METHOD AND DEVICE FOR PARALLEL PROCESSING OF IMAGES

• US 20130251265A1
• Filed: 12/15/2011
• Published: 09/26/2013
• Est. Priority Date: 12/17/2010
• Status: Active Grant

• Current Assignee: Ivisys Sweden AB
• Original Assignee: Ivisys APS
• Inventors: Chehaiber, Moatasem
• Granted Patent: US 9,020,297 B2
• Time in Patent Office: Days
• US Class Current: 382/190
• CPC Class Codes: G06T 2207/20016 Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
• CPC Class Codes: G06T 2207/20224 Image subtraction
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06V 10/462 Salient features, e.g. scale invariant feature transforms [SIFT]
• CPC Class Codes: G06V 10/94 Hardware or software architectures specially adapted for image or video understanding

Abstract

The present invention relates to the parallel calculation of convoluted data. In particular, the invention relates to Gaussian pyramid construction and parallel processing of image data, such as parallel calculation of repeatedly convoluted data for use in a SIFT algorithm. This is achieved by providing a method for obtaining a plurality of difference images from an original image defined by a plurality of pixels, said method comprising: Providing a plurality of blurring convolution functions, each of said blurring functions providing increasing degree of blurring of an original image upon convolution of said original image; establishing a plurality of difference convolution functions, Dif, by calculating the difference between two of said blurring convolution functions, each of said two blurring convolution functions providing different degrees of blurring of an original image upon convolution of said original image; and calculating a plurality of difference images from said original image, by convolving each of said difference convolution functions, Dif, with said original image to obtain difference images.

Claims

1. A method for obtaining a plurality of difference images from an original image defined by a plurality of pixels, said method comprising:

a. Providing a plurality of blurring convolution functions, each of said blurring functions providing increasing degree of blurring of an original image upon convolution of said original image;

b. Establishing a plurality of difference convolution functions, Dif, by calculating the difference between two of said blurring convolution functions, each of said two blurring convolution functions providing different degrees of blurring of said original image upon convolution of said original image; and

c. Calculating a plurality of difference images from said original image, by convolving each of said difference convolution functions, Dif, with said original image to obtain difference images.

2. The method according to claim 1, wherein said plurality of difference images are computed in parallel.

3. The method according to claim 1, further comprising identifying scale invariant features in said original image.

4. The method according to claim 1, wherein said plurality of blurring convolution functions are established once and repeatedly used for subsequent calculation of said difference images.

5. The method according to claim 1, wherein said blurring convolution functions are Gaussian.

6. The method according to claim 1, wherein said difference convolution function, Dif, is approximated by a polynomial.

7. A method for obtaining a first plurality of difference images and at least one subsequent plurality of difference images, said method comprising:

a. Obtaining the first plurality of difference images according to claim 1, from an original image, using a plurality of blurring convolution functions;

b. Convolving said plurality of blurring convolution functions with a down sampling function and an anti-aliasing function to obtain a down sampling anti-aliasing convolution function, DAcon;

c. Convolving said down sampling anti-aliasing convolution function, DAcon, with the original image to obtain a subsequent image; and

d. Obtaining a subsequent plurality of difference images according to claim 1 from the subsequent image.

8. The method according to claim 7, wherein subsequent pluralities of difference images are calculated directly from the original image by convolving with additional blurring, down sampling and anti-aliasing convolution functions.

9. A method for obtaining a first plurality of difference images and at least one subsequent plurality of difference images, said method comprising:

a. Obtaining the first plurality of difference images according to claim 1, from an original image, using a plurality of blurring convolution functions;

b. Down sampling a blurred, original image, to obtain a first image.

c. Convolving said plurality of blurring convolution functions with an anti-aliasing function to obtain a anti-aliasing convolution function, Aeon;

d. Convolving the obtained anti-aliasing convolution function, Aeon, with the first image to obtain a subsequent image; and

e. Obtaining a subsequent plurality of difference images according to claim 1 from the subsequent.

10. A method for obtaining a first plurality of difference images and a number, n, of subsequent plurality of difference images, said method comprising:

a. Obtaining said first plurality of difference images according to claim 1, from an original image, using a plurality of blurring convolution functions;

b. Obtaining a first down sampling anti-aliasing convolution function, DAcon(I), by convolving said plurality of blurring convolution functions with a down sampling function and an anti-aliasing function;

c. When n is larger than 1, obtaining additional down sampling anti-aliasing convolution function(s), DAcon(t), by convolving a previous down sampling antialiasing convolution function, DAcon(t−

I), with said plurality of blurring functions, a down sampling function and an anti-aliasing function, beginning this step c with t=2, and repeating this step while increasing t with unitary increments until t reaches n;

d. Convolving all of the down sampling anti-aliasing convolution functions, DAcon, with said original image to obtain an image(x) for each down sampling antialiasing convolution function DAcon(x);

e. Obtaining the n subsequent pluralities of difference images, according to claim 1, obtaining each plurality(x) from each image(x).

11. A method for identifying image features which are stable across different scales in an image defined by a plurality of pixels, wherein multiple filtering steps and re-sampling steps may be performed in parallel, the method comprising:

a. Obtaining multiple difference images according to claim 1;

b. Locating pixel amplitude extrema in said multiple difference images;

c. Producing a pixel gradient vector for each pixel in each difference image;

d. Associating vector orientations with respective actual maximal and minimal amplitude pixels associated with each difference image.

12. The method according to claim 11, wherein said multiple filtering steps are Gaussian filtering steps.

22. The method according to claim 7, further comprising using the subsequent image as the original image and repeating steps c and d for obtaining additional subsequent pluralities of difference images.

23. The method according to claim 9, further comprising denoting the subsequent image the original image and repeating steps b, c and d for obtaining additional subsequent pluralities of difference images.

13. An image processing device for obtaining a plurality of difference images from an original image defined by a plurality of pixels, said device comprising means for receiving data in respect of said first image, means for transmitting or storing said difference images, and processing means, configured to:

Calculate a plurality of difference images from an original image, by convolving each of a plurality of difference convolution functions, Dif, with said original image to obtain difference images;

wherein said plurality of difference convolution functions, Dif, have been established by calculating a difference between two blurring convolution functions, each of said two blurring convolution functions providing different degrees of blurring of said original image upon convolution of said original image.

15. The image processing device according to claim 13, wherein said processor circuit comprises a chip.

16. The image processing device according to claim 13, said image processing device comprising multiple chips, each chip configured to calculate at least one difference image.

17. The image processing device according to claim 13, wherein said image processing device comprises a bilinear interpolator, said bilinear interpolator comprising a multiply and accumulate unit (MAC), a delay line adder and a control logic block.

24. The image processing device according to claim 15, wherein said chip comprises a CPU.

14. (canceled)

18. (canceled)

19. A computer program product comprising program code means stored on a computer readable medium for:

a. Providing a plurality of blurring convolution functions, each of said blurring functions providing increasing degree of blurring of an original image upon convolution of said original image;

b. Establishing a plurality of difference convolution functions, Dif, by calculating the difference between two of said blurring convolution functions, each of said two blurring convolution functions providing different degrees of blurring of said original image upon convolution of said original image; and

c. Calculating a plurality of difference images from said original image, by convolving each of said difference convolution functions, Dif, with said original image to obtain difference images, when said program product is run on a computer.

20-21. -21. (canceled)