Methods for locating and decoding distorted two-dimensional matrix symbols

• US 7,965,899 B2
• Filed: 07/03/2008
• Issued: 06/21/2011
• Est. Priority Date: 06/25/2005
• Status: Active Grant

• Current Assignee: Cognex Technology & Investment Corporation (Cognex Corporation)
• Original Assignee: Gognex Technology and Investment Corporation
• Inventors: Ye, Xiangyun, Nadabar, Sateesha
• Primary Examiner(s): Cunningham; Gregory F
• Application Number:
  US12/167,644
• Publication Number:
  US 20090003722A1
• Time in Patent Office: 1,083 Days
• Field of Search: 702/17, 702/69, 702/191, 702/195, 702/FOR.166
• US Class Current: 382/254
• CPC Class Codes: G06K 19/06037 multi-dimensional coding
• CPC Class Codes: G06V 10/34 Smoothing or thinning of the pattern; Morphological operations; Skeletonisation

Abstract

A method is presented for processing an image of a two-dimensional (2D) matrix symbol having a plurality of data modules and a discontinuous finder pattern, each distorted by “donut effects”. A resulting processed image contains an image of the 2D matrix symbol having a continuous finder pattern suitable for conventional 2D matrix symbol locating techniques, and having a plurality of data modules, each data module having a center more truly representative of intended data, and suitable for conventional 2D matrix symbol sampling and decoding. The method includes sharpening the distorted image of the 2D matrix symbol to increase a difference between low frequency and high frequency image feature magnitudes, thereby providing a sharpened image, and smoothing the sharpened image using a moving window over the sharpened image so as to provide a smoothed image, the moving window and a module of the 2D matrix code being of substantially similar size.

Claims

1. A method for processing an image of a two-dimensional (2D) matrix code having a plurality of data modules and a discontinuous finder pattern, each distorted by “

donut effects”

such that a resulting processed image contains an image of the 2D matrix code having a continuous finder pattern suitable for conventional data matrix locating techniques, and having a plurality of data modules, each data module having a center more truly representative of intended data, and suitable for conventional 2D matrix symbol sampling and decoding, the method comprising;

removing noise from the distorted image so as to provide a noise-reduced image with improved signal-to-noise-ratio;

sharpening the noise-reduced image so as to remove low frequency image features while preserving high frequency image features, thereby providing a high-pass filtered image;

amplifying the sharpened image so as to increase contrast in the sharpened image, thereby providing an amplified sharpened image; and

smoothing the amplified high-pass filtered image using a moving window over the high-pass filtered image so as to provide a smoothed image, the moving window and a module of the 2D matrix code being of substantially similar size.

2. The method of claim 1, further comprising:

repeating the method at different resolutions so as to expand operating range over a variety of module sizes.

3. The method of claim 1, wherein sharpening includes:

computing gradient magnitude at each pixel using a Sobel edge detector.

4. The method of claim 1, wherein smoothing the amplified sharpened image includes:

convolving the amplified sharpened image with a smoothing kernel.

5. A method for processing a distorted image of a two-dimensional (2D) matrix code having a plurality of data modules and a discontinuous finder pattern, each distorted by “

donut effects”

such that a resulting processed image contains an image of the 2D matrix code having a continuous finder pattern adapted for conventional data matrix locating techniques, and having a plurality of data modules, each data module having a center representative of intended polarity, and adapted for conventional 2D matrix symbol sampling and decoding, the method comprising;

removing noise from the distorted image so as to provide a noise-reduced image with improved signal-to-noise-ratio;

sharpening the noise-reduced image so as to remove low frequency image features while preserving high frequency image features, thereby providing a high-pass filtered sharpened image;

amplifying the high-pass filtered sharpened image so as to increase contrast in the high-pass filtered sharpened image, thereby providing an amplified sharpened image; and

smoothing the amplified sharpened image using a moving window over the amplified sharpened image so as to provide a smoothed image, the moving window and at least one of the plurality of data modules of the 2D matrix code being of substantially similar size.

6. The method of claim 5, further comprising:

repeating the method at different resolutions so as to expand operating range over a variety of data module sizes.

7. The method of claim 5, wherein sharpening includes:

computing gradient magnitude at each pixel using a Sobel edge detector.

8. The method of claim 5, wherein smoothing the amplified sharpened image includes:

convolving the amplified sharpened image with a smoothing kernel.

9. A method for processing a distorted image of a two-dimensional (2D) matrix symbol having a plurality of data modules and a discontinuous finder pattern, each distorted by “

donut effects”

such that a resulting processed image contains an image of the 2D matrix symbol having a continuous finder pattern adapted for conventional 2D matrix symbol locating techniques, and having a plurality of data modules, each data module having a center representative of intended polarity , and adapted for conventional 2D matrix symbol sampling and decoding, the method comprising;

sharpening the distorted image so as to remove low frequency image features while preserving high frequency image features, thereby providing a sharpened image;

smoothing the sharpened image using a moving window over the sharpened image so as to provide a smoothed image, the moving window and at least one of the plurality of data modules of the 2D matrix code being of substantially similar size.

10. The method of claim 9, further comprising:

removing noise from the distorted image so as to provide a noise-reduced image with improved signal-to-noise-ratio.

11. The method of claim 9, further comprising:

amplifying the sharpened image so as to increase contrast in the sharpened image, thereby providing an amplified sharpened image.

12. A method for processing a distorted image of a two-dimensional (2D) matrix symbol having a plurality of data modules and a discontinuous finder pattern, each distorted by “

donut effects”

such that a resulting processed image contains an image of the 2D matrix symbol having a continuous finder pattern adapted for conventional 2D matrix symbol locating techniques, and having a plurality of data modules, each data module having a center representative of intended polarity , and adapted for conventional 2D matrix symbol sampling and decoding, the method comprising;

sharpening a portion of the distorted image so as to remove low frequency image features while preserving high frequency image features, thereby providing a sharpened portion image;

amplifying the sharpened portion image so as to increase contrast in the sharpened portion image, thereby providing an amplified sharpened portion image;

repeating the sharpening step and the amplifying step until all portions of the distorted image have been sharpened and amplified, thereby providing an amplified sharpened image; and

smoothing the amplified sharpened image using a moving window over the amplified sharpened image so as to provide a smoothed image, the moving window and at least one of the plurality of data modules of the 2D matrix code being of substantially similar size.

13. The method of claim 12, wherein the moving window visits a plurality of pixel locations.

14. The method of claim 12, wherein the moving window visits a plurality of sub-pixel locations.

15. The method of claim 12, wherein sharpening includes:

using a Sobel gradient magnitude operator.

16. The method of claim 12, wherein sharpening includes:

using one of a Laplacian filter;

a Mexican hat filter;

a Roberts gradient magnitude operator;

a Difference-of-Gaussian filter;

a Rank Filter; and

a Top-hat filter.

17. The method of claim 12, wherein smoothing the amplified sharpened image using a moving window includes:

applying a weighted smoothing function within the moving window.

18. The method of claim 17, wherein the weighted smoothing function is a Gaussian function.

19. The method of claim 12, wherein smoothing the amplified sharpened image using a moving window includes using at least one of:

applying an average smoothing function within the moving window;

morphological dilation;

morphological dilation, followed by morphological erosion; and

Median filtering.

20. The method of claim 12, wherein the moving window is sized such that, at a center of a “

donut effect”

ring that is substantially equal in size to a data module size, a magnitude of the smoothed image at the center will be sufficient for correct sampling and decoding.