Software methodology for autonomous concealed object detection and threat assessment

1. A method comprising the steps of:

receiving image data from a millimeter wave imaging device comprising at least one scan of a scene,

enhancing the image data, using at least one computing device, using at least one enhancement method, thereby creating enhanced image data comprising a plurality of enhanced image pixels;

evaluating the enhanced image data, using the at least one computing device, using at least one identification method, thereby identifying at least one subject within the enhanced image data;

separating the at least one subject from a background within the enhanced image data, using the at least one computing device, using at least one separation method;

enhancing contrast of the at least one subject within the enhanced image data, using the at least one computing device, using at least one enhancement method, thereby creating enhanced contrast image data;

detecting concealed physical objects associated with the at least one subject within the enhanced contrast image data, using the computing device, using at least one detection method, thereby detecting at least one concealed physical object associated with the at least one subject;

displaying a representation of the at least one subject and the at least one concealed physical object on a display device.

2. The method of claim 1 wherein the at least one enhancement method comprises a dithering and sub-pixel resolution enhancement method.

3. The method of claim 2 wherein the dithering and sub-pixel resolution enhancement method comprises a 2-pixel average sub-pixel dither method.

4. The method of claim 3 wherein the image data comprises at least a first scan of the scene comprising a first plurality of pixels and at least a second scan of the scene comprising a second plurality of pixels, wherein the first plurality of image pixels overlaps the second plurality of image pixels by 1 h pixel in a horizontal and a vertical direction of the at least a first scan and the at least a second scan, wherein each enhanced image pixel comprises four sub-pixels, each sub-pixel representing the overlap of one of the first plurality of image pixels and one of the second plurality of image pixels, wherein each enhanced image sub-pixel is created from a 2-point average between the overlapping one of the first plurality of image pixels and one of the second plurality of image pixels.

5. The method of claim 2

wherein the image data comprises at least a first scan of the scene comprising a first plurality of pixels and at least a second scan of the scene comprising a second plurality of pixels,

wherein the first plurality of image pixels overlaps the second plurality of image pixels by 1 h pixel in a horizontal and a vertical direction of the at least a first scan and the at least a second scan,

wherein each enhanced image pixel comprises four sub-pixels, each subpixel representing the overlap of one of the first plurality of image pixels and one of the second plurality of image pixels, wherein the value of each sub-pixel of each of the plurality of enhanced image pixels is calculated as follows:

Let “A”,“B”, “C” and “D” represent four pixels of the at least a first scan arranged in a first 2 pixel×2 pixel pattern, wherein:

“A” is at a upper left corner of the pattern;

“B” is at a upper right corner of the pattern;

“C” is at a lower left corner of the pattern;

“D” is at a lower right corner of the pattern;

Let “a”,“b”, “c” and “d” represent four pixels of the at least a first scan arranged in a second 2 pixel×2 pixel pattern, wherein:

“a” is at a upper left corner of the pattern;

“b” is at a upper right corner of the pattern;

“c” is at a lower left corner of the pattern;

“d” is at a lower right corner of the pattern;

Let the first and second patterns overlap such that an upper left quadrant of “a” overlaps the lower right quadrant of “D”;

Let “Sub-pixel” represent the sub-pixel defined by the overlap of the upper left quadrant of “a” and the lower right quadrant of “D”;

Then, the value of the sub-pixel is computed as follows:

Sub-pixel= Y 2( a+D ).

6. The method of claim 5 wherein the value of the sub-pixel is computed as follows:

Sub-pixel=114 [a+D+ 114( B+D+a+b )+114( C+D+a+c )].

7. The method of claim 5 wherein the value of the sub-pixel is computed as follows:

Sub-pixel=114[6/4 a+ 6/4 D+ 114( B+b+C+c )].

8. The method of claim 5 wherein the value of the sub-pixel is computed as follows:

Sub-pixel=⅜( a+D )+ 1/16( B+b+C+c ).

9. The method of claim 5 wherein the value of the sub-pixel is computed as follows:

Sub-pixel=[6( a+D )+( B+b+C+c )]/16.

10. The method of claim 5 wherein the value of the sub-pixel is computed as follows:

Sub-pixel=( 7/18 a+ 3/18 b+ 3/18 c+ 1/18 d )+( 1/18 A+ 3/18 B+ 3/18 C+ 7/18 D ).

11. The method of claim 5 wherein the value of the sub-pixel is computed as follows:

Sub-pixel=(7 a+ 3 b+ 3 c+d+A+ 3 B+ 3 C+ 7 D )/18.

12. The method of claim 5 wherein the value of the sub-pixel is computed as follows:

Sub-pixel=[7( a+D )+3( b+c+B+C )+ d+A]/ 18.

