,” IEE International Conference on Image Processing (ICIP), Sep. 25, 2016, pp. 1057-10… [cited by applicant]
Girshick, Ross (“Fast R-CNN,” IEEE International Conference on Computer Vision, Dec. 7-13, 2015) (Year: 2015). [cited by applicant]
Salvador et al. (“Faster R-CNN Features for Instance Search,” IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Jun. 26-Jul. 1, 2016) (Year: 2016). [cited by applicant]
He et al. “Mask R-CNN,” Facebook AI Research (FAIR) Apr. 5, 2017, 10 pages. [cited by applicant]
He et al. “Deep Residual Learning for Image Recognition,” Microsoft Research Dec. 10, 2015, 12 pages. [cited by applicant]
Simonyan et al. “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Visual Geometry Group, Department of Engineering Science, University of Oxford, Apr. 10, 2015, 14 pages. [cited by applicant]
Ren et al. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” Jan. 6, 2016, 14 pages. [cited by applicant]
Steitz et al. “Multi-view X-ray R-CNN,” Department of Computer Science, TU Darmstadt, Darmstadt, Germany, Oct. 4, 2018, 16 pages. [cited by applicant]
Girshick et al. “Rich feature hierarchies for accurate object detection and semantic segmentation,” Tech report (v5), UC Berkeley, Oct. 22, 2014, 21 pages. [cited by applicant]
Girshick, Ross “Fast R-CNN,” Microsoft Research, Sep. 27, 2015, 9 pages. [cited by applicant]
Liu et al. “SSD: Single Shot MultiBox Detector,” Dec. 29, 2016, 17 pages. [cited by applicant]
Lin et al. “Focal Loss for Dense Object Detection,” Facebook AI Research (FAIR), Feb. 7, 2018, 10 pages. [cited by applicant]
Lin et al. “Microsoft COCO: Common Objects in Context,” Feb. 21, 2015, 15 pages. [cited by applicant]
Lin et al. “Feature Pyramid Networks for Object Detection,” Facebook AI Research (FAIR), Cornell University and Cornell Tech, Apr. 19, 2017, 10 pages. [cited by applicant]
Lin et al. “Cross-View Image Geolocalization,” University of California, Brown University, 8 pages, 2013. [cited by applicant]
Krizhevsky et al. “ImageNet Classification with Deep Convolutional Neural Networks,” 9 pages, /2012/. [cited by applicant]
Redmon et al. “You Only Look Once: Unified, Real-Time Object Detection,” 10 pages, /2016/. [cited by applicant]
Isola et al. “Image-to-Image Translation with Conditional Adversarial Networks.” Berkeley AI Research (BAIR) Laboratory, University of California, Berkeley, Nov. 21, 2016, 16 pages. [cited by applicant]
Kalogerakis et al. “A Probabilistic Model for Component-Based Shape Synthesis.” Standford University, ACM Transactions on Graphics 31:4, Jul. 2012, 13 pages. [cited by applicant]
Lin et al. “Feature Pyramid Networks for Object Detection,” Facebook AI Research (FAIR), Cornell University and Cornell Tech, Jan. 6, 2016, 10 pages. [cited by applicant]
Reed et al., “Generative Adversarial Text to Image Synthesis”, Proc. of ICML, 2016. [cited by applicant]
Shaoqing, et al., “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, No. 6, Jun. 1, 2017, pp. 1137-1149. [cited by applicant]
Nguyen et al., “Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images” (Year: 2015). [cited by applicant]
Bousmalis et al., “Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks”, CVPR, Jul. 2017 (Year: 2017). [cited by applicant]