Correction of “red-eye” effects in images

• US 6,980,691 B2
• Filed: 07/05/2001
• Issued: 12/27/2005
• Est. Priority Date: 07/05/2001
• Status: Expired due to Term

• Current Assignee: Tahoe Research Limited (f/k/a Learndale Limited) (Vector Capital Corporation)
• Original Assignee: Corel Corporation (Corel Holdings LP)
• Inventors: Nesterov, Victor Anatol'evich, Khvatov, Vladimir Alezandrovich, Lalyko, Leonid Borisovich, Zaklika, Kryzstof Antoni
• Primary Examiner(s): Wu, Jingge
• Application Number:
  US09/899,572
• Publication Number:
  US 20030007687A1
• Time in Patent Office: 1,636 Days
• Field of Search: 382/117, 382162-167, 358518-523, 348576-577
• US Class Current: 382/165
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/30216 Redeye defect
• CPC Class Codes: G06T 5/77 Retouching; Inpainting; Scratch removal
• CPC Class Codes: G06T 7/0004 Industrial image inspection
• CPC Class Codes: G06T 7/90 Determination of colour characteristics
• CPC Class Codes: G06V 40/193 Preprocessing; Feature extraction
• CPC Class Codes: H04N 1/624 Red-eye correction control of camera based on recognised facial parts H04N23/611
• CPC Class Codes: H04N 9/3114 by using a sequential colour filter producing one colour at a time

Abstract

After identification of an image or image area where correction is to be effected for red-eye defects, and identification of eye regions within the image or the image area, color data from the area is provided, collected or analyzed according to color channel information. The color channel information may be defined for specific or collections of wavelength values or color content values, usually with at least two channels, up to four or more color channels used to carry all of the information (and with digital images, pixel information) for each color. By using color channel information, local color content information can be readily accessed and evaluated, and after the exact nature or degree of a red-eye effect is determined from evaluation of the color channel information, the data may be readily corrected by a number of different techniques. Templates of colors and/or templates for features (such as glint, pigmentation, or artistic features) may be added to identified regions to be corrected for red-eye

Claims

1. A method of automatically identifying a red-eye defect in a region of an image comprising classifying pixels within the region according to a ratio of the respective values of a first color channel and a second color channel, a red-eye defect being identified when the value of the ratio exceeds a predetermined value;

wherein the classifying of pixels is performed without specifying use of a particular color in the red-eye defect.

2. The method of claim 1 wherein a region of an image is first selected for analysis of a value of a color channel ratio to search for a red-eye defect.

3. The method of claim 1 wherein the ratio of values of color channels comprises a ratio of the darkest color channel to the lightest color channel.

4. The method of claim 1 wherein the ratio of values of color channels comprises a ratio of the lightest color channel to the darkest color channel.

5. The method of claim 1 wherein the classification is effected by a procedure selected from the group consisting of segmentation, iterative segmentation, and iterative segmentation based on a threshold value of color channel ratios.

6. The method of claim 1 wherein the classification is effected by a procedure selected from the group consisting of segmentation, iterative segmentation, and iterative segmentation based on a threshold value of area ratios.

18. A computer having a program therein that can effect a method for detecting identifying a red-eye defect, wherein the method comprises the method of claim 1.

7. A method of identifying a red-eye defect in a region of an image comprising classifying pixels within the region according to a ratio of the respective values of a first color channel and a second color channel, a red-eye defect being identified when the value of the ratio exceeds a predetermined value;

wherein the classifying of pixels is performed without specifying use of a particular color in the red-eye defect.

8. The method of claim 7 wherein a region of an image is first selected for analysis of a value of a color channel ratio to search for a red-eye defect.

9. The method of claim 7 wherein the ratio of values of color channels comprises a ratio of the darkest color channel to the lightest color channel.

10. The method of claim 7 wherein the ratio of values of color channels comprises a ratio of the lightest color channel to the darkest color channel.

11. The method of claim 7 wherein the boundaries containing at least all pixels of a red-eye defect are identified by classifying pixels within the region according to values of a ratio of color channels, red-eye defects being identified when values of the ratio exceed a predetermined value.

12. The method of claim 7 wherein the boundaries containing at least all pixels of a red-eye defect are identified by classifying pixels within the region according to values of a ratio of color channels, red-eye defects being identified when values of the ratio do not exceed a predetermined value.

