Multi-scanner networked systems for performing material discrimination processes on scanned objects

Information

  • Patent Grant
  • 11768313
  • Patent Number
    11,768,313
  • Date Filed
    Thursday, February 3, 2022
    2 years ago
  • Date Issued
    Tuesday, September 26, 2023
    8 months ago
Abstract
The present application is directed toward cargo scanning systems having scanners, each arranged to scan a respective object and generate a set of scan data, processors arranged to process each set of scan data to determine whether it meets a predetermined threat condition, workstations, and data management system arranged to direct data that meets the threat condition to one of the workstations for analysis.
Description
FIELD OF THE INVENTION

The present invention relates to scanning systems. It has particular application in scanning systems for cargo.


BACKGROUND

There is a requirement to be able to screen cargo items for the presence of illicit materials and devices for the protection of the public.


Currently, such inspection may be undertaken using X-ray based screening apparatus. In these systems, an X-ray image of the object under inspection is taken and an operator reviews this image to resolve, in their experience, whether the cargo is clear for onwards travel or whether the cargo requires a further level of inspection. However greater volumes of cargo traffic and greater desire and need for security scanning have lead to an increasing need to increase the throughput of scanning systems.


SUMMARY OF THE INVENTION

The present invention provides a cargo scanning system comprising a plurality of scanners each arranged to scan a respective object and generate a set of scan data, processing means arranged to process each set of scan data to determine whether it meets a predetermined threat condition, and data management means arranged to direct data that meets the threat condition to a workstation, or one of a plurality of workstations, for analysis.


The present application is directed toward cargo scanning systems having scanners, each arranged to scan a respective object and generate a set of scan data, processors arranged to process each set of scan data to determine whether it meets a predetermined threat condition, workstations, and data management system arranged to direct data that meets the threat condition to one of the workstations for analysis.


The data management means may comprise a job dispatcher. The job dispatcher may be arranged to coordinate the tasks which are directed to each of the workstations. The data management means may further comprise a threat detection processor, which may be arranged to process image data to allocate the data to a threat category automatically, for example using one or more image processing algorithms. The data management means may also comprise a threat injector, which may be arranged to input test image data defining an image of a threat item. These different functions of the data management system can be provided as separate processors, or can be provided as different functions of a single processor.


The system may further comprise a cargo movement control means arranged to control movement of the objects through the scanners. Where the system is arranged to scan cargo carried on road-going vehicles the movement control means may include traffic lights and other signs and indicators for the driver of the vehicle. Where the system is arranged to scan rail cargo, the movement control means may include points on the railway. Where the system is arranged to scan cargo on a conveyor, the movement control means can include the conveyor.


The system may further comprise a holding bay and the movement control means may be arranged to hold one of the objects in the holding bay in response to the object meeting the threat condition. The movement control means may be arranged to cause the object to bypass the holding bay if it does not meet the threat condition.


According to some embodiments of the invention, a multi-level inspection process is provided which seeks to automate the scanning process to allow higher throughput and lower screening cost per cargo item.


The present invention further provides a method of scanning cargo comprising providing a plurality of scanners, scanning a respective object with each of the scanners to generate a respective set of scan data, processing each set of scan data to determine whether it meets a predetermined threat condition, and directing data that meets the threat condition to a workstation for analysis.


Preferred embodiments of the present invention will now be described by way of example only with reference to the accompanying drawings.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 is a schematic diagram of a scanning system according to an embodiment of the invention;



FIG. 2 is a schematic view of part of the scanning system of FIG. 1;



FIG. 3 is a schematic plan view of a scanning system according to a further embodiment of the invention;



FIG. 4 is a schematic diagram of a threat detection system forming part of a scanning system according to a further embodiment of the invention; and;



FIG. 5 is a schematic diagram of a cargo security system according to a further embodiment of the invention.





DETAILED DESCRIPTION OF THE INVENTION

Referring to FIG. 1, a scanning system according to one embodiment of the invention comprises a number of scanners 10, which can be for example static, moving gantry or mobile scanners, each of which is arranged to scan a cargo container to generate image data. In this case the scanners 10 are arranged over a roadway 11 so that they can scan road-going cargo trucks. A storage array 12, threat detection processor 14 and job dispatcher 16, which generally includes a computer with a processor, are all connected to the scanners 10 and to each other by a data switch 18 or other suitable data transmission system. The data switch is also connected to a network of workstations 20. Each of the workstations 20 includes a display 22 arranged to display the image data in the form of an image for viewing by an operator, and a user input 24, in this case in the form of a mouse, which enables the operator to allocate one of a number of threat categories to each image.


The scanners 10 are able to operate independently and at high throughput. A typical scanner comprises an X-ray generator 30, a set of X-ray detector arrays 32, 34 each comprising a number of individual detectors 36 each arranged to generate an output signal. The scanner may be a drive-through scanner, or it may include means, such as a movable gantry, to scan the cargo item through an X-ray beam which fires from the X-ray generator 30 through the cargo item and onto the set of X-ray detectors 36. A two-dimensional image data set is formed by the scanner from the detector output signals. That data set contains information about the cargo item under inspection. In some embodiments more than one X-ray beam is used. In this case the beams may be used to generate two-dimensional image data sets, or three dimensional image data sets. In either case the image data from a series of scans is typically in a form that can be used to build up a three-dimensional image of the cargo item. The scanners 10 pass the image information through the data switch 18 which is able to route the information directly from the scanners 10 to the other nodes 12, 14, 16, 20. Typically, a scan will generate data in the form of Ethernet packets and the data switch 18 is therefore simply an Ethernet switch.


In the embodiment described here, data from the scanners 10 is passed directly to the central storage array 12 and the job dispatcher node 16 which is therefore arranged to receive from the generating scanner 10 the new cargo image data.


The job dispatcher 16 is then arranged, on receipt of any new image data set, to allocate time on the threat detection processor 14 for automated analysis of the new image data. Advantageously, the image data produced by the scanner 10 will have multi-energy attributes such that a detailed materials discrimination algorithm can be executed first by the threat detection processor 14, followed by an automated detection algorithm. Once the threat detection processor has analysed the image data produced by the scanner 10, it is arranged to notify the job dispatcher 16 of its conclusions.


