Object segmentation at a self-checkout

1. A method for segmenting an object at a self-checkout, wherein the method comprises:

capturing an image of an object at a self-checkout, said capturing carried out by a distinct module executing on a hardware processor;

dividing the image into one or more blocks, said dividing carried out by a distinct module executing on a hardware processor;

computing one or more features of the image, said computing carried out by a distinct module executing on a hardware processor;

computing a confidence value for each of the one or more blocks, wherein computing a confidence value for each of the one or more blocks comprises using a minimum feature distance from one or more reference background blocks, said computing carried out by a distinct module executing on a hardware processor; and

eliminating one or more blocks from consideration via use of an adaptive threshold computed on the confidence value for each of the one or more blocks, wherein the one or more blocks remaining map to a region of the image containing the object, said eliminating carried out by a distinct module executing on a hardware processor.

2. The method of claim 1 , further comprising:

reading a barcode for the object at the self-checkout; and

using a database to identify one or more known features corresponding to the barcode.

3. The method of claim 2 , further comprising comparing the one or more computed features of the image with the one or more known features corresponding to the barcode to determine whether or not the image matches the object's barcode identity.

4. The method of claim 1 , further comprising creating and maintaining a set of one or more reference background images.

5. The method of claim 4 , wherein the set of one or more background reference images cover one or more visual variations of the self-checkout.

6. The method of claim 4 , wherein creating and maintaining a set of one or more reference background images comprises creating and maintaining a set of one or more reference background images over two or more different time periods.

7. The method of claim 4 , further comprising dividing each reference background image into a set of one or more non-overlapping blocks.

8. The method of claim 7 , further comprising extracting one or more features from each of the one or more non-overlapping blocks.

9. The method of claim 1 , further comprising using the confidence value of each block to weight a color histogram used for object classification.

10. The method of claim 1 , wherein capturing an image of an object at a self-checkout comprises capturing, via a camera, an image of an object as it moves across an open conveyor belt of a self-checkout.

11. The method of claim 1 , wherein eliminating one or more blocks from consideration via use of an adaptive threshold computed on the confidence value for each of the one or more blocks comprises iteratively eliminating one or more blocks from consideration via use of an adaptive threshold computed on the confidence value for each of the one or more blocks.

12. The method of claim 1 , wherein the one or more features of the image comprise a histogram.

13. The method of claim 1 , further comprising providing a system, wherein the system comprises one or more distinct software modules, each of the one or more distinct software modules being embodied on a tangible computer-readable recordable storage medium, and wherein the one or more distinct software modules comprise an image acquisition module and a segmentation module executing on a hardware processor.

14. A computer program product comprising a tangible computer readable recordable storage device including computer useable program code for segmenting an object at a self-checkout, the computer program product including:

computer useable program code for capturing an image of an object at a self-checkout;

computer useable program code for dividing the image into one or more blocks;

computer useable program code for computing one or more features of the image;

computer useable program code for computing a confidence value for each of the one or more blocks, wherein computing a confidence value for each of the one or more blocks comprises using a minimum feature distance from one or more reference background blocks; and

computer useable program code for eliminating one or more blocks from consideration via use of an adaptive threshold computed on the confidence value for each of the one or more blocks, wherein the one or more blocks remaining map to a region of the image containing the object.

15. The computer program product of claim 14 , further comprising:

computer useable program code for reading a barcode for the object at the self-checkout; and

computer useable program code for using a database to identify one or more known features corresponding to the barcode.

16. The computer program product of claim 15 , further comprising computer useable program code for comparing the one or more computed features of the image with the one or more known features corresponding to the barcode to determine whether or not the image matches the object's barcode identity.

17. The computer program product of claim 14 , further comprising computer useable program code for creating and maintaining a set of one or more reference background images.

18. The computer program product of claim 17 , wherein the computer useable program code for creating and maintaining a set of one or more reference background images comprises computer useable program code for creating and maintaining a set of one or more reference background images over two or more different time periods.

19. The computer program product of claim 14 , further comprising computer useable program code for using the confidence value of each block to weight a color histogram used for object classification.

20. A system for segmenting an object at a self-checkout, comprising:

a memory; and

at least one processor coupled to the memory and operative to:

capture an image of an object at a self-checkout;

divide the image into one or more blocks;

compute one or more features of the image;

compute a confidence value for each of the one or more blocks, wherein computing a confidence value for each of the one or more blocks comprises using a minimum feature distance from one or more reference background blocks; and

eliminate one or more blocks from consideration via use of an adaptive threshold computed on the confidence value for each of the one or more blocks, wherein the one or more blocks remaining map to a region of the image containing the object.

21. The system of claim 20 , wherein the at least one processor coupled to the memory is further operative to:

read a barcode for the object at the self-checkout; and

use a database to identify one or more known features corresponding to the barcode.

22. The system of claim 21 , wherein the at least one processor coupled to the memory is further operative to compare the one or more computed features of the image with the one or more known features corresponding to the barcode to determine whether or not the image matches the object's barcode identity.

23. The system of claim 20 , wherein the at least one processor coupled to the memory is further operative to create and maintain a set of one or more reference background images.

24. The system of claim 20 , wherein the at least one processor coupled to the memory is further operative to use the confidence value of each block to weight a color histogram used for object classification.

25. An apparatus for segmenting an object at a self-checkout, the apparatus comprising:

means for capturing an image of an object at a self-checkout;

means for dividing the image into one or more blocks;

means for computing one or more features of the image;

means for computing a confidence value for each of the one or more blocks, wherein computing a confidence value for each of the one or more blocks comprises using a minimum feature distance from one or more reference background blocks; and

means for eliminating one or more blocks from consideration via use of an adaptive threshold computed on the confidence value for each of the one or more blocks, wherein the one or more blocks remaining map to a region of the image containing the object.