Method and system for detecting and correcting defects in a digital image

• US 20040114829A1
• Filed: 10/10/2003
• Published: 06/17/2004
• Est. Priority Date: 10/10/2002
• Status: Abandoned Application

• Current Assignee: Intelligent System Solutions Corporation
• Original Assignee: Intelligent System Solutions Corporation
• Inventors: Hale, Rodney D., LeFeuvre, Edythe Patricia
• Application Number:
  US10/682,364
• Publication Number:
  US 20040114829A1
• Time in Patent Office: Days
• US Class Current: 382/275
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/20036 Morphological image processing
• CPC Class Codes: G06T 2207/30216 Redeye defect
• CPC Class Codes: G06T 5/77 Retouching; Inpainting; Scratch removal
• CPC Class Codes: G06T 7/40 Analysis of texture depth or shape recovery from texture G06T7/529

Abstract

The invention relates to a method, system and computer program product for correcting a red-eye effect in a digital image provided by a cluster of pixels. It comprises (a) conducting at least one tophat operation over each pixel in the digital image to provide a tophat image; (b) conducting an intensity threshold operation on the tophat image to provide a segmentation mask for segmenting objects in the digital image; (c) for each segmented object in the segmentation mask, extracting at least one feature from at least one of the segmented object and a border region surrounding the segmented object and classifying the segmented object based on the at least one feature; and (d) for each segmented object in the segmentation mask classified as red-eye effect in step (c), correcting the red-eye effect by re-coloring the segmented object to generate a corrected image.

Claims

1. A method of correcting a red-eye effect in a digital image provided by a cluster of pixels, the method comprising:

(a) conducting at least one tophat operation over each pixel in the digital image to provide a tophat image;

(b) conducting an intensity threshold operation on the tophat image to provide a segmentation mask for segmenting objects in the digital image;

(c) for each segmented object in the segmentation mask, extracting at least one feature from at least one of the segmented object and a border region surrounding the segmented object and classifying the segmented object based on the at least one feature; and

(d) for each segmented object in the segmentation mask classified as red-eye effect in step (c), correcting the red-eye effect by re-coloring the segmented object to generate a corrected image.

2. The method as defined in claim 1 wherein the tophat image is a dark tophat image, and the tophat operation comprises the steps of:

conducting at least one greyscale dilation operation over each pixel in the digital image to provide a dilated image;

conducting at least one greyscale erosion operation over each pixel in the eroded image to provide an eroded image; and

, subtracting the digital image from the eroded image to provide the dark tophat image.

3. The method as defined in claim 1 wherein the tophat image is a bright tophat image, and the tophat operation comprises the steps of:

conducting at least one greyscale erosion operation over each pixel in the digital image to provide an eroded image;

conducting at least one greyscale dilation operation over each pixel in the eroded image to provide a dilated image; and

, subtracting the dilated image from the digital image to provide the bright tophat image.

4. The method as defined in claim 1 further-comprising generating at least one low resolution image from the digital image;

conducting a secondary tophat operation over each pixel in the at least one low resolution image to provide at least one low resolution tophat image;

conducting an intensity threshold operation on the at least one low resolution tophat image to provide at least one low resolution segmentation mask for segmenting objects in the digital image;

for each segmented object in the at least one low resolution segmentation mask, extracting at least one feature from one of the segmented object and a border region surrounding the segmented object and classifying the segmented object based on the at least one feature; and

for each segmented object in the at least one low resolution segmentation mask classified as red-eye effect in step (c), correcting the red-eye effect by re-coloring the segmented object.

5. The method as defined in claim 1 wherein step (b) comprises, after intensity thresholding the bright tophat image, filtering out objects having a compactness below a threshold level of compactness to provide the segmentation mask.

6. The method as defined in claim 1 wherein step (c) comprises, for each segmented object in the segmentation mask, after extracting the at least one feature, comparing the at least one feature with a paradigmatic red-eye feature cluster to determine an associated probability that the segmented object is a red-eye defect, and classifying the segmented object as a red-eye defect if and only if the associated probability exceeds a threshold probability.