If a threat item (e.g. a material or device) has been detected by the threat detection processor 14, the job dispatcher 16 is arranged to allocate an operator to review the image data produced by the scanner to resolve the severity of the threat item(s) that were detected by the threat detection processor 14, and to transmit the image data to one of the workstations 20, or simply make the data available for retrieval and analysis by the operator. The operator will utilise one of the networked operator workstations 20 that has the capability to manipulate the image data for optimal display.


Once the operator has made their decision, and input it as an operator decision input to the workstation using the input device 24, the result (either that the cargo is in fact clear for onwards travel or that it does indeed contain threat materials or devices) is forwarded to the job dispatcher 16 by the operator workstation. This can be done by sending the image data back with the decision attached to it in the form of a threat categorization, or by sending the decision, again for example as a threat categorization, with an identifier which uniquely identifies the image data set. The job dispatcher 16 is then arranged to notify the scanner 10 of the result.


In the event that a cargo item is flagged or categorized by the operator at the workstation 20 as containing a threat material or device, the facility manager is also notified, and a traffic management system controlled as described in more detail below to direct the cargo items appropriately, such that the threat cargo item can be quarantined until such time as an operative is available for manual search of the cargo item.


Typically, the threat detection processor 14 can be optimised to deliver a low false alarm rate to minimise the congestion and process delays that are caused when a threat cargo item is detected. The corollary of this is that the true detection rate will also be low. In this situation, very few operators are required in order to inspect image data from large numbers of scanning devices. This ensures a low screening cost per cargo item.


In this low false alarm rate scenario, it is reasonable to send a fraction of all the scanned images to the network of operators using random scheduling of cargo items which were cleared by the threat detection processor 14. This ensures that good inspection coverage of all the cargo items that are passing through the facility is achieved.


In a further mode of operation of the system, the balance between false alarm rate and detection probability is adjusted such that a higher detection rate is achieved but with a consequent increase in false alarm rate. In this scenario, more operators will be required in order to confirm or reject the cargo items following automated threat detection processing. At this higher false alarm rate level, it is unlikely that additional random inspection of automatically cleared containers will be required. The use of more operators pushes up the cost of screening containers but this comes at the benefit of an enhanced detection probability.


The threat detection processor 14 can be set to any particular sensitivity to suit the environment in which the system is to be used. However in this embodiment the sensitivity of the threat detection processor 14 is adjustable so that the operation of the system can be adjusted to suit the prevailing conditions. This means that where the threat detection processor is arranged to allocate each item to one of a number of threat categories, corresponding to different levels of threat, the category to which any particular images will be allocated can be adjusted so as to adjust the proportion of items that will be allocated to each of the categories. The threat detection processor can be arranged to adjust this allocation on the basis of one or more inputs, for example inputs indicative of an overall threat level, the volume of traffic which needs to be scanned, or the number of operators available to review the images. In a modification to this arrangement, the threat detection processor 14 can be arranged to allocate the items in the same way at all times, and the job dispatcher 16 can be made adjustable so that it allocates jobs to the workstations, and controls the flow of traffic in a way which is variable and adjustable in response to the same variables.


In a further embodiment of this invention, a further network node is added in the form of a threat injector 40. The threat injector node 40 comprises a computer 42 having a processor 44 and memory 46, with a library, stored in the memory 46, of images of threat items that have been collected under controlled conditions using scanners identical to those 10 in use in the installation. Using a scheduling algorithm that is controlled by the job dispatcher 16, image data that has been cleared by the threat detection processor 14 is passed to the threat injector 40. The threat injector 40 superimposes a threat object image from its library of stored images into the true cargo image in order to create a hybrid image that now contains a known threat in an otherwise clear image.


This hybrid image is then dispatched by the job dispatcher 16 to one of the workstations 20 for an operator review. The operator will be expected to find and mark the threat object. When the operator threat categorization decision is input at the workstation 20 and returned to the job dispatcher 16, the job dispatcher will send a notification to the workstation 20 to notify the operator that a known threat had been inserted into the image and will confirm whether the operator located the threat correctly. This information is then stored in a database of records, as part of one of the records which is relevant to the particular operator, in order to build up a picture of the individual operator's performance standard.


In a practical realisation of this invention, each workstation 20 can be arranged to display to an operator approximately 10% hybrid threat images, and 90% pure scanned images, in order to keep them occupied and well trained. The nature and complexity of the threat images that are injected are arranged to be variable and dependent on the identity of the operator, so that the testing can be balanced against the performance ability of the observer. This allows targeted training programmes to be established by the facility managers to ensure optimal human operation of the screening system.


In a modification to this system, instead of a hybrid image being generated as described above, a test image representing a threat object is simply selected from a library of test images and sent to one of the work stations 20, and the response of the operator monitored to see whether their categorization of the image is correct.


The job dispatcher 16 can be arranged to allocate jobs to individual workstations or workstation operators on the basis simply of the current workload of each operator, which the job dispatcher can determine from the tasks it has already allocated, and results it is waiting for from each operator, and the threat category to which the threat detection processor has allocated the item. However where the system has a record or profile associated with each operator, the allocation of tasks to operators can also be made on the basis of the profile. For example in some case the threat detection processor may allocate items to different categories not just on the basis of a level of threat that it associates with the item, but also on the basis of the type of threat, for example the type of threat object that has been detected or the category of threat material that has been detected. Where the operator profile includes types of threat that each operator is able to analyse, or a degree of proficiency of each operator at analysing each type of threat, the job dispatcher can allocate each task to an operator at least on the basis of this information to match each task to an operator suitable to perform it.


Each operator workstation 20 has the facility to annotate the displayed image, in response to inputs from the user input 24, in order to mark up an image to indicate the presence and type of threat objects and materials that have been detected in the cargo item.


