Detecting redeye defects in digital images
First Claim
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1. A method for detecting a redeye defect in a digital image containing an eye, the method comprising the following steps:
- (a) converting the digital image into an intensity image, including;
(i) subjecting the intensity image to local averaging, and(ii) subjecting the intensity image to mean shift segmentation, and(b) segmenting at least a portion of the intensity image into segments each having a local intensity maximum,(c) thresholding a corresponding portion of the digital image to identify regions exhibiting an intensity measure that is above a threshold value,(d) selecting a region from at least two regions of step (c) having the most extreme average intensity, and(e) identifying segments from step (b) intersecting the region selected at step (d) according to a pre-determined criterion,wherein the average intensities of the two regions from step (c) having the highest and next highest average intensities differ by more than a predetermined amount, and the region selected at step (d) is the region having the highest average intensity.
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Abstract
A method for detecting a redeye defect in a digital image containing an eye comprises converting the digital image into an intensity image, and segmenting the intensity image into segments each having a local intensity maximum. Separately, the original digital image is thresholded to identify regions of relatively high intensity and a size falling within a predetermined range. Of these, a region is selected having substantially the highest average intensity, and those segments from the segmentation of the intensity image whose maxima are located in the selected region are identified.
57 Citations
21 Claims
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1. A method for detecting a redeye defect in a digital image containing an eye, the method comprising the following steps:
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(a) converting the digital image into an intensity image, including; (i) subjecting the intensity image to local averaging, and (ii) subjecting the intensity image to mean shift segmentation, and (b) segmenting at least a portion of the intensity image into segments each having a local intensity maximum, (c) thresholding a corresponding portion of the digital image to identify regions exhibiting an intensity measure that is above a threshold value, (d) selecting a region from at least two regions of step (c) having the most extreme average intensity, and (e) identifying segments from step (b) intersecting the region selected at step (d) according to a pre-determined criterion, wherein the average intensities of the two regions from step (c) having the highest and next highest average intensities differ by more than a predetermined amount, and the region selected at step (d) is the region having the highest average intensity. - View Dependent Claims (2, 3, 4, 5, 6, 20, 21)
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7. A method for detecting a redeye defect in a digital image containing an eye, the method comprising the following steps:
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(a) converting the digital image into an intensity image, including; (i) subjecting the intensity image to local averaging, and (ii) subjecting the intensity image to mean shift segmentation, and (b) segmenting at least a portion