Method for automatically determining a region of interest for text and data overlay

• US 20060126932A1
• Filed: 12/10/2004
• Published: 06/15/2006
• Est. Priority Date: 12/10/2004
• Status: Active Grant

• Current Assignee: III Holdings 6, LLC
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Eschbach, Reiner
• Granted Patent: US 7,352,912 B2
• Time in Patent Office: Days
• US Class Current: 382/173
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06V 10/25 Determination of region of interest [ROI] or a volume of interest [VOI]
• CPC Class Codes: G06V 20/00 Scenes; Scene-specific elements control of digital cameras H04N23/60

Abstract

Provided is a method for the automatic determination of a region of interest in an image, comprising the steps of: segmenting the image into a plurality of smaller regions, each region extending over a plurality of pixels; performing an analysis on each of said regions to characterize an aspect of the region relating to its level of importance in communicating information to a viewer; grouping adjacent regions having similar aspect characteristics; and identifying at least one group as a region of interest. Also provided is a method employing the steps above for use in an automated document composition process, where overlaid content is not placed over regions of interest that are identified.

Claims

1. A method for the automatic determination of a region of interest in an image, comprising the steps of:

segmenting the image into a plurality of smaller regions, each region extending over a plurality of pixels;

performing an analysis on each of said regions to characterize an aspect of the region relating to its level of importance in communicating information to a viewer;

grouping adjacent regions having similar aspect characteristics; and

identifying at least one group as a region of potential overlay.

2. The method of claim 1, wherein each region is rectangular in shape.

3. The method of claim 1, wherein image is segmented into a predefined number of regions.

4. The method of claim 1, wherein image is segmented in regions having at least one predefined dimension.

5. The method of claim 1, wherein said step of performing an analysis, comprises the steps of:

determining a contrast value as an aspect characteristic for each region; and

averaging the contrast values across multiple regions.

6. The method of claim 5, wherein said step of averaging the contrast values across multiple regions comprises applying a filter to the regions.

7. The method of claim 6, wherein said filter is a low-pass filter.

8. The method of claim 5, wherein determining a contrast value for each region comprises the steps of:

for each region of the image, characterizing a local histogram signal representing the image signals within the region;

comparing each local histogram signal to a reference histogram signal; and

deriving a local contrast variance from the comparison, representing the flatness of the local histogram.

9. The method of claim 5, wherein said step of grouping adjacent regions comprises the steps of:

identifying the contrast variance of the region with the greatest image contrast;

based upon the contrast variance of each region, classifying each of the regions, as at least a region of high relevance or a region of low relevance, as compared to a region with the greatest contrast variance; and

applying a threshold to the contrast variance values to identifying those regions with the lowest contrast.

10. The method of claim 9, wherein said step of applying a threshold includes selecting all areas above a threshold value

11. The method of claim 9, wherein the step of applying a threshold includes selecting a predetermined number of regions having the highest contrast variance.

12. The method of claim 9, wherein the step of applying a threshold includes selecting regions using a conditional threshold and determining a Boolean combination of the different averages.

13. The method of claim 1, wherein the step of identifying at least one group as a region of potential overlay, is a function of image space required for an overlay region.

14. An automated document composition process, comprising the steps of:

receiving a document including at least one image therein;

specifying content to be overlaid on at least a portion of the image;

identifying at least one low interest region of the image upon which the content may be overlaid; and

automatically inserting at least a portion of the content into at least the one region.

15. The method of claim 14, wherein the step of identifying at least one low interest region of the image upon which the content may be overlaid, further comprises the steps of:

segmenting the image into a plurality of smaller regions, each region extending over a plurality of pixels;

performing an analysis on each of said regions to characterize an aspect of the region relating to its level of importance in communicating information to a viewer;

grouping adjacent regions having similar aspect characteristics; and

identifying at least one group as a region of interest.

16. The method of claim 15, wherein said step of performing an analysis, comprises the steps of:

determining a contrast value as an aspect characteristic for each region; and

averaging the contrast values across multiple regions.

17. The method of claim 16, wherein said step of grouping adjacent regions comprises the steps of:

identifying the contrast variance of the region with the greatest image contrast;

based upon the contrast value variance of each region, classifying each of the regions, as at least a region of high relevance or a region of low relevance, as compared to the a region with the greatest contrast variance; and

applying a threshold to the contrast variance values to identifying those regions with the lowest contrast.