In a further modification to this embodiment of this invention, to facilitate the smooth operation of each scanning device 10, the job dispatcher 16 is able to cause the scanning system to route the passage of cargo items at its exit depending on the results of the automated detection processor and of any subsequent human inspection of the image data. For example, as shown in FIG. 2, each of the scanners 10 can have a holding bay 50 which a vehicle can enter after passing through the scanner, with a traffic control system, such as traffic lights 52, arranged to direct vehicles that have passed through the scanner 10 into the holding bay, or past the holding bay 50. If the automated threat detection processor 14 detected the presence of a threat item or material, of the traffic lights 52 adjacent to the scanner 10 will be controlled by the job dispatcher 16 to direct the load to the holding bay 50 until such time as the operator has input their response. When the operator response has been received by the job dispatcher 14 it is arranged to control further traffic controls, such as a further set of traffic lights 54, to indicate that the cargo is free to leave the scanning site, or that it needs to move on to another area for example for manual searching.


To maximise throughput of the installation, the automated threat detection processor 14 is arranged to generate a decision relating to a cargo item in a time period which is short compared to the overall scanning time for the cargo item. The job dispatcher 16 is arranged to be capable of allowing a scanner 10 to continue scanning new cargo items even if a cargo item is located in the associated holding bay 50 awaiting an operator decision.


The embodiments of FIGS. 1 and 2 are arranged to scan and control cargo carried on road vehicles, and the traffic management systems therefore rely on traffic lights and other suitable indicators or signs to direct the driver of the vehicle where to drive. However in another embodiment the system is arranged to scan cargo transported by rail. In this case the traffic management systems comprise traffic lights and also points on the rail tracks, for example at the exits 62 from the scanners in FIG. 3, that can be switched to determine the route which the cargo takes.


The job dispatcher 16 is also arranged to control queuing of multiple suspect cargo items in the holding bay in order to maximise throughput of the screening installation.


Referring to FIG. 3, in a further embodiment, a security installation is similar to that of FIG. 2 but comprises a number of scanners 60, each with an associated traffic control system 61, and arranged to scan cargo items in parallel. The exits 62 from all of the scanners 60 lead to a shared quarantine area 64 that serves all of the scanning systems 60. The traffic control systems 61 which comprise traffic lights or equivalent traffic management systems, are arranged to direct traffic either straight through scanners 60 to the exit of the scanning installation or, in the event of a threat being detected, to direct the load to the quarantine area 64 where further traffic management systems 66 are provided and arranged to route cargo loads to the exit of the installation following manual search as required.


Referring to FIG. 4, in further embodiments of the invention, which can be otherwise similar to those of FIGS. 1 to 3, the job dispatcher 16a is similar to that of FIG. 1, but is also arranged to receive, use and manage one or more different forms of information in addition to X-ray image data. This could typically include video images of the cargo load, which the job dispatcher 16a is arranged to receive from one or more video cameras 70. It can also include optical character recognition data related to container numbering, which can either be obtained by an image processor 72 arranged to process images from the video cameras, or a separate processor 74 arranged to receive and process images from an imaging device 76 specifically arranged to image a part of the container that carries the numbering. The information can also include scanned images of manifest information that may be provided with the cargo item. It may include data from secondary sensors such as weighbridge data from a weighbridge 78 indicative of the weight of the container, data from chemical detectors or ‘sniffers’ 80 indicative of the presence of one or more chemical compounds in the container, passive gamma ray data from a gamma ray detector 82 or neutron sensing data from a neutron sensor 84. The secondary sensors are shown here is present at the scanner site and part of the installation, but any of them can equally be at a separate location, and arranged to store the data they provide on a data carrier so that it can be input to the job dispatcher, or to transmit the data to the job dispatcher with some form of identification of the container it relates to. Where this ancillary data is available, the job dispatcher 16a is typically arranged to pass the data to the automated threat detection processor which is arranged to use it as an input to the threat detection algorithm that it uses in order to assist it in making the best possible threat categorization decision.


Referring to FIG. 5, in a further embodiment of the invention a cargo security system is similar to that of FIGS. 3 and 4, but the system is arranged to scan cargo carried by rail on a rail train 81. The parts of the system are distributed over larger distances so as to enable an efficient flow of cargo traffic. The system is arranged to scan and categorize cargo arriving at a port 80 on a vessel 82. The system includes a number of scanners, and all of the sources of secondary data described above with reference to FIG. 4, but these are distributed at a number of locations 84 along the rail route between the port 80 and a final quarantine or checking area. In particular the scanners 60 are at one location 84a close to the port 80 where they can be used to scan the cargo shortly after it has been loaded onto the rail vehicle 81, and the final checking area is provided at another location 84b further away from the port which may be at a destination of the cargo where it is removed from the rail vehicle 81 carrying it, and any individual cargo items or containers which are identified as a possible threat can be checked without delaying the progress of containers which are not identified as a threat. A traffic management system similar to that of FIG. 3 including rail points and traffic lights is used to control the route of each item of cargo, into or past the checking area 86, dependent on the analysis of the scan data and other secondary data by the threat detection processor. This arrangement means that the cargo items do not need to be delayed close to the port 80, and can be moving away from the port, and towards their final destination, while the threat detection analysis is being performed.

