WO2005010561B1 - Methods and apparatus for detecting objects in baggage using x-rays - Google Patents
Methods and apparatus for detecting objects in baggage using x-raysInfo
- Publication number
- WO2005010561B1 WO2005010561B1 PCT/US2004/023360 US2004023360W WO2005010561B1 WO 2005010561 B1 WO2005010561 B1 WO 2005010561B1 US 2004023360 W US2004023360 W US 2004023360W WO 2005010561 B1 WO2005010561 B1 WO 2005010561B1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- act
- determining
- voxels
- objects
- eliminating
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract 83
- 239000002360 explosive Substances 0.000 claims abstract 4
- 238000001514 detection method Methods 0.000 claims abstract 2
- 239000000463 material Substances 0.000 claims abstract 2
- 239000013598 vector Substances 0.000 claims 28
- 239000011159 matrix material Substances 0.000 claims 20
- 238000002591 computed tomography Methods 0.000 claims 3
- 230000003247 decreasing effect Effects 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract 1
Classifications
-
- G01V5/20—
Abstract
A method of detecting a presence or absence of subject matter of interest, for example, sheet explosives and/or other potential threat objects is provided by various local and/or global gradient analysis methods including determining characteristics of gradient information of regions in an X-ray image to determine if the regions are associated with subject matter of interest for which detection may be desired, for example, contraband, explosives and/or other prohibited or unauthorized material.
Claims
AMENDED CLAIMS [received by the International Bureau on 14 June 2005 (14.06.05) ; claims 1,3,5,14,26,27,29,30,31,36 and 42 amended ; claim 2 cancelled ; remaining claims unchanged. (14 pages)]
1. A method of detecting a presence or an absence of subj ect matter of interest in an article of baggage from at least one image of the article of baggage, the method comprising acts of. determining a gradient field for at least a portion of the at least one image; determining at least one characteristic of the gradient field; and determining whether the portion of the at least one image is associated with the subject matter of interest based on the at least one characteristic.
2. (Cancel).
3. The method of claim 2, wherein the act of determining at least one characteristic of the gradient field includes an act of determining principal directions of variation of the gradient field.
4. The method of claim 3 , wherein the act of determining whether the portion of the at least one image is associated with the subject matter of interest includes an act of performing at least one comparison of the principal directions of variation.
5. The method of claim 2, wherein the act of determining at least one characteristic includes an act of determining a covariance matrix associated with the gradient field.
6. The method of claim 5, wherein the act of determining at least one characteristic includes an act of determining a plurality of eigenvalues associated with respective eigenvectors of the covariance matrix,
7. The method of claim 6, wherein the act of deteπruning at least one characteristic includes an act of performing at least one comparison of the plurality of eigenvalues.
8. The method of claim 7, wherein the act of determining whether the portion of the at least one image is associated with the subject matter of interest includes an act of determining at least one ratio between the plurality of eigenvalues.
AMENDED SHEET (ARTICLE 19) 39
9. The method of claim 8, wherein the act of determining the plurality of eigenvalues includes an act of determining a first eigenvalue, a second eigenvalue and a third eigenvalue respectively ordered according to decreasing magnitude.
10. The method of claim 9, wherein the act of determining at least one ratio includes an act of deteπnirung at least one of a first ratio of the first eigenvalue to the third eigenvalue, a second ratio of the first eigenvalue to the second eigenvalue, and a third ratio of the third eigenvalue to a product of the first and second eigenvalue,
11. The method of claim 10, wherein the act of determining whether the portion of the at least one image is associated with the subject matter of interest includes comparing at least one of the first ratio, the second ratio and the third ratio to a predetermined threshold.
12. The method of claim 1 , wherein the portion of the at least one image includes a region of interest having a plurality of voxels.
13. The method of claim 12, wherein the act of determining whether the portion is associated with the subject matter of interest includes an act of forming a first set of voxels from voxels in the region of interest based on the at least one characteristic.
14. The method of claim 13, wherein the act of determining the gradient field includes an act of determining for each target voxel in the region of interest a gradient field of a neighborhood of the voxel.
15. The method of claim 14, further comprising an act of determining principal directions of variation associated with the gradient field.
16. The method of claim 14, further comprising an act of deterrnining a covariance matrix associated with the gradient field.
AMENDED SHEET (ARTICLE 19) 40
17, The method of claim 16, further comprising an act of determining a plurality of eigenvalues associated with respective eigenvectors of the covariance matrix.
