Identifying embedded data in an image

• US 8,243,981 B2
• Filed: 11/26/2007
• Issued: 08/14/2012
• Est. Priority Date: 11/26/2007
• Status: Active Grant

• Current Assignee: Seiko Epson Corporation (Seiko Group)
• Original Assignee: Seiko Epson Corporation (Seiko Group)
• Inventors: Zandifar, Ali, Liu, Che-Bin
• Primary Examiner(s): Tabatabai, Abolfazl
• Application Number:
  US11/945,139
• Publication Number:
  US 20090136080A1
• Time in Patent Office: 1,723 Days
• Field of Search: 382/100, 382/164, 382/173, 382/190, 380/51, 380/54, 380/252, 380/287, 370522-529, 283/72, 283 74- 85, 358/3.28
• US Class Current: 382/100
• CPC Class Codes: G06T 1/0028 Adaptive watermarking, e.g. Human Visual System [HVS]-based watermarking
• CPC Class Codes: G06T 1/005 Robust watermarking, e.g. average attack or collusion attack resistant
• CPC Class Codes: G06T 2201/0051 Embedding of the watermark in the spatial domain
• CPC Class Codes: G06T 2201/0065 Extraction of an embedded watermark; Reliable detection
• CPC Class Codes: H04N 1/32208 involving changing the magnitude of selected pixels, e.g. overlay of information or super-imposition H04N1/32219, H04N1/32224 take precedence
• CPC Class Codes: H04N 1/32229 with selective or adaptive application of the additional information, e.g. in selected regions of the image H04N1/32213 takes precedence
• CPC Class Codes: H04N 1/32251 in multilevel data, e.g. greyscale or continuous tone data

Abstract

Disclosed are embodiments of systems and methods for embedding and/or extracting data from images. In embodiments, an image may be segmented into regions, and characters or other image groups within a segmented region may be determined to be embedding sites. A data vector may be embedded into a set of corresponding ordered embedding sites by representing each data element as different intensity values assigned to the pixels within one portion of an embedding site relative to the pixels in another portion of the embedding site. In embodiments, embedded data may be extracted from an image by extracting and decoding a set of bit values from a set of identified and ordered embedding sites.

Claims

1. A method for determining a robust average intensity of pixels from a portion of an image comprising pixels, the method comprising:

computing a cumulative density function based upon an intensity histogram of the pixels from the portion of the image;

using the cumulative density function to identify pixels to exclude;

calculating a robust average intensity using at least some of the pixels that were not excluded;

using the robust average intensity to determine an embed pattern in a candidate embedding site comprising pixels; and

using the embed pattern to extract a data value from the candidate embedding site.

2. The method of claim 1 wherein the step of using the cumulative density function to identify pixels to exclude comprises:

excluding the pixels having an intensity corresponding to a cumulative density function that is below a first cumulative density function threshold value; and

excluding the pixels having an intensity corresponding to a cumulative density function that is above a second cumulative density function threshold value.

3. The method of claim 1 wherein the robust average intensity is a first mean intensity of the at least some of the pixels that were not excluded.

4. The method of claim 3 wherein the robust average intensity is a second mean intensity and the step of calculating a robust average intensity using at least some of the pixels that were not excluded further comprises:

excluding the pixels having an intensity value greater than the first mean intensity value; and

calculating the second mean intensity using at least some of the pixels that were not excluded.

5. The method of claim 1 wherein using the robust average intensity to determine an embed pattern in a candidate embedding site comprises:

calculating a difference between a first robust average intensity value of pixels in a first portion of the candidate embedding site and a second robust average intensity value of pixels in a second portion of the candidate embedding site; and

responsive to the difference between the first robust average intensity value and the second robust average intensity value being greater than a threshold, determine the embed pattern by determining which robust average intensity value is greater than the other robust average intensity value.

6. The method of claim 5 wherein the step of using the embed pattern to extract a data value from the candidate embedding site comprises correlating the embed pattern with the data value.