Claims
  • 1. A scanning system, comprising: two or more scanners, wherein each of the two or more scanners is configured to scan at least one of a plurality of containers, generate scan data, and communicate the scan data to a network;a data switch configured to receive the scan data via the network;a dispatcher in data communication with the data switch;an imaging device configured to capture images of characters associated with each of the plurality of containers;at least one processor, wherein the at least one processor is in data communication with the dispatcher, wherein the dispatcher is configured to cause scan data to be sent to the at least one processor, wherein the at least one processor is configured to i) generate optical character recognition data indicative of the characters associated with each of the plurality of containers ii) execute a material discrimination process on the scan data and iii) execute an automated detection process on the scan data; andone or more operator workstations in data communication with the network and configured to receive and display the scan data to one or more operators, wherein the one or more operator workstations are physically remote from the at least one processor, wherein the two or more scanners are each physically remote from the at least one processor, wherein data flow between any of the two or more scanners, the dispatcher, the at least one processor, and any of the one or more operator workstations are mediated through the data switch, and wherein the dispatcher is further configured to execute a process for automatically allocating the scan data to at least one of the one or more operator workstations for analysis, to make the scan data available to the one of the one or more operator workstations, to receive and direct the data indicative of the characters associated with the at least one of the plurality of containers and data indicative of manifest information related to the cargo and to generate a hybrid set of scan data by combining a threat object image from a library of threat object images with a portion of the scan data.
  • 2. The scanning system of claim 1, wherein the one or more operator workstations are configured to receive and display the scan data to one or more operators after the material discrimination process and the automated detection process are performed on the scan data.
  • 3. The scanning system of claim 1, wherein the at least one processor is configured to first execute the material discrimination process on the scan data and then subsequently execute the automated detection process on the scan data.
  • 4. The scanning system of claim 1, further comprising a cargo movement control system arranged to control movement of the at least one of the plurality of containers.
  • 5. The scanning system of claim 4, wherein the cargo movement control system comprises an input that, if manipulated by a user, allocates each of the plurality of containers to one of a plurality of predetermined threat categories.
  • 6. The scanning system of claim 1, further comprising a central storage array in data communication with the data switch, wherein the data switch is configured to directly transmit the scan data to the dispatcher and to directly transmit the scan data to the central storage array.
  • 7. The scanning system of claim 1, wherein the at least one processor is configured to determine a severity of a threat item based on the scan data.
  • 8. The scanning system of claim 7, wherein the dispatcher is configured to receive decision data from the one or more operator workstations, and wherein the decision data comprises a categorization of the threat item.
  • 9. The scanning system of claim 8, wherein the dispatcher is configured to generate and transmit to at least one of the two or more scanners a notification based on the decision data.
  • 10. The scanning system of claim 1, wherein the at least one processor is configured to allocate each threat item to one of a number of threat categories and wherein each of the threat categories corresponds to a different level of threat.
  • 11. The scanning system of claim 10, wherein the at least one processor is configured to be set to a predefined level of sensitivity.
  • 12. The scanning system of claim 10, wherein the at least one processor is configured to adjust said allocation such that a number of the threat items allocated to each of the threat categories changes based on said allocation.
  • 13. The scanning system of claim 10, wherein the at least one processor is configured to adjust said allocation based on an overall threat level.
  • 14. The scanning system of claim 10, wherein the at least one processor is configured to adjust said allocation based on a volume of traffic to be scanned by the two or more scanners.
  • 15. The scanning system of claim 10, wherein the at least one processor is configured to adjust said allocation based on a number of the one or more workstations available to review the scan data.
  • 16. The scanning system of claim 1, wherein the dispatcher is configured to allocate scan data to the one or more operator workstations based on an overall threat level.
  • 17. The scanning system of claim 16, wherein the dispatcher is configured to allocate scan data to the one or more operator workstations based on a volume of traffic that requires scanning by the two or more scanners.
  • 18. The scanning system of claim 1, wherein the at least one processor is configured to combine a threat object image with a portion of the scan data by superimposing the threat object image onto an image corresponding to the portion of the scan data.
  • 19. The scanning system of claim 18, wherein the portion of the scan data combined with the threat object image corresponds to an image of cargo without a threat.
Priority Claims (1)
Number Date Country Kind
0803644 Feb 2008 GB national
CROSS REFERENCE

The present application is a national stage application of PCT/GB2009/000575, filed on Feb. 27, 2009, which further relies on Great Britain Patent Application Number 0803644.4, filed on Feb. 28, 2008, for priority. The applications are incorporated herein by reference in their entirety.

