IMAGE ANALYSIS SYSTEMS HAVING IMAGE SHARPENING CAPABILITIES AND METHODS USING SAME

• US 20130188878A1
• Filed: 11/08/2012
• Published: 07/25/2013
• Est. Priority Date: 07/20/2010
• Status: Abandoned Application

• Current Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Original Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Inventors: KACENJAR, Steve T.
• Application Number:
  US13/672,530
• Publication Number:
  US 20130188878A1
• Time in Patent Office: Days
• US Class Current: 382/209
• CPC Class Codes: G06T 2207/20021 Dividing image into blocks, subimages or windows
• CPC Class Codes: G06T 2207/30088 Skin; Dermal
• CPC Class Codes: G06T 3/14 Transformations for image registration, e.g. adjusting or mapping for alignment of images
• CPC Class Codes: G06T 5/73 Deblurring; Sharpening
• CPC Class Codes: G06T 7/0016 involving temporal comparison
• CPC Class Codes: G06T 7/30 Determination of transform parameters for the alignment of images, i.e. image registration

Abstract

Described herein are image analysis systems that utilize a non-linear data processing algorithm for overlaying and comparing time sequence images. The image analysis system and method also sharpens at least one of the time sequence images during the process to improve image registration accuracy.

Claims

1. An image analysis system comprising:

at least one image collection device;

an image processing device operable to

overlay a test image and a reference image upon one another and perform a comparison therebetween; and

sharpen at least one of the test image and the reference image; and

at least one data output device for outputting data related to the comparison of the test image and the reference image.

2. The image analysis system of claim 1, wherein the image processing device operates a non-linear data processing algorithm.

3. The image analysis system of claim 1, wherein the image processing device operates a non-linear data processing algorithm, the image analysis being selected from the group including a particle swarm optimizer, a neural network, a genetic algorithm, and any combination thereof.

4. The image analysis system of claim 1, wherein the image processing device processes both linear parameters and non-linear parameters in overlaying the test image and the reference image.

5. The image analysis system of claim 4, wherein the linear parameters and the nonlinear parameters are selected from the group consisting of x-translation relative to the reference image, y-translation relative to the reference image, image rotation relative to the reference image, shear of the at least one image collection device, image magnification relative to the reference image, image tone relative to the reference image, image gain relative to the reference image, and any combination thereof.

6. The image analysis system of claim 4, wherein each image contains a plurality of inclusions and the image processing device is operable for subdividing each image into a plurality of sectors and determining a set of mapping coefficients for each of the plurality of sectors.

7. The image analysis system of claim 6, wherein the image processing device is operable to iteratively minimize an objective function for each of the plurality of sectors within the test image;

wherein the objective function is selected from the group consisting of image entropy, hamming distance, gray level per band, and any combination thereof.

8. The image analysis system of claim 6, wherein the image processing device is operable to deform each sector in the test image onto a corresponding sector in the reference image after determining the set of mapping coefficients.

9. The image analysis system of claim 8, wherein each sector is deformed using an Affine transformation.

10. The image analysis system of claim 8, wherein each sector is deformed using a Perspective transformation.

11. The image analysis system of claim 6, wherein the image processing device processes the linear parameters prior to processing the non-linear parameters.

12. The image analysis system of claim 11, wherein processing of the linear parameters provides an estimated set of mapping coefficients for each sector, prior to processing of the non-linear parameters by the non-linear data processing algorithm.

13. The image analysis system of claim 1, the image processing device includes a microprocessor.

14. The image analysis system of claim 1, wherein the at least one image collection device comprises a camera.

15. The image analysis system of claim 1, wherein the at least one image collection device is selected from the group consisting of a camera, a confocal microscope, a magnetic sensing device, a hyperspectral sensing device, a multispectral sensing device, a thermal sensing device, a polarimetric sensing device, a radiometric sensing device, and any combination thereof.

16. A method comprising:

acquiring a reference image containing a plurality of inclusions;

after acquiring the reference image, acquiring a test image containing at least some of the plurality of inclusions;

sharpening at least one of the reference image and the test image;

overlaying the test image upon the reference image by using a non-linear data processing algorithm; and

producing an output that illustrates any differences between the test image and the reference image after overlaying takes place.

17. The method of claim 16, wherein the non-linear data processing algorithm is selected from the group consisting of a particle swarm optimizer, a neural network, a genetic algorithm, and any combination thereof.

18. The method of claim 16, wherein the non-linear data processing algorithm comprises a particle swarm optimizer.

19. The method of claim 16, wherein the plurality of inclusions are located on a deformable surface.

20. The method of claim 16, further comprising prior to using the non-linear data processing algorithm, dividing the reference image and the test image into a plurality of sectors.

21. The method of claim 20, further comprising prior to using the non-linear data processing algorithm, performing a coarse alignment of the sectors in the test image upon the corresponding sectors in the reference image.

22. The method of claim 21, wherein both linear parameters and non-linear parameters are used to overlay the sectors in the test image upon the corresponding sectors in the reference image.

23. The method of claim 22, wherein the linear parameters are processed prior to the non-linear parameters.

24. The method of claim 23, wherein only the non-linear parameters are processed using the non-linear data processing algorithm.

25. The method of claim 24, wherein the linear parameters and the non-linear parameters selected from the group consisting of x-translation relative to the reference image, y-translation relative to the reference image, image rotation relative to the reference image, shear of the image collection device, image magnification relative to the reference image, image tone relative to the reference image, image gain relative to the reference image, and any combination thereof.

26. The method of claim 25, further comprising determining a set of mapping coefficients for each of the plurality of sectors in the test image.

27. The method of claim 26, wherein overlaying is performed iteratively until a desired degree of convergence is reached.

28. The method of claim 27, wherein the desired degree of convergence is based upon a minimization of an objective function for each of the plurality of sectors within the test image;

wherein the objective function is selected from the group consisting of image entropy, hamming distance, gray level per band, and any combination thereof.

29. The method of claim 26, wherein overlaying is performed iteratively for a fixed number of cycles.

30. The method of claim 20, further comprising:

after overlaying using the non-linear data processing algorithm, deforming each sector in the test image onto a corresponding sector in the reference image.

31. A machine-readable medium providing instructions that, when executed by a machine, cause the machine to perform operations comprising:

acquiring a reference image containing a plurality of inclusions;

acquiring a test image containing at least some of the plurality of inclusions;

sharpening at least one of the reference image and the test image;

overlaying the test image and the reference image by using a non-linear data processing algorithm; and

producing an output that illustrates any differences between the test image and the reference image after overlaying takes place.

32. The machine-readable medium of claim 31, wherein overlaying the test image and the reference image by using a non-linear data processing algorithm includes use of a particle swarm optimizer.

33. The machine-readable medium of claim 32, wherein overlaying the test image and the reference image by using a non-linear data processing algorithm includes use of a neural network, a genetic algorithm, and any combination thereof.

34. The machine-readable medium of claim 33, wherein overlaying the test image and the reference image by using a non-linear data processing algorithm includes use of a genetic algorithm.

35. The machine-readable medium of claim 32, wherein overlaying the test image and the reference image by using a non-linear data processing algorithm includes use of a genetic algorithm.