13. The method of claim 1 wherein the at least one enhancement method is selected from the list: contrast enhancement, noise reduction, spatial range expansion, thresholding, convolutions, image filtering, Gaussian noise reduction and DWMTMF (Double Window Modified Trimmed Mean Filter).

14. The method of claim 1 wherein the at least one identification method is based on the size, intensity and distribution of data in the image data.

15. The method of claim 1 wherein the at least one identification method compares the image with an empty background image.

16. The method of claim 1 wherein the at least one identification method evaluates motion between successive scans of the scene.

17. The method of claim 1 wherein the at least one separation method comprises at least one of the following methods: connected graphs, contour following/prediction, topographical analysis and variational expectation maximization algorithms.

18. The method of claim 1 wherein the at least one separation method comprises at least one of the following methods: pixel level analysis, edge detection, and blob analysis.

19. The method of claim 1 wherein the at least one detection method detects the at least one concealed object by detecting a contrast within the image data relating to the at least one subject.

20. The method of claim 1 wherein the at least one detection method detects the at least one concealed object by detecting areas of greater pixel and lesser pixel value within the image data relating to the at least one subject.

21. The method of claim 1 wherein the at least one detection method detects the at least one concealed object by evaluating an edge of the at least one concealed object within the image data relating to the scene.

22. The method of claim 21 wherein the edge of the at least one concealed object within the image data relating to the scene is evaluated using one of the following techniques: Laplace convolutions, Laplace transforms, single gradient image processing, and double gradient image processing.

23. The method of claim 1 wherein the at least one detection method detects the at least one concealed object by evaluating the at least one concealed object texture, smoothness or spatial frequency with respect to the texture, smoothness or spatial frequency of the at least one subject.

24. The method of claim 1 wherein the at least one detection method detects the at least one concealed object by evaluating absolute pixel values of image data relating to the at least one concealed object.

25. The method of claim 1 wherein displaying a representation of the at least one subject and the at least one concealed physical object on a display device comprises a Blue Man display, wherein concealed objects are indicated using computer generated colored highlights overlaying a location of the at least one concealed object and indicating the size and severity of the at least one concealed object.

26. A computer-readable medium having computer-executable instructions for a method comprising the steps of:

receiving image data from a millimeter wave imaging device comprising at least one scan of a scene,

enhancing the image data, using at least one computing device, using at least one enhancement method, thereby creating enhanced image data comprising a plurality of enhanced image pixels;

evaluating the scene within the enhanced image data, using the at least one computing device, using at least one identification method, thereby identifying at least one subject within the scene;

separating the at least one subject from the scene background, using the at least one computing device using at least one separation method;

enhancing contrast of the at least one subject, using the at least one computing device, using at least one enhancement method, thereby creating enhanced contrast image data;

detecting concealed physical objects associated with the at least one subject within the enhanced contrast image data, using the computing device,

using at least one detection method, thereby detecting method at least one concealed object associated with the at least one subject;

displaying a representation of the at least one subject and the at least one concealed physical object on a display device.

27. The computer-readable medium of claim 26 wherein the at least one enhancement method comprises a dithering and sub-pixel resolution enhancement method.

28. The computer-readable medium of claim 27 wherein the dithering and sub-pixel resolution enhancement method comprises a 2-pixel average sub-pixel dither method.

29. The computer-readable medium of claim 28 wherein the image data comprises at least a first scan of the scene comprising a first plurality of pixels and at least a second scan of the scene comprising a second plurality of pixels, wherein the first plurality of image pixels overlaps the second plurality of image pixels by 1 h pixel in a horizontal and a vertical direction of the at least a first scan and the at least a second scan, wherein each enhanced image pixel comprises four sub-pixels, each sub-pixel representing the overlap of one of the first plurality of image pixels and one of the second plurality of image pixels, wherein each enhanced image subpixel is created from a 2-point average between the overlapping one of the first plurality of image pixels and one of the second plurality of image pixels.