US Referenced Citations (456)
Number Name Date Kind
2952790 Steen Sep 1960 A
3146349 Jordan Aug 1964 A
3239706 Farrell Mar 1966 A
3458026 Lauzon Jul 1969 A
3485339 Miller Dec 1969 A
3768645 Conway Oct 1973 A
3955678 Moyer May 1976 A
3980889 Haas Sep 1976 A
4057725 Wagner Nov 1977 A
4105922 Lambert Aug 1978 A
4228353 Johnson Oct 1980 A
4259721 Kuznia Mar 1981 A
4266425 Allport May 1981 A
4274005 Yamamura Jun 1981 A
4340816 Schott Jul 1982 A
4352021 Boyd Sep 1982 A
4366382 Kotowski Dec 1982 A
4468802 Friedel Aug 1984 A
4626688 Barnes Dec 1986 A
4672649 Rutt Jun 1987 A
4675890 Plessis Jun 1987 A
4709382 Sones Nov 1987 A
4817123 Sones Mar 1989 A
RE32961 Wagner Jun 1989 E
4866439 Kraus Sep 1989 A
4866745 Akai Sep 1989 A
4868856 Frith Sep 1989 A
4872188 Lauro Oct 1989 A
4887604 Shefer Dec 1989 A
4979137 Gerstenfeld Dec 1990 A
4987584 Doenges Jan 1991 A
4991708 Francioni Feb 1991 A
5033106 Kita Jul 1991 A
5086300 Ash More Feb 1992 A
5092451 Jones Mar 1992 A
5097939 Shanklin Mar 1992 A
5144191 Jones Sep 1992 A
5182764 Peschmann Jan 1993 A
5221843 Alvarez Jun 1993 A
5243693 Maron Sep 1993 A
5247556 Eckert Sep 1993 A
5247561 Kotowski Sep 1993 A
5259014 Brettschneider Nov 1993 A
5272627 Maschhoff Dec 1993 A
5313511 Annis May 1994 A
5319547 Krug Jun 1994 A
5341916 Doane Aug 1994 A
5367552 Peschmann Nov 1994 A
5410156 Miller Apr 1995 A
5412702 Sata May 1995 A
5467377 Dawson Nov 1995 A
5490196 Rudich Feb 1996 A
5490218 Krug Feb 1996 A
5505291 Huang Apr 1996 A
5511104 Mueller Apr 1996 A
5548123 Perez-Mendez Aug 1996 A
5557108 Tumer Sep 1996 A
5590057 Fletcher Dec 1996 A
5600303 Husseiny Feb 1997 A
5600700 Krug Feb 1997 A
5604778 Polacin Feb 1997 A
5606167 Miller Feb 1997 A
5633907 Gravelle May 1997 A
5634551 Francioni Jun 1997 A
5642393 Krug Jun 1997 A
5660549 Witt Aug 1997 A
5661774 Gordon Aug 1997 A
5689541 Schardt Nov 1997 A
5712926 Eberhard Jan 1998 A
5738202 Ydoate Apr 1998 A
5764683 Swift Jun 1998 A
5796802 Gordon Aug 1998 A
5818897 Gordon Oct 1998 A
5838758 Krug Nov 1998 A
5841831 Hell Nov 1998 A
5859891 Hibbard Jan 1999 A
5870449 Lee Feb 1999 A
5881122 Crawford Mar 1999 A
5882206 Gillio Mar 1999 A
5887047 Bailey Mar 1999 A
5901198 Crawford May 1999 A
5903623 Swift May 1999 A
5905806 Eberhard May 1999 A
5909477 Crawford Jun 1999 A
5910973 Grodzins Jun 1999 A
5930326 Rothschild Jul 1999 A
5949842 Schafer Sep 1999 A
5963211 Oikawa Oct 1999 A
5966422 Dafni Oct 1999 A
5974111 Krug Oct 1999 A
5982843 Bailey Nov 1999 A
5987097 Salasoo Nov 1999 A
6018562 Willson Jan 2000 A
6021174 Campbell Feb 2000 A
6026143 Simanovsky Feb 2000 A
6026171 Hiraoglu Feb 2000 A
6035014 Hiraoglu Mar 2000 A
6037597 Karavolos Mar 2000 A
6044353 Pugliese Mar 2000 A
6067366 Simanovsky May 2000 A
6073751 Worzischek Jun 2000 A
6075871 Simanovsky Jun 2000 A
6076400 Bechwati Jun 2000 A
6078642 Simanovsky Jun 2000 A
6088423 Krug Jul 2000 A
6091795 Schafer Jul 2000 A
6108396 Bechwati Aug 2000 A
6111974 Hiraoglu Aug 2000 A
6118852 Rogers Sep 2000 A
6122343 Pidcock Sep 2000 A
6128365 Bechwati Oct 2000 A
6137895 Al-Sheikh Oct 2000 A
6149592 Yanof Nov 2000 A
6163591 Benjamin Dec 2000 A
6181765 Sribar Jan 2001 B1
6183139 Solomon Feb 2001 B1
6185272 Hiraoglu Feb 2001 B1
6188745 Gordon Feb 2001 B1
6195444 Simanovsky Feb 2001 B1
6216540 Nelson Apr 2001 B1
6218943 Ellenbogen Apr 2001 B1
6236709 Perry May 2001 B1
6246320 Monroe Jun 2001 B1
6252929 Swift Jun 2001 B1
6256404 Gordon Jul 2001 B1
6269142 Smith Jul 2001 B1
6272230 Hiraoglu Aug 2001 B1
6292533 Swift Sep 2001 B1
6301327 Martens Oct 2001 B1
6304629 Conway Oct 2001 B1
6317509 Simanovsky Nov 2001 B1
6324243 Edic Nov 2001 B1
6324249 Fazzio Nov 2001 B1
6345113 Crawford Feb 2002 B1
6370222 Cornick Apr 2002 B1
6418189 Schafer Jul 2002 B1
6429578 Danielsson Aug 2002 B1
6430255 Fenkart Aug 2002 B2
6431344 Emmermann Aug 2002 B1
6445765 Frank Sep 2002 B1
6446782 Patrick Sep 2002 B1
6459755 Li Oct 2002 B1
6459761 Grodzins Oct 2002 B1
6459764 Chalmers Oct 2002 B1
6507025 Verbinski Jan 2003 B1
6542580 Carver Apr 2003 B1
6546072 Chalmers Apr 2003 B1
6549683 Bergeron Apr 2003 B1
6552346 Verbinski Apr 2003 B2
6556653 Hussein Apr 2003 B2
6563906 Hussein May 2003 B2
6590956 Fenkart Jul 2003 B2
6618466 Ning Sep 2003 B1
6629593 Zeitler Oct 2003 B2
6647091 Fenkart Nov 2003 B2
6647094 Harding Nov 2003 B2
6647095 Hsieh Nov 2003 B2
6687333 Carroll Feb 2004 B2
6690766 Kresse Feb 2004 B2