18 , The method of claim 17, further comprising acts of: performing at least one comparison of the plurality of eigenvalues; and adding the target voxel to the first set of voxels depending on the at least one comparison,
19, The method of claim 18, wherein the act of performing at least one comparison includes an act of determining from the plurality of eigenvalues, a first ratio of a first eigenvalue having a greatest magnitude to a second eigenvalue having a least magnitude and a second ratio of the first eigenvalue to a third eigenvalue having a magnitude between the first eigenvalue and the second eigenvalue,
20, The method of claim 19, wherein the act of forming the first set of voxels includes acts of: comparing the first ratio to a first threshold value; and adding the target voxel to the first set of voxels when the first ratio exceeds the first threshold.
21 , The method of claim 19, wherein the act of forming the first set of voxels includes acts of: comparing the first ratio to a first threshold value; comparing the second ratio to a second threshold value; and adding the target voxel to the first set of voxels when the first ratio exceeds the first threshold value and the second ratio exceeds the second threshold value,
22, The method of claim 18, further comprising an act of forming a first set of objects from the first set of voxels, each object in the first set of objects being comprised of voxels from the first set of voxels that are respectively connected.
23 , The method of claim 22, further comprising an act of determining at least a mass of each object in the first set of objects.
AMENDED SHEET (ARTICLE 19) 41
24. The method of claim 23, further comprising an act of comparing each mass to a mass threshold value.
25. The method of claim 24, wherein the act of determining whether the portion is associated with the subject matter of interest includes an act of determining each object having a mass exceeding the mass threshold as including the subject matter of interest.
26. The method of claim 1 , wherein the act of determining the gradient field includes an act of determining at least one Hessian matrix associated with the portion of the at least one image,
27. The method of claim 26, wherein the act of determining at least one characteristic of the gradient field includes an act of determining a set of eigenvalues associated with respective eigenvectors of the at least one Hessian matrix.
28. The method of claim 1, wherein the portion of the at least one image includes a first segmented object having a plurality of voxels.
29. The method of claim 28, wherein the act of determining the gradient field includes an act of determining a gradient field for the first segmented object.
30. The method of claim 29, wherein the act of determining at least one characteristic of the gradient field includes an act of determining principal directions of variance for the gradient field.
31. The method of claim 28, wherein the act of determining the gradient field includes determining a gradient field vector for each of the plurality of voxels.
32. The method of claim 31 , wherein the act of determining at least one characteristic includes an act of comparing a magnitude of each of the plurality of gradient field vectors with a first and second threshold value.
3 . The method of claim 32, further comprising an act of grouping together each voxel having a magnitude greater than the first threshold value and less than the second threshold value to form a second segmented object.
34. The method of claim 33, further comprising acts of: comparing a number of voxels in the second segmented object to a number of voxels in the first segmented object; and eliminating the second segmented object when the number of voxels in the second segmented object is less than a predetermined percentage of the number of voxels in the first segmented object.
35. The method of claim 34, further comprising an act of determining a mean vector from the plurality of gradient field vectors of voxels in the second segmented object.
36. The method of claim 35, wherein the act of determining the gradient field includes an act of determining a covariance matrix of the gradient field of the second segmented object based on the mean vector.
37. The method of claim 36, wherein the act of determining at least one characteristic includes an act of determining a plurality of eigenvalues associated with eigenvectors of the convariance matrix.
38. The method of claim 37, wherein the act of determining whether the portion is associated with the subject matter of interest includes an act of performing at least one comparison of the plurality of eigenvalues.
39. The method of claim 37, wherein the act of determining at least one characteristic includes an act of determining at least one of a first ratio of a greatest magnitude eigenvalue of the covariance matrix to a least magnitude eigenvalue of the convariance matrix and a second ratio of the least magnitude eigenvalue of the covariance matrix to a product of the greatest magnitude eigenvalue and a median magnitude eigenvalue of the covariance matrix.
AMENDED SHEET (ARTICLE 19) 43
40. The method of claim 39, wherein the act of determining whether the portion is associated with the subject matter of interest includes an act of identifying the second segmented object as including the subject matter of interest when the first ratio is less than a predetermined threshold value.
41. The method of claim 39, wherein the act of determining whether the portion is associated with an object of interest includes an act of identifying the second segmented object as an object of interest when the first ratio is less than a first predetermined threshold value and the second ratio is less than a second predetermined threshold value.