30. The computer-readable medium of claim 27

wherein the image data comprises at least a first scan of the scene comprising a first plurality of pixels and at least a second scan of the scene comprising a second plurality of pixels,

wherein the first plurality of image pixels overlaps the second plurality of image pixels by 1 h pixel in a horizontal and a vertical direction of the at least a first scan and the at least a second scan,

wherein each enhanced image pixel comprises four sub-pixels, each subpixel representing the overlap of one of the first plurality of image pixels and one of the second plurality of image pixels, wherein the value of each sub-pixel of each of the plurality of enhanced image pixels is calculated as follows:

Let “A”,“B”, “C” and “D” represent four pixels of the at least a first scan arranged in a first 2 pixel×2 pixel pattern, wherein:

“A” is at a upper left corner of the pattern;

“B” is at a upper right corner of the pattern;

“C” is at a lower left corner of the pattern;

“D” is at a lower right corner of the pattern;

Let “a”,“b”, “c” and “d” represent four pixels of the at least a first scan arranged in a second 2 pixel×2 pixel pattern, wherein:

“a” is at a upper left corner of the pattern;

“b” is at a upper right corner of the pattern;

“c” is at a lower left corner of the pattern;

“d” is at a lower right corner of the pattern;

Let the first and second patterns overlap such that an upper left quadrant of “a” overlaps the lower right quadrant of “D”;

Let “Sub-pixel” represent the sub-pixel defined by the overlap of the upper left quadrant of “a” and the lower right quadrant of “D”;

Then, the value of the sub-pixel is computed as follows:

Sub-pixel=Y2( a+D ).

31. The computer-readable medium of claim 30 wherein the value of the sub-pixel is computed as follows:

Sub-pixel=114 [a+D+ 114( B+D+a+b )+114( C+D+a+c )].

32. The computer-readable medium of claim 30 wherein the value of the sub-pixel is computed as follows:

Sub-pixel=114[6/4 a+ 6/4 D+ 114( B+b+C+c )].

33. The computer-readable medium of claim 30 wherein the value of the sub-pixel is computed as follows:

Sub-pixel=⅜( a+D )+ 1/16( B+b+C+c ).

34. The computer-readable medium of claim 30 wherein the value of the sub-pixel is computed as follows:

Sub-pixel=[6( a+D )+( B+b+C+c )]/ 16 .

35. The computer-readable medium of claim 30 wherein the value of the sub-pixel is computed as follows:

Sub-pixel=( 7/18 a+ 3/18 b+ 3/18 c+ 1/18+( 1/18 A+ 3/18 B+ 3/18 C+ 7/18 D ).

36. The computer-readable medium of claim 30 wherein the value of the sub-pixel is computed as follows:

Sub-pixel=(7 a+ 3 b+ 3 c+d+A+ 3 B+ 3 C+ 7 D )/18.

37. The computer-readable medium of claim 30 wherein the value of the sub-pixel is computed as follows:

Sub-pixel[7( a+D )+3( b+c+B+C )+ d+A]/ 18.

38. The method of claim 26 wherein the at least one enhancement method is selected from the list: contrast enhancement, noise reduction, spatial range expansion, thresholding, convolutions, image filtering, Gaussian noise reduction and DWMTMF (Double Window Modified Trimmed Mean Filter).

39. The computer-readable medium of claim 26 wherein the at least one identification method is based on the size, intensity and distribution of data in the image data.

40. The computer-readable medium of claim 26 wherein the at least one identification method compares the image with an empty background image.

41. The computer-readable medium of claim 26 wherein the at least one identification method evaluates motion between successive scans of the scene.

42. The computer-readable medium of claim 26 wherein the at least one separation method comprises at least one of the following methods: connected graphs, contour following/prediction, topographical analysis and variational expectation maximization algorithms.

43. The computer-readable medium of claim 26 wherein the at least one separation method comprises at least one of the following methods: pixel level analysis, edge detection, and blob analysis.

44. The computer-readable medium of claim 26 wherein the at least one detection method detects the at least one concealed object by detecting a contrast within the image data relating to the at least one subject.

45. The computer-readable medium of claim 26 wherein the at least one detection method detects the at least one concealed object by detecting areas of greater pixel and lesser pixel value within the image data relating to the at least one subject.

46. The computer-readable medium of claim 26 wherein the at least one detection method detects the at least one concealed object by evaluating an edge of the at least one concealed object within the image data relating to the scene.

47. The computer-readable medium of claim 46 wherein the edge of the at least one concealed object within the image data relating to the scene is evaluated using one of the following techniques: Laplace convolutions, Laplace transforms, single gradient image processing, and double gradient image processing.

48. The computer-readable medium of claim 26 wherein the at least one detection method detects the at least one concealed object by evaluating the at least one concealed object texture, smoothness or spatial frequency with respect to the texture, smoothness or spatial frequency of the at least one subject.

49. The computer-readable medium of claim 26 wherein the at least one detection method detects the at least one concealed object by evaluating absolute pixel values of image data relating to the at least one concealed object.

50. The computer-readable medium of claim 26 wherein displaying a representation of the at least one subject and the at least one concealed physical object on a display device comprises a Blue Man display, wherein concealed objects are indicated using computer generated colored highlights overlaying a location of the at least one concealed object and indicating the size and severity of the at least one concealed object.