6707879 McClelland Mar 2004 B2
6715533 Kresse Apr 2004 B2
6721387 Naidu Apr 2004 B1
6721391 McClelland et al. Apr 2004 B2
6735271 Rand May 2004 B1
6737652 Lanza May 2004 B2
6748043 Dobbs Jun 2004 B1
6754298 Fessler Jun 2004 B2
6760407 Price Jul 2004 B2
6770884 Bryman Aug 2004 B2
6775348 Hoffman Aug 2004 B2
6788761 Bijjani Sep 2004 B2
6812426 Kotowski Nov 2004 B1
6813374 Karimi Nov 2004 B1
6816571 Bijjani Nov 2004 B2
6827265 Knowles Dec 2004 B2
6829585 Grewal et al. Dec 2004 B1
6830185 Tsikos Dec 2004 B2
6837432 Tsikos Jan 2005 B2
6856667 Ellenbogen Feb 2005 B2
6859514 Hoffman Feb 2005 B2
6899540 Neiderman May 2005 B1
6901135 Fox May 2005 B2
6901346 Tracy May 2005 B2
6906329 Bryman Jun 2005 B2
6907101 Hoffman Jun 2005 B2
6922455 Jurczyk Jul 2005 B2
6922460 Skatter Jul 2005 B2
6922461 Kang Jul 2005 B2
6928141 Carver Aug 2005 B2
6933504 Hoffman Aug 2005 B2
6934354 Hoffman Aug 2005 B2
6940071 Ramsden Sep 2005 B2
6944264 Bijjani Sep 2005 B2
6947517 Hoffman Sep 2005 B2
6950492 Besson Sep 2005 B2
6950493 Besson Sep 2005 B2
6952163 Huey Oct 2005 B2
6953935 Hoffman Oct 2005 B1
6957913 Renkart Oct 2005 B2
6962289 Vatan Nov 2005 B2
6968030 Hoffman Nov 2005 B2
6968034 Ellenbogen Nov 2005 B2
6971577 Tsikos Dec 2005 B2
6973158 Besson Dec 2005 B2
6975698 Katcha Dec 2005 B2
6978936 Tsikos Dec 2005 B2
6980627 Qiu Dec 2005 B2
6990171 Toth Jan 2006 B2
6990172 Toth Jan 2006 B2
6991371 Georgeson Jan 2006 B2
6993115 McGuire Jan 2006 B2
6996209 Marek Feb 2006 B2
7010083 Hoffman Mar 2006 B2
7016459 Ellenbogen Mar 2006 B2
7020241 Beneke Mar 2006 B2
7020242 Ellenbogen Mar 2006 B2
7023956 Heaton Apr 2006 B2
7023957 Bijjani Apr 2006 B2
7027553 Dunham Apr 2006 B2
7027554 Gaultier Apr 2006 B2
7031430 Kaucic Apr 2006 B2
7031434 Saunders Apr 2006 B1
7034313 Hoffman Apr 2006 B2
7039154 Ellenbogen May 2006 B1
7042975 Heuscher May 2006 B2
7045787 Verbinski May 2006 B1
7046756 Hoffman May 2006 B2
7046761 Ellenbogen May 2006 B2
7050536 Fenkart May 2006 B1
7054408 Jiang May 2006 B2
7062009 Karimi Jun 2006 B2
7062011 Tybinkowski Jun 2006 B1
7062074 Beneke Jun 2006 B1
7064334 Hoffman Jun 2006 B2
7065175 Green Jun 2006 B2
7065179 Block Jun 2006 B2
7068749 Kollegal Jun 2006 B2
7068750 Toth Jun 2006 B2
7068751 Toth Jun 2006 B2
7072434 Tybinkowski Jul 2006 B1
7076029 Toth Jul 2006 B2
7078699 Seppi Jul 2006 B2
7081628 Granfors Jul 2006 B2
7084404 Hoffman Aug 2006 B2
7087902 Wang Aug 2006 B2
7088799 Hoffman Aug 2006 B2
7090133 Zhu Aug 2006 B2
7092481 Hoffman Aug 2006 B2
7092485 Kravis Aug 2006 B2
7103137 Seppi Sep 2006 B2
7110488 Katcha Sep 2006 B2
7112797 Hoge Sep 2006 B2
7116749 Besson Oct 2006 B2
7116751 Ellenbogen Oct 2006 B2
7119553 Yang Oct 2006 B2
7123681 Ellenbogen Oct 2006 B2
7127027 Hoffman Oct 2006 B2
7130374 Jacobs Oct 2006 B1
7133491 Bernardi Nov 2006 B2
7136450 Ying Nov 2006 B2
7136451 Naidu Nov 2006 B2
7139367 Le Nov 2006 B1
7139406 McClelland Nov 2006 B2
7142629 Edic Nov 2006 B2
7149278 Arenson Dec 2006 B2
7149339 Veneruso Dec 2006 B2
7155812 Peterson Jan 2007 B1
7158611 Heismann Jan 2007 B2
7164747 Ellenbogen Jan 2007 B2
7164750 Nabors Jan 2007 B2
7166458 Ballerstadt Jan 2007 B2
7166844 Gormley Jan 2007 B1
7167539 Hoffman Jan 2007 B1
7173998 Hoffman Feb 2007 B2
7177387 Yasunaga Feb 2007 B2
7177391 Chapin Feb 2007 B2
7190757 Ying Mar 2007 B2
7192031 Dunham Mar 2007 B2
7197113 Katcha Mar 2007 B1
7197172 Naidu Mar 2007 B1
7203629 Oezis Apr 2007 B2
7204125 Fine et al. Apr 2007 B2
7206379 Lemaitre Apr 2007 B2
7212113 Zanovitch May 2007 B2
7215731 Basu May 2007 B1
7215738 Muenchau May 2007 B2
7218700 Huber May 2007 B2
7218704 Adams May 2007 B1
7224763 Naidu May 2007 B2
7224765 Ellenbogen May 2007 B2
7224766 Jiang May 2007 B2
7224769 Turner May 2007 B2
7233640 Ikhlef Jun 2007 B2
7236564 Hopkins Jun 2007 B2
7238945 Hoffman Jul 2007 B2
7247856 Hoge Jul 2007 B2
7251310 Smith Jul 2007 B2
7257189 Modica Aug 2007 B2
7260170 Arenson Aug 2007 B2
7260171 Arenson Aug 2007 B1
7260172 Arenson Aug 2007 B2
7260173 Wakayama Aug 2007 B2
7260174 Hoffman Aug 2007 B2
7260182 Toth Aug 2007 B2
7263160 Schlomka Aug 2007 B2
7266180 Saunders Sep 2007 B1
7272429 Walker Sep 2007 B2
7274767 Clayton Sep 2007 B2
7277577 Ying Oct 2007 B2
7279120 Cheng Oct 2007 B2
7280631 De Oct 2007 B2
7282727 Retsky Oct 2007 B2
7283604 De Oct 2007 B2
7283609 Possin Oct 2007 B2
7295019 Yang Nov 2007 B2
7295651 Delgado Nov 2007 B2
7298812 Tkaczyk Nov 2007 B2