42. The method of claim 1, A wherein the subject matter of interest is a sheet explosive.
43. A method of segmenting at least one image of an article of baggage into a first set of objects representing potential threat material within the baggage, the method comprising acts of: obtaining a region of interest having a plurality of voxels from the at least one image; eliminating voxels from the plurality of voxels based on an intensity of each of the plurality of voxels and based on at least one characteristic of first gradient information of a neighborhood of each voxel; forming a first set of voxels from voxels of the plurality of voxels that were not eliminated; and grouping together connected voxels from the first set of voxels, to form the first set of objects.
44. The method of claim 43, wherein the act of eliminating voxels based on the at least one characteristic of the first gradient information further comprises an act of determining a gradient field vector for each voxel in the neighborhood.
45. The method of claim 44, wherein the act of eliminating voxels based on at the least one characteristic of the first gradient information further comprises an act of determining principal directions of variation of the gradient field vectors,
AMENDED SHEET (ARTICLE 19) 44
46. The method of claim 44, wherein the act of eliminating voxels based on at the least one characteristic of the first gradient information further comprises an act of determining a mean vector from the gradient field vectors.
47. The method of claim 46, wherein the act of eliminating voxels based on the at least one characteristic of the first gradient information further comprises an act of determining a covariance matrix of the neighborhood gradient field based on the mean vector and the gradient field vectors.
48. The method of claim 47, wherein the act of eliminating voxels based on the at least one characteristic of the first gradient information further comprises an act of determining a plurality of eigenvalues associated with eigenvectors of the covariance matrix.
49. The method of claim 48, wherein the act of eliminating voxels based on the at least one characteristic of the first gradient information includes an act of eliminating voxels based on at least one comparison of the plurality of eigenvalues.
50. The method of claim 49, wherein the act of eliminating voxels based on the at least one characteristic of the first gradient information includes an act of eliminating voxels based on a threshold of at least one ratio of the plurality of eigenvalues.
51. The method of claim 43, further comprising an act of eliminating objects from the first set of objects based at least on the number of voxels comprising the respective object.
52. The method of claim 51 , wherein the act of eliminating obj ects from the first set of objects based at least on the number of voxels includes eliminating objects having a mass below a predetermined mass threshold.
53. The method of claim 43, further comprising an act of eliminating objects from the first set of objects based on at least one characteristic of second gradient information obtained from each respective object in the first set of objects to form a second set of objects.
AMENDED SHEET (ARTICLE 19) 45
54, The method of claim 53, wherein the act of eliminating objects from the first set of objects based on at least one characteristic of second gradient information includes an act of determining a gradient field for each respective object.
55. The method of claim 54, wherein the act of eliminating objects from the first set of objects based on at least one characteristic of second gradient information includes an act of determining principal directions of variation of the gradient field.
56. The method of claim 54, wherein the act of eliminating objects from the first set of objects based on at least one characteristic of second gradient information includes an act of eliminating voxels of an object having gradient field vectors with magnitudes outside a predetermined range.
57. The method of claim 56, further comprising an act of eliminating objects from the first set of objects when a number of voxels having gradient field vector magnitudes within the predetermined magnitude range is below a predetermined percentage of a number of voxels comprising the object.
58. The method of claim 54, wherein the act of eliminating objects from the first set of objects based on at least one characteristic of second gradient information includes an act of determining a mean vector for each object in the first set of objects based on the gradient field.
59. The method of claim 58, wherein the act of eliminating objects from the first set of objects based on at least one characteristic of second gradient information includes an act of determining a covariance matrix for each object based on the mean vector and the gradient field,
60. The method of claim 59, wherein the act of eliminating objects from the first set of objects based on at least one characteristic of second gradient information includes an act of deter iriing a plurality of eigenvalues associated with respective eigenvectors of the covariance matrix.
AMENDED SHEET (ARTICLE 19) 46
61. The method of claim 60, wherein the act of eliminating objects from the first set of objects based on at least one characteristic of second gradient information includes an act of eliminating objects based on at least one comparison of the plurality of eigenvalues.
62. A method of reducing false alarms in detecting subject matter of interest in at least one article of baggage from at least one image of the at least one article of baggage, the method comprising acts of: segmenting the at least one image into a first set of objects based at least on voxel intensity; eliminating objects from the first set of objects based on first gradient information obtained from each respective object to form a second set of objects, each object in the second set of objects having a plurality of voxels; and eliminating voxels from the plurality of voxels based on second gradient information obtained from a neighborhood of each respective voxel to form a third set of objects,
63. The method of claim 62, further comprising an act of eliminating objects from the third set of objects based at least on a number of voxels comprising each respective object to form a set of threat objects.