7. A computer readable medium having instructions for performing the method of claim 1.

8. A method for extracting a data vector from an image comprising pixels wherein the data vector comprises data values, the method comprising:

identifying an embed pattern in a candidate embedding site in the image wherein an embed pattern comprises a difference between a robust average intensity of pixels from a first portion of the candidate embedding site and a robust average intensity of pixels from a second portion of the candidate embedding site;

correlating the embed pattern with a data value;

extracting a set of data values from a set of ordered candidate embedding sites in the image; and

extracting at least one data vector by segmenting the set of data values.

9. The method of claim 8 wherein the step of identifying an embed pattern in a candidate embedding site in the image comprises:

responsive to the difference between the first robust average intensity value and the second robust average intensity value being greater than a threshold, identify the embed pattern by determining which robust average intensity value is greater than the other robust average intensity value.

10. The method of claim 9 wherein the robust average intensity is a first mean intensity of the at least some of the pixels that were not excluded.

11. The method of claim 10 wherein the robust average intensity is a second mean intensity and the step of calculating a robust average intensity using at least some of the pixels that were not excluded further comprises:

excluding the pixels having an intensity value greater than the first mean intensity value; and

calculating the second mean intensity using at least some of the pixels that were not excluded.

12. The method of claim 8 wherein calculating a robust average intensity comprises:

computing a cumulative density function based upon an intensity histogram of the pixels from a portion of the image;

using the cumulative density function to identify pixels to exclude; and

calculating a robust average intensity using at least some of the pixels that were not excluded.

13. The method of claim 12 wherein the step of using the cumulative density function to identify pixels to exclude comprises:

excluding the pixels having an intensity corresponding to a cumulative density function that is below a first cumulative density function threshold value; and

excluding the pixels having an intensity corresponding to a cumulative density function that is above a second cumulative density function threshold value.

14. The method of claim 8 wherein identifying a set of ordered candidate embedding sites in the image comprises:

segmenting the image into regions;

dilating pixels from one of the regions;

identifying a plurality of connected components from a set of pixels selected from the region with dilated pixels;

selecting a set of candidate embedding sites wherein a candidate embedding site comprises at least one connected component is selected from the plurality of connected components; and

assigning an order to the candidate embedding sites within the set of candidate embedding sites.

15. A computer readable medium having instructions for performing the method of claim 8.

16. A system for extracting a data vector from an image comprising pixels wherein the data vector comprises data values, the system comprising:

an embedding site ordering processor, coupled to receive a set of candidate embedding sites from the image, that orders at least some candidate embedding sites from the set of candidate embedding sites to form a set of ordered candidate embedding sites;

a bitstream extractor, coupled to receive the set of ordered candidate embedding sites, that extracts a set of data values from at least some of the set of ordered candidate embedding sites wherein a data value is extracted from an ordered candidate embedding site by examining a difference between a first robust average intensity value of the pixels in a first portion of the ordered candidate embedding site and a second robust average intensity value of the pixels in a second portion of the ordered candidate embedding site; and

a bitstream segmenter, coupled to receive the set of data values, that segments the set of data values to obtain at least one data vector.

17. The system of claim 16 wherein calculating a robust average intensity by performing the steps comprises:

computing a cumulative density function based upon an intensity histogram of the pixels from a portion of the image;

using the cumulative density function to identify pixels to exclude; and

calculating a robust average intensity using at least some of the pixels that were not excluded.

18. The system of claim 17 wherein the step of using the cumulative density function to identify pixels to exclude comprises:

excluding the pixels having an intensity corresponding to a cumulative density function that is below a first cumulative density function threshold value; and

excluding the pixels having an intensity corresponding to a cumulative density function that is above a second cumulative density function threshold value.

19. The system of claim 18 wherein the robust average intensity is a first mean intensity of the at least some of the pixels that were not excluded.

20. The system of claim 19 wherein the robust average intensity is a second mean intensity and calculating a robust average intensity using at least some of the pixels that were not excluded further by performing the steps further comprises:

excluding the pixels having an intensity value greater than the first mean intensity value; and

calculating the second mean intensity using at least some of the pixels that were not excluded.