Methods and Systems for Segmenting a Digital Image into Regions

• US 20070291288A1
• Filed: 06/15/2006
• Published: 12/20/2007
• Est. Priority Date: 06/15/2006
• 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: Campbell, Richard John, Chen, Lawrence Shao-Hsien, Matsuda, Toyohisa
• Granted Patent: US 7,864,365 B2
• Time in Patent Office: Days
• US Class Current: 358/1.9
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/20152 Watershed segmentation
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/136 involving thresholding
• CPC Class Codes: G06T 7/155 involving morphological operators
• CPC Class Codes: G06T 7/187 involving region growing; involving region merging; involving connected component labelling
• CPC Class Codes: G06T 7/90 Determination of colour characteristics
• CPC Class Codes: H04N 1/40062 Discrimination between different image types, e.g. two-tone, continuous tone

Abstract

Embodiments of the present invention comprise methods and systems for segmenting a digital image into regions.

Claims

1. A method for segmenting a digital image into regions, said method comprising:

a. determining a frequency-of-occurrence of a plurality of pixel values in a digital image comprising pixels, wherein said determining comprises excluding at least one of said pixels in said digital image from said determining;

b. identifying at least one pixel value associated with a peak frequency-of-occurrence;

c. associating said at least one pixel value with an image label, thereby producing an association between said at least one pixel value and said image label;

d. determining a candidate pixel location in said digital image; and

e. labeling said candidate pixel location based on said association.

2. The method of claim 1 further comprising;

a. determining an unreliable-pixel location in said digital image, wherein said unreliable-pixel location is not said candidate pixel location; and

b. labeling said unreliable-pixel location based on a plurality of neighboring pixel labels.

3. The method of claim 2 wherein said labeling said unreliable-pixel location comprises:

a. determining a class label for said unreliable-pixel location based on said association between said at least one pixel value and said image label;

b. accumulating a frequency-of-occurrence of said image label among said plurality of neighboring pixel labels; and

c. labeling said unreliable-pixel location based on a comparison between a threshold value and said frequency-of-occurrence of said label among said plurality of neighboring pixel labels.

4. The method of claim 1 further comprising generating a look-up-table wherein said look-up-table comprises an association between said at least one pixel value and said image label.

5. The method of claim 1 wherein said determining a frequency-of-occurrence further comprises generating a histogram.

6. The method of claim 5 further comprising smoothing said histogram.

7. The method of claim 1 wherein said at least one excluded pixel in said digital image comprises a pixel that is a portion of a text region.

8. The method of claim 1 wherein said determining a candidate pixel location comprises one or more of a uniformity measure, a variance measure, and a text measure.

9. The method of claim 1 wherein said image label indicates a local background region.

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

a. a counter for counting a frequency-of-occurrence of a plurality of pixel values in a digital image comprising pixels, wherein said counting comprises excluding at least one of said pixels in said digital image from said counting;

b. an identifier for identifying at least one pixel value associated with a peak frequency-of-occurrence;

c. an associator for associating said at least one pixel value with an image label, thereby producing an association between said at least one pixel value and said image label;

d. a first determiner for determining a candidate pixel location in said digital image; and

e. a first labeler for labeling said candidate pixel location in said digital image based on said association.

11. The system of claim 10 further comprising;

a. a second determiner for determining an unreliable-pixel location in said digital image, wherein said unreliable-pixel location is not said candidate pixel location; and

b. a second labeler for labeling said unreliable-pixel location based on a plurality of neighboring pixel labels.

12. The system of claim 11 wherein said second labeler comprises:

a. a labeler determiner for determining a class label for said unreliable-pixel location based on said association between said at least one pixel value and said image label;

b. an accumulator for accumulating a frequency-of-occurrence of said image label among said plurality of neighboring pixel labels; and

c. an unreliable-pixel labeler for labeling said unreliable-pixel location based on a comparison between a threshold value and said frequency-of-occurrence of said label among said plurality of neighboring pixel labels.

13. The system of claim 10 further comprising a generator for generating a look-up-table wherein said look-up-table comprises an association between said at least one pixel value and said image label.

14. The system of claim 10 wherein said at least one excluded pixel in said digital image comprises a pixel that is a portion of a text region.

15. The system of claim 10 wherein said plurality of pixel values comprise color values in a luminance-chrominance color space.

16. The system of claim 10 wherein said image label indicates a local background region.

17. A method for labeling an unlabeled pixel in a classification map, said method comprising:

a. performing a scan of a classification map;

b. identifying in said scan an unlabeled pixel;

c. accumulating a scan count for said unlabeled pixel if a condition is satisfied; and

d. labeling said unlabeled pixel based on said scan count.

18. The method of claim 17 wherein said scan of said classification map is one of a plurality of scans of said classification map, wherein each of said plurality of scans of said classification map is performed a different direction.

19. The method of claim 17 wherein said condition is related to the labels of a plurality of neighboring pixels to said unlabeled pixel.

20. The method of claim 19 wherein said plurality of neighboring pixels comprises the four nearest neighboring scanned pixels.