6 . The method of claim 62, wherein the act of eliminating voxels based on second gradient information further comprises an act of determining a gradient field vector for each voxel in the neighborhood.
65. The method of claim 64, wherein the act of eliminating voxels based on second gradient information further comprises an act of determining principal directions of variation of the gradient field vectors.
66. The method of claim 64, wherein the act of eliminating voxels based on second gradient information further comprises determining a mean vector from the gradient field vectors.
AMENDED SHEET (ARTICLE 19) 47
67. The method of claim 66, wherein the act of eliminating voxels based on second gradient information further comprises an act of determining a covariance matrix of the neighborhood based on the mean vector and the gradient field vectors.
68. The method of claim 67, wherein the act of eliminating voxels based on second gradient information further comprises an act of determining a plurality of eigenvalues associated with respective eigenvectors of the covariance matrix.
69. The method of claim 68, wherein the act of eliminating voxels based on second gradient information includes an act of eliminating voxels based on at least one comparison of the plurality of eigenvalues.
70. The method of claim 62, further comprising an act of eliminating objects from the second set of objects based at least on a number of voxels comprising the respective object.
71. The method of claim 62, wherein the act of eliminating obj ects from the first set of objects based on first gradient information includes an act of determining a gradient field for each respective object.
72. The method of claim 71 , wherein the act of eliminating objects from the first set of objects based on first gradient information includes an act of eliminating voxels of an object having gradient field vectors with magnitudes outside a predetermined magnitude range.
73. The method of claim 72, further comprising an act of eliminating objects from the first set of objects when a number of voxels having gradient field vector magnitudes within the predetermined magnitude range is below a predetermined percentage of a number of voxels in the object.
74. The method of claim 71 , wherein the act of eliminating objects from the first set of objects based on first gradient information includes an act of determining a mean vector for each object in the second set based on the gradient field.
AMENDED SHEET (ARTICLE 19) 48
75. The method of claim 74, wherein the act of eliminating obj ects from the first set of objects based on first gradient information includes an act of determining a covariance matrix of each object based on the mean vector and the gradient field,
76. The method of claim 75, wherein the act of eliminating objects from the first set of objects based on first gradient information includes an act of determining a plurality of eigenvalues associated with eigenvectors of the covariance matrix.
77. The method of claim 76, wherein the act of eliminating objects from the first set of objects based on first ^adient information includes an act of eliminating objects based on at least one comparison of the plurality of eigenvalues.
78. A computer readable medium encoded with a program for execution on at least one processor, the program, when executed on the at least one processor, performing a method of detecting a presence or an absence of subject matter of interest in an article of baggage from at least one image of the article of baggage, the method comprising acts of: obtaining gradient information of at least a portion of the at least one image; determining at least one characteristic of the gradient information; and determining whether the portion of the at least one image is associated with the subject matter of interest based on the at least one characteristic,
79. The computer readable medium of claim 78, wherein the act of obtaining gradient information includes an act of determining a gradient field for the portion of the at least one image, the gradient field having a plurality of gradient field vectors associated with a plurality of voxels comprising the portion of the at least one image.
80. The computer readable medium of claim 79, wherein the act of determining at least one characteristic of the gradient information includes an act of deteπnining principal directions of variation of the gradient field.
AMENDED SHEET (ARTICLE 19) 49
81. The computer readable medium of claim 80, wherein the act of determining whether the portion of the at least one image i9 associated with the subject matter of interest includes an act of performing at least one comparison of the principal directions of variation.
82. The computer readable medium of claim 78, wherein the portion of the at least one image includes a region of interest having a plurality of voxels.
83. The computer readable medium of claim 82, wherein the act of determining whether the portion is associated with the subject matter of interest includes an act of forming a first set of voxels from voxels in the region of interest based on the at least one characteristic.
84. The computer readable medium of claim 83, wherein the act of determining gradient information includes an act of determining for each target voxel in the region of interest a gradient field of a neighborhood of the voxel.
85. The computer readable medium of claim 84, further comprising an act of deterrnining principal directions of variation associated with the gradient field.
86. The computer readable medium of claim 85, further comprising acts of: performing at least one comparison of the principal directions of variation; and adding the target voxel to the first set of voxels depending on the at least one comparison.
