Method of creating a divergence transform for a class of objects

• US 20060269161A1
• Filed: 03/14/2006
• Published: 11/30/2006
• Est. Priority Date: 03/15/2005
• Status: Abandoned Application

• Current Assignee: Guardian Technologies LLC (Lasko Holdings, Inc.)
• Original Assignee: Guardian Technologies LLC (Lasko Holdings, Inc.)
• Inventors: Andrushchenko, Oleksandr, Fesenko, Anatoliy, Ramsay, Thomas E., Ramsay, Eugene B., Hamilton, Victor, Richard, Martin, Felteau, Gerard
• Application Number:
  US11/373,908
• Publication Number:
  US 20060269161A1
• Time in Patent Office: Days
• US Class Current: 382/276
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06T 2207/10116 X-ray image
• CPC Class Codes: G06T 2207/30112 Baggage; Luggage; Suitcase
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/143 involving probabilistic approaches, e.g. Markov random field [MRF] modelling
• CPC Class Codes: G06T 7/174 involving the use of two or more images

Abstract

A system and method for identifying objects of interest in image data is provided. The present invention utilizes principles of Iterative Transformational Divergence in which objects in images, when subjected to special transformations, will exhibit radically different responses based on the physical, chemical, or numerical properties of the object or its representation (such as images), combined with machine learning capabilities. Using the system and methods of the present invention, certain objects that appear indistinguishable from other objects to the eye or computer recognition systems, or are otherwise almost identical, generate radically different and statistically significant differences in the image describers (metrics) that can be easily measured.

Claims

1. A method of creating a divergence transform for a class of objects, comprising:

(1) selecting a point operation;

(2) performing the point operation on a subset of images, wherein the subset of images comprises at least one image containing an object in the class of objects; and

(3) repeating steps (1) and (2) until a resulting point operation bifurcates the object, whereby the resulting point operation corresponds to the divergence transform.

2. The method of claim 1, wherein the point operation is at least partially a non-linear point operation.

3. The method of claim 1, wherein the point operation comprises at least one nodal point.

4. The method of claim 3, wherein the at least one nodal point is set so as to effect the bifurcation of the object.

5. The method of claim 3, wherein the nodal point is set so as to reduce background clutter in at least some of the images in the subset of images.

6. The method of claim 1, wherein the resulting point operation is adapted to maintain an integrity of the object when the resulting point operation bifurcates the object.

7. The method of claim 1, wherein the images comprise nonparametric image data.

8. The method of claim 1, wherein the images comprise two-dimensional image data.

9. The method of claim 1, wherein the images comprise three-dimensional image data.

10. The method of claim 1, wherein the images comprise x-ray image data.

11. A method of creating a series of divergence transforms for a class of objects, comprising:

(1) selecting a series of point operations;

(2) performing the series of point operations sequentially on a subset of images, wherein the subset of images comprises at least one image containing an object in the class of objects; and

(3) repeating steps (1) and (2) until a resulting series of point operations bifurcates the object, whereby the resulting series of point operations correspond to the series of divergence transforms.

12. The method of claim 11, wherein the series of point operations are at least partially non-linear point operations.

13. The method of claim 11, wherein each of the point operations in the series of point operations comprises at least one nodal point.

14. The method of claim 13, wherein the nodal points are set so as to effect the bifurcation of the object.

15. The method of claim 13, wherein the nodal points are set so as to reduce background clutter in at least some of the images in the subset of images.

16. The method of claim 11, wherein the resulting series of point operations are adapted to maintain an integrity of the object when the resulting series of point operations bifurcates the object.

17. The method of claim 11, wherein the images comprise nonparametric image data.

18. The method of claim 11, wherein the images comprise two-dimensional image data.

19. The method of claim 11, wherein the images comprise three-dimensional image data.

20. The method of claim 11, wherein the images comprise x-ray image data.

21. A method of creating a divergence transform for a class of objects of interest, comprising:

(1) assimilating a database of images, wherein at least some of the images contain an object in the class of objects;

(2) selecting a point operation;

(3) performing the point operation on a subset of images, wherein the subset of images comprises at least one image containing an object in the class of objects; and

(4) repeating steps (2) and (3) until a resulting point operation bifurcates the object, whereby the resulting point operation corresponds to the divergence transform.

22. The method of claim 21, wherein the point operation is at least partially a non-linear point operation.

23. The method of claim 21, wherein the point operation comprises at least one nodal point.

24. The method of claim 23, wherein the at least one nodal point is set so as to effect the bifurcation of the object.

25. The method of claim 23, wherein the nodal point is set so as to reduce background clutter in at least some of the images in the subset of images.

26. The method of claim 21, wherein the resulting point operation is adapted to maintain an integrity of the object when the resulting point operation bifurcates the object.

27. The method of claim 21, wherein the images comprise nonparametric image data.

28. The method of claim 21, wherein the images comprise two-dimensional image data.

29. The method of claim 21, wherein the images comprise three-dimensional image data.

30. The method of claim 21, wherein the images comprise x-ray image data.

31. A method of creating a series of divergence transforms for a class of objects of interest, comprising:

(1) assimilating a database of images, wherein at least some of the images contain an object in the class of objects;

(2) selecting a series of point operations;

(3) performing the series of point operations sequentially on a subset of images, wherein the subset of images comprises at least one image containing an object in the class of objects; and

(4) repeating steps (2) and (3) until a resulting series of point operations bifurcates the object, whereby the resulting series of point operations correspond to the series of divergence transforms.

32. The method of claim 31, wherein the series of point operations are at least partially non-linear point operations.

33. The method of claim 31, wherein each of the point operations in the series of point operations comprises at least one nodal point.

34. The method of claim 33, wherein the nodal points are set so as to effect the bifurcation of the object.

35. The method of claim 33, wherein the nodal points are set so as to reduce background clutter in at least some of the images in the subset of images.

36. The method of claim 31, wherein the resulting series of point operations are adapted to maintain an integrity of the object when the resulting series of point operations bifurcates the object.

37. The method of claim 31, wherein the images comprise nonparametric image data.

38. The method of claim 31, wherein the images comprise two-dimensional image data.

39. The method of claim 31, wherein the images comprise three-dimensional image data.

40. The method of claim 31, wherein the images comprise x-ray image data.