Methods and Systems for Detecting Regions in Digital Images

• US 20070206856A1
• Filed: 06/15/2006
• Published: 09/06/2007
• Est. Priority Date: 03/02/2006
• Status: Active Grant

• Current Assignee: Sharp Kabushiki Kaisha (Hon Hai Precision Industry Co., Ltd.)
• Original Assignee: Sharp Kabushiki Kaisha (Hon Hai Precision Industry Co., Ltd.)
• Inventors: Matsuda, Toyohisa, Campbell, Richard John
• Granted Patent: US 7,792,359 B2
• Time in Patent Office: Days
• US Class Current: 382/171
• CPC Class Codes: G06V 10/507 Summing image-intensity values; Histogram projection analysis
• CPC Class Codes: G06V 30/413 Classification of content, e.g. text, photographs or tables

Abstract

Embodiments of the present invention comprise systems, methods and devices for detection of image regions using a masking condition and an entropy measure.

Claims

1. A method for detecting a region in a digital image, said method comprising:

a) applying a masking condition to a digital image thereby identifying masked pixels that satisfy said masking condition;

b) calculating an entropy feature for a pixel-of-interest in a digital image, wherein said calculating comprises;

i) forming a first histogram of pixel values for a plurality of pixels in a region surrounding said pixel-of-interest, wherein said masked pixels do not accumulate in said first histogram; and

ii) calculating an entropy measure for said first histogram; and

c) classifying a neighborhood of said digital image based on said entropy measure and said pixel-of-interest location.

2. A method as described in claim 1 wherein said calculating further comprises a first quantization of said pixel values for said plurality of pixels in said region.

3. A method as described in claim 1 wherein said first histogram bin size comprises a plurality of pixel values.

4. A method as described in claim 1 wherein said first histogram range is smaller than said dynamic range of said pixel values for a plurality of pixels in said digital image.

5. A method as described in claim 1 wherein said calculating further comprises:

a) forming a second histogram of pixel values for a plurality of pixels in a region surrounding said pixel-of-interest, wherein said masked pixels do not accumulate in said second histogram;

b) calculating a second entropy measure for said second histogram; and

c) combining said first entropy measure and said second entropy measure to form said entropy measure.

6. A method as described in claim 5 wherein said first histogram bin size is not the same as said second histogram bin size.

7. A method as described in claim 5 wherein said first histogram bins are not located at the same locations as said second histogram bins.

8. A method as described in claim 1 wherein said calculating further comprises:

a) selecting a first lobe of said first histogram; and

b) calculating said entropy measure using only said first lobe of said first histogram.

9. A method as described in claim 8 wherein said selecting comprises identifying a lobe containing said pixel value of said pixel-of-interest.

10. A system for detecting a region in a digital image, said system comprising:

a) a processor for applying a masking condition to a digital image thereby identifying masked pixels that satisfy said masking condition;

b) a calculator for calculating an entropy feature for a pixel-of-interest in a digital image, said calculator comprising;

i) a histogram generator for forming a first histogram of pixel values for a plurality of pixels in a region surrounding said pixel-of-interest, wherein said masked pixels do not accumulate in said first histogram; and

ii) a calculator for calculating an entropy measure for said first histogram; and

c) a classifier for classifying a neighborhood of said digital image based on said entropy measure and said pixel-of-interest location.

11. A system as described in claim 10 wherein said calculator further comprises a first quantizer for quantizing said pixel values for said plurality of pixels in said region.

12. A system as described in claim 10 wherein said first histogram bin size comprises a plurality of pixel values.

13. A system as described in claim 10 wherein said first histogram range is smaller than said dynamic range of said pixel values for a plurality of pixels in said digital image.

14. A system as described in claim 10 wherein said calculator further comprises:

a) a histogram generator for forming a second histogram of pixel values for a plurality of pixels in a region surrounding said pixel-of-interest, wherein said masked pixels do not accumulate in said second histogram;

b) a calculator for calculating a second entropy measure for said second histogram; and

c) a combiner for combining said first entropy measure and said second entropy measure to form said entropy measure.

15. A system as described in claim 14 wherein said first histogram bin size is not the same as said second histogram bin size.

16. A system as described in claim 14 wherein said first histogram bins are not located at the same locations as said second histogram bins.

17. A system as described in claim 10 wherein said calculator further comprises:

a) a selector for selecting a first lobe of said first histogram; and

b) a calculator for calculating said entropy measure using only said first lobe of said first histogram.

18. A system as described in claim 17 wherein said selector comprises an identifier for identifying a lobe containing said pixel value of said pixel-of-interest.

19. A method for detecting a region in a digital image, said method comprising:

a) applying a masking condition to a digital image thereby identifying masked pixels that satisfy said masking condition;

b) calculating an entropy feature for a pixel-of-interest in a digital image, wherein said calculating comprises;

i) forming a plurality of histograms of pixel values for a plurality of pixels in a region surrounding said pixel-of-interest, wherein said masked pixels do not accumulate in said plurality of histograms;

ii) selecting a first histogram from said plurality of histograms; and

iii) calculating an entropy measure for said first histogram; and

c) classifying a neighborhood of said digital image based on said entropy measure and said pixel-of-interest location.

20. The method of claim 19 wherein said selecting comprises comparing the lobes of said plurality of histograms.