7. The method as defined in claim 1 further comprising selecting the digital image from an initial image.

8. A system for correcting a red-eye effect in a digital image provided by a cluster of high intensity pixels, the system comprising:

a memory for storing the digital image; and

means for performing the steps of

(a) conducting at least one tophat operation over each pixel in the digital image to provide a tophat image;

(b) conducting an intensity threshold operation on the tophat image to provide a segmentation mask for segmenting objects in the digital image;

(c) for each segmented object in the segmentation mask, extracting at least one feature from at least one of the segmented object and a border region surrounding the segmented object and classifying the segmented object based on the at least one feature; and

(d) for each segmented object in the segmentation mask classified as red-eye effect in step (c), correcting the red-eye effect by re-coloring the segmented object to generate a corrected image.

9. The system as defined in claim 8 wherein the tophat image is a dark tophat image, and the tophat operation comprises the steps of:

conducting at least one greyscale dilation operation over each pixel in the digital image to provide an dilated image;

conducting at least one greyscale erosion operation over each pixel in the eroded image to provide a eroded image;

subtracting the digital image from the eroded image to provide the dark tophat image.

10. The system as defined in claim 8 wherein the tophat image is a bright tophat image, and the tophat operation comprises the steps of:

conducting at least one greyscale erosion operation over each pixel in the digital image to provide an eroded image;

conducting at least one greyscale dilation operation over each pixel in the eroded image to provide a dilated image; and

, subtracting the dilated image from the digital image to provide the bright tophat image.

11. The system as defined in claim 8 further comprising means for generating at least one low resolution image from the digital image.

12. The system as defined in claim 8 wherein step (b) comprises, after intensity thresholding the bright tophat image filtering out objects having a compactness below a threshold level of compactness stored in the memory to provide the segmentation mask.

13. The system as defined in claim 8 wherein step (c) comprises, for each segmented object in the segmentation mask, after extracting the at least one feature, comparing the at least one feature with a paradigmatic red-eye feature cluster stored in the memory to determine an associated probability that the segmented object is a red-eye defect, and classifying the segmented object as a red-eye defect if and only if the associated probability exceeds a threshold probability.

14. The system as defined in claim 8 further comprising

a display for displaying n initial image;

and

a user-operable selection means for selecting the digital image from the large image.

15. The system as defined in claim 13 further comprising a user-operable selection means for selectably changing the threshold probability.

16. The system as defined in claim 8.further comprising

a display for displaying the corrected image;

a user-operable selection means for selecting an object in the corrected image to generate a corrected image; and

,

a user-selectable manual override operation for (i) when the object has been classified as red-eye, uncoloring and reclassifying the object and (ii) when the object has not been classified as red-eye, reclassifying the object as red-eye and recoloring the object to correct for the red-eye effect.

17. A computer program product for use on a computer system to correct a red-eye effect in a digital image defined over a cluster of pixels, the computer program product comprising:

a recording medium;

means recorded on the medium for instructing the computer system to perform the steps of;

(a) conducting at least one tophat operation over each pixel in the digital image to provide a tophat image;

(b) conducting an intensity threshold operation on the tophat image to provide a segmentation mask for segmenting objects in the digital image;

(c) for each segmented object in the segmentation mask, extracting at least one feature from at least one of the segmented object and a border region surrounding the segmented object and classifying the segmented object-based on the at least one feature; and

(d) for each segmented object in the segmentation mask classified as red-eye effect in step (c), correcting the red-eye effect by re-coloring the segmented object to generate a corrected image.

18. The computer program product as defined in claim 17 wherein the tophat image is a dark tophat image, and the tophat operation comprises the steps of:

conducting at least one greyscale dilation operation over each pixel in the digital image to provide an dilated image;

conducting at least one greyscale erosion operation over each pixel in the eroded image to provide a eroded image;

subtracting the digital image from the eroded image to provide the dark tophat image.

19. The computer program product as defined in claim 17 wherein the tophat image is a bright tophat image, and the tophat operation comprises the steps of:

conducting at least one greyscale erosion operation over each pixel in the digital image to provide an eroded image;

conducting at least one greyscale dilation operation over each pixel in the eroded image to provide a dilated image; and

,

subtracting the dilated image from the digital image to provide the bright tophat image.

20. The computer program product as defined in claim 17 wherein step (b) comprises, after intensity thresholding the bright tophat image filtering out objects having a compactness below a threshold level of compactness stored in the memory to provide the segmentation mask.