13. The method of claim 11 wherein pixels within the bounded region are classified into eye and non-eye pixels according to one of a plurality of pixel information channels, wherein the one channel is selected as the one of a plurality of channels that contains the largest number of pixels in said region that exceeds an area threshold at a predetermined measure of central tendency computed for each respective channel.

14. The method of claim 12 wherein pixels within the bounded region are classified into eye and non-eye pixels according to one of a plurality of pixel information channels, wherein the one channel is selected as the one of a plurality of channels that contains the largest number of pixels in said region that exceeds an area threshold at a predetermined measure of central tendency computed for each respective channel.

19. A computer having a program therein that can effect a method for correcting red-eye defect, wherein the method comprises the method of claim 11.

20. A computer having a program therein that can effect a method for correcting red-eye defect, wherein the method comprises the method of claim 12.

21. A computer having a program therein that can effect a method for correcting red-eye defect, wherein the method comprises the method of claim 13.

22. A computer having a program therein that can effect a method for correcting red-eye defect wherein the method comprises the method of claim 14.

15. A method of automatically identifying an object within in a region of an image comprising classifying pixels within the region according to a ratio of the respective values of a first color channel and a second color channel, an object being identified when the value of the ratio exceeds a predetermined value;

wherein the classifying of pixels is performed without specifying use of a particular color in the object.

16. A method of selecting pixels from a digital image that has an eye defect, comprising circumscribing a region including all pixels of a portion of an image comprising an eye, and classifying pixels in this region into eye and non-eye pixels according to one of a plurality of pixel information channels, wherein the one channel is selected as the one of a plurality of channels that contains the largest number of pixels in said region that exceeds an area threshold at a predetermined measure of central tendency computed for each respective channel.

17. A method of selecting pixels from a digital image that has an eye defect, comprising circumscribing a region including all pixels of a portion of an image comprising an eye by classification of pixels to one of a plurality of pixel information channels, wherein the one channel is selected as the one of a plurality of channels that contains the largest number of pixels in said region that does not exceed an area threshold at a predetermined measure of central tendency computed for each respective channel.

23. A computer readable medium having computer executable instructions for performing steps comprising:

classifying pixels within a region of an image according to a ratio of the respective values of a first color channel and a second color channel;

wherein the classifying of pixels is performed without specifying use of a particular color channel in the region of the image; and

,

identifying an object in the region of an image;

the object being identified when the value of the ratio exceeds a predetermined value.

24. A computer readable medium according to claim 23 wherein the object is a red eye defect.

25. A computer readable medium according to claim 23 wherein the identifying is automatic.

26. A computer readable medium according to claim 23 further comprising:

providing for preliminarily selecting the region of the image for analysis of a value of a color channel ratio.

27. A computer readable medium according to claim 23 wherein the ratio of values of color channels comprises a ratio of the darkest color channel to the lightest color channel.

28. A computer readable medium according to claim 23 wherein the ratio of values of color channels comprises a ratio of the lightest color channel to the darkest color channel.

29. A computer readable medium according to claim 23 wherein the boundaries containing at least all pixels of a red-eye defect are identified by classifying pixels within the region according to values of a ratio of color channels, red-eye defects being identified when the value of the ratio is one of exceeding or not exceeding a predetermined value.

30. A computer readable medium according to claim 23 wherein pixels within the bounded region are classified into eye and non-eye pixels according to one of a plurality of pixel information channels, wherein the one channel is selected as the one of a plurality of channels that contains the largest number of pixels in said region that exceeds an area threshold at a predetermined measure of central tendency computed for each respective channel.

31. A computer readable medium according to claim 23 wherein the classification is effected by a procedure selected from the group consisting of segmentation, iterative segmentation, iterative segmentation based on a threshold value of color channel ratios, and iterative segmentation based on a threshold value of area ratios.

32. A computer readable medium having computer executable instructions for performing steps in selecting pixels in a digital image, the steps comprising:

circumscribing a region including all pixels of a portion of a digital image; and

,

classifying pixels in this region into eye and non-eye pixels according to a selected channel of a plurality of pixel information channels, wherein the selected channel is selected as the channel of the plurality of channels that contains the largest number of pixels in said region that one of exceeds or does not exceed an area threshold at a predetermined measure of central tendency computed for each respective channel.