87. The computer readable medium of claim 86, further comprising an act of forming a first set of objects from the first set of voxels, each object in the first set of objects being comprised of voxels from the first set of voxels that are respectively connected.
88. The computer readable medium of claim 87, further comprising an act of determining at least a mass of each object in the first set of objects.
89. The computer readable medium of claim 88, further comprising an act of comparing each mass to a mass threshold value.
AMENDED SHEET (ARTICLE 19) 50
90. The computer readable medium of claim 89, wherein the act of determining whether the portion is associated with the subject matter of interest includes an act of determining each object having a mass exceeding the mass threshold as including the subject matter of interest.
91. The computer readable medium of claim 78, wherein the portion of the at least one image includes a first segmented object having a plurality of voxels.
92. The computer readable medium of claim 91 , wherein the act of obtaining gradient information includes an act of determining a gradient field for the first segmented object,
93. The computer readable medium of claim 92, wherein the act of determining at least one characteristic of the gradient information includes an act of determining principal directions of variance for the gradient field.
94. The computer readable medium of claim 1 , wherein the act of deterrnining gradient information includes determining a gradient field vector for each of the plurality of voxels.
95. The computer readable medium of claim 94, wherein the act of determining at least one characteristic includes an act of comparing a magnitude of each of the plurality of gradient field vectors with a first and second threshold value.
96. The computer readable medium of claim 95, further comprising an act of grouping together each voxel having a magnitude greater than the first threshold value and less than the second threshold value to form a second segmented object.
97. The computer readable medium of claim 96, further comprising acts of: comparing a number of voxels in the second segmented object to a number of voxels in the first segmented object; and eliminating the second segmented object when the number of voxels in the second segmented object is less than a predβteπnined percentage of the number of voxels in the first segmented object.
AMENDED SHEET (ARTICLE 19) 51
98. The computer readable medium of claim 78, wherein the subject matter of interest is a sheet explosive.
99. The computer readable medium of claim 78, in combination with the at least one processor.
100. The combination of claim 99, in further combination with at least one X-ray scanning device, the at least one X-ray scanning device capable of providing image data to the at least one processor.
101. The combination of claim 100, wherein the at least one X-ray scanning device includes at least one X-ray computed tomography (CT) device, and wherein the at least one image includes at least one CT image.
102. The combination of claim 100, wherein the at least one X-ray scanning device includes at least one X-ray detection device adapted to scan baggage.
1 3. An apparatus adapted to detect a presence or an absence of subject matter of interest in an article of baggage from at least one image of the article of baggage, the apparatus comprising: at least one input adapted to receive the at least one image; and at least one controller, coupled to the at least one input, the at least one controller adapted to obtain gradient information of at least a portion of the at least one image, determine at least one characteristic of the gradient information, and determine whether the portion of the at least one image is associated with the subject matter of interest based on the at least one characteristic.
104. The apparatus of claim 103, wherein the at least one controller comprises means for obtaining gradient information of at least the portion of the at least one image, means for detemuning the at least one characteristic of the gradient information, and means for deteπnining whether the portion of the at least one image is associated with the subject matter of interest based on the at least one characteristic.
AMENDED SHEET (ARTICLE 19) 52
Applications Claiming Priority (2)
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|---|---|---|---|
| US48924703P | 2003-07-22 | 2003-07-22 | |
| US60/489,247 | 2003-07-22 |
Publications (3)
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| WO2005010561A2 WO2005010561A2 (en) | 2005-02-03 |
| WO2005010561A3 WO2005010561A3 (en) | 2005-06-16 |
| WO2005010561B1 true WO2005010561B1 (en) | 2005-08-11 |
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| PCT/US2004/023360 WO2005010561A2 (en) | 2003-07-22 | 2004-07-22 | Methods and apparatus for detecting objects in baggage using x-rays |
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| US (1) | US7613316B2 (en) |
| WO (1) | WO2005010561A2 (en) |
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-
2004
- 2004-07-22 WO PCT/US2004/023360 patent/WO2005010561A2/en active Application Filing
- 2004-07-22 US US10/896,753 patent/US7613316B2/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| US7613316B2 (en) | 2009-11-03 |
| US20050036689A1 (en) | 2005-02-17 |
| WO2005010561A3 (en) | 2005-06-16 |
| WO2005010561A2 (en) | 2005-02-03 |
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