7302083 Larson Nov 2007 B2
7308073 Tkaczyk Dec 2007 B2
7308074 Jiang Dec 2007 B2
7308077 Bijjani Dec 2007 B2
7317195 Eikman Jan 2008 B2
7317390 Huey Jan 2008 B2
7319737 Singh Jan 2008 B2
7322745 Agrawal Jan 2008 B2
7324625 Eilbert Jan 2008 B2
7327853 Ying Feb 2008 B2
7330527 Hoffman Feb 2008 B2
7330535 Arenson Feb 2008 B2
7333587 De Feb 2008 B2
7333588 Mistretta Feb 2008 B2
7333589 Ellenbogen Feb 2008 B2
7335887 Verbinski Feb 2008 B1
7336769 Arenson Feb 2008 B2
7349525 Morton Mar 2008 B2
7397891 Johnson Jul 2008 B2
7430479 Holslin Sep 2008 B1
7440543 Morton Oct 2008 B2
7492855 Hopkins Feb 2009 B2
7505557 Modica Mar 2009 B2
7512215 Morton Mar 2009 B2
7564939 Morton Jul 2009 B2
7580505 Kang Aug 2009 B2
7684538 Morton Mar 2010 B2
7734066 DeLia Jun 2010 B2
7734102 Bergeron Jun 2010 B2
7817775 Kang Oct 2010 B2
7903783 Modica Mar 2011 B2
7973697 Reilly Jul 2011 B2
8173970 Inbar May 2012 B2
8243167 Liang Aug 2012 B2
8304740 Frank Nov 2012 B1
8472583 Star-Lack Jun 2013 B2
9111331 Parikh Aug 2015 B2
9632206 Parikh Apr 2017 B2
20010016684 Shahidi Aug 2001 A1
20010022346 Katagami Sep 2001 A1
20010033635 Kuwabara Oct 2001 A1
20020031202 Callerame Mar 2002 A1
20020038753 Ursu Apr 2002 A1
20020045152 Viscardi Apr 2002 A1
20020094064 Zhou Jul 2002 A1
20020172324 Ellengogen Nov 2002 A1
20020176531 McClelland Nov 2002 A1
20030021377 Turner Jan 2003 A1
20030023592 Modica Jan 2003 A1
20030031352 Nelson Feb 2003 A1
20030085163 Chan et al. May 2003 A1
20030191557 Takehara Oct 2003 A1
20040041724 Levitan Mar 2004 A1
20040073808 Smith Apr 2004 A1
20040080315 Beevor Apr 2004 A1
20040086078 Adams May 2004 A1
20040101098 Bijjani et al. May 2004 A1
20040120454 Ellenbogen Jun 2004 A1
20040126015 Hadell Jul 2004 A1
20040140924 Keller Jul 2004 A1
20040202154 Aklepi Oct 2004 A1
20040212492 Boesch Oct 2004 A1
20040212499 Bohinc Oct 2004 A1
20040213378 Zhou Oct 2004 A1
20040213379 Bittl Oct 2004 A1
20040232054 Brown Nov 2004 A1
20040251415 Verbinski Dec 2004 A1
20040252024 Huey Dec 2004 A1
20040252807 Skatter Dec 2004 A1
20040258198 Carver Dec 2004 A1
20040258305 Burnham Dec 2004 A1
20040263379 Keller Dec 2004 A1
20050008119 McClelland et al. Jan 2005 A1
20050024199 Huey Feb 2005 A1
20050031075 Hopkins Feb 2005 A1
20050031076 McClelland et al. Feb 2005 A1
20050053189 Gohno Mar 2005 A1
20050064922 Owens Mar 2005 A1
20050105682 Heumann May 2005 A1
20050110672 Cardiasmenos May 2005 A1
20050111610 Deman May 2005 A1
20050117700 Peschmann Jun 2005 A1
20050156734 Zerwekh Jul 2005 A1
20050157844 Bernardi Jul 2005 A1
20050157925 Lorenz Jul 2005 A1
20050169421 Muenchau Aug 2005 A1
20050198226 Delia Sep 2005 A1
20050226364 Bernard Oct 2005 A1
20050249416 Leue Nov 2005 A1
20050251397 Zanovitch et al. Nov 2005 A1
20050281390 Johnson Dec 2005 A1
20060018428 Li Jan 2006 A1
20060045323 Ateya Mar 2006 A1
20060066469 Foote Mar 2006 A1
20060086794 Knowles Apr 2006 A1
20060113163 Hu Jun 2006 A1
20060115044 Wu Jun 2006 A1
20060115109 Whitson et al. Jun 2006 A1
20060138331 Guillebaud Jun 2006 A1
20060220851 Wisherd Oct 2006 A1
20060257005 Bergeron Nov 2006 A1
20060273259 Li Dec 2006 A1
20060274916 Chan et al. Dec 2006 A1
20070003003 Seppi Jan 2007 A1
20070083414 Krohn Apr 2007 A1
20070096030 Li May 2007 A1
20070110215 Hu May 2007 A1
20070133740 Kang Jun 2007 A1
20070165777 Anwar Jul 2007 A1
20070172024 Morton Jul 2007 A1
20070183568 Kang Aug 2007 A1
20070194909 Garfield et al. Aug 2007 A1
20070195994 McClelland Aug 2007 A1
20070280416 Bendahan Dec 2007 A1
20070280502 Paresi Dec 2007 A1
20080023631 Majors Jan 2008 A1
20080044801 Modica Feb 2008 A1
20080056432 Pack Mar 2008 A1
20080056435 Basu Mar 2008 A1
20080075230 Oreper Mar 2008 A1
20080111693 Johnson May 2008 A1
20080143545 King Jun 2008 A1
20080198967 Connelly Aug 2008 A1
20080260097 Anwar Oct 2008 A1
20090034790 Song Feb 2009 A1
20090161816 Deman Jun 2009 A1
20090174554 Bergeron Jul 2009 A1
20090236531 Frank Sep 2009 A1
20090283690 Bendahan Nov 2009 A1
20090323894 Hu Dec 2009 A1
20100030370 King Feb 2010 A1
20100161504 Casey Jun 2010 A1
20110060426 Morton Mar 2011 A1
20110172972 Gudmundson Jul 2011 A1
20110216881 Modica Sep 2011 A1
20120093367 Gudmundson Apr 2012 A1
20120105267 DeLia May 2012 A1
20120300902 Modica Nov 2012 A1
20150325010 Bedford Nov 2015 A1
Foreign Referenced Citations (58)
Number Date Country
101022649 Aug 2007 CN
101303317 Nov 2008 CN
101446910 Jun 2009 CN
2729353 Jan 1979 DE
3214910 May 1983 DE
0176314 Apr 1986 EP
0432568 Jun 1991 EP
0531993 Mar 1993 EP
0584871 Mar 1994 EP
0924742 Jun 1999 EP
0930046 Jul 1999 EP
0963925 Dec 1999 EP
1277439 Jan 2003 EP
1374776 Jan 2004 EP
2270547 Jan 2011 EP
2328280 May 1977 FR
1497396 Jan 1978 GB
1526041 Sep 1978 GB
2015245 Sep 1979 GB
2089109 Jun 1982 GB
2212903 Aug 1989 GB
2337032 Nov 1999 GB
2404431 Feb 2005 GB
2437777 Nov 2007 GB
S57175247 Oct 1982 JP
S5916254 Jan 1984 JP
59075549 Apr 1984 JP
600015546 Jan 1985 JP
600021440 Feb 1985 JP
06038957 Feb 1994 JP
H10211196 Aug 1998 JP
H11230918 Aug 1999 JP
2001176408 Jun 2001 JP
2001233440 Aug 2001 JP
2003126075 May 2003 JP
2004000605 Jan 2004 JP
2004079128 Mar 2004 JP
2005013768 Jan 2005 JP
2005257400 Sep 2005 JP
1019920010403 Jun 1992 KR
100796878 Mar 2006 KR
1020060078151 Jul 2006 KR
9528715 Oct 1995 WO
9960387 Nov 1999 WO
2000049428 Aug 2000 WO
03051201 Jun 2003 WO
03105159 Dec 2003 WO
2004037088 May 2004 WO
2004111625 Dec 2004 WO
2005091227 Sep 2005 WO
2005084351 Nov 2006 WO
2006119603 Nov 2006 WO
2006119605 Nov 2006 WO
2006135586 Dec 2006 WO
2007051092 May 2007 WO
2007055720 May 2007 WO
2007103216 Sep 2007 WO
2009106857 Sep 2009 WO
Non-Patent Literature Citations (23)
Entry
US 5,987,079 A, 11/1999, Scott (withdrawn)
Horner et al., “Phase-Only Matched Filtering”, Applied Optics, vol. 23, No. 6, Mar. 15, 1994, pp. 812-816.
Mahalanobis, et al. “Minimum Average Correlation Energy Filters”, Applied Optics, vol. 26, No. 17, pp. 3633-3640, Sep. 1987.
Kumar et al. “Spatial frequency domain image processing for biometric recognition”, Biometrics ICIP Conference 2002.
Caulfield, et al. “Improved Discrimination in Optical Character Recognition”, Applied Optics, vol. 8, pp. 2354-2356, Nov. 1969.
Morin, et al. “Optical Character Recognition (OCR) in Uncontrolled Environments Using Optical Correlators”, Proc. SPIE Int. Soc. Opt. Eng. 3715, 346; 1999.
International Search Report for PCT/US2012/054110, dated Dec. 24, 2012.
International Search Report for PCT/US2017/017642, dated Jun. 29, 2017.
‘Test and Evaluation Plan for Screener Proficiency Evaluation and Reporting System (SPEARS) Threat Image Projection’ J.L.Fobes, Ph.D., et al. FAA, Dec. 1995.
‘Revised Test and Evaluation Plan for Determining Screener Training Effectiveness’ Brenda A. Klock, et al. FAA, Aug. 2000.
‘Development and Validation of a Test of X-ray Screener Readiness’ Eric C. Neiderman, Ph.D., et al. IEEE, 2000.
Rapiscan Security Products, Inc., Users Guide for Level 3 Threat Image Projection (TIP) System Manual, Aug. 4, 1999, document in general.
Viggo Butler and Robert W. Poole, Jr., Rethinking Checked-Baggage Screening, Reason Public Policy Institute, Policy Study 297, Jul. 2002.
McLay, Laura A., Jacobson, Sheldon H., and Kobza, John E., A multilevel passenger screening problem for aviation security, Naval Research Logistics (NRL), vol. 53, issue 3, pp. 183-197, 2006.
Sun Olapiriyakul and Sanchoy Das, Design and analysis of a two-stage security screening and inspection system, Journal of Air Transport Management, vol. 13, Issue 2, Mar. 2007, pp. 67-74.
Kelly Leone and Rongfang (Rachel) Liu, The key design parameters of checked baggage security screening systems in airports, Journal of Air Transport Management, vol. 11, Issue 2, Mar. 2005, pp. 69-78.
International Search Report for PCT/GB2009/000575, dated Apr. 7, 2010, Rapiscan Security Products Inc.
International Search Report for PCT/US2007/005444, dated Oct. 29, 2007, Telesecurity Sciences, Inc.
International Search Report for PCT/US2006/11492, dated Oct. 11, 2007, United Technologies Corporation.
International Search Report for PCT/GB2004/001747, dated Aug. 10, 2004, CXR Ltd.
ClearView Workstation, L3 Security & Detection Systems, Jun. 9, 2011.
Rapiscan Security Products, Inc., Users Guide for Levels 1 and 2 Threat Image Protection (TIP) Users Manual, Jan. 12, 2001, document in general.
Victor J. Orphan, Ernie Muenchau, Jerry Gormley, and Rex Richardson, “Advanced y ray technology for scanning cargo containers,” Applied Radiation and Isotopes, vol. 63, Issues 5-6, 2005, pp. 723-732.
Related Publications (1)
Number Date Country
20220229204 A1 Jul 2022 US
Continuations (3)
Number Date Country
Parent 16778004 Jan 2020 US
Child 17649847 US
Parent 14948788 Nov 2015 US
Child 16778004 US
Parent 12919484 US
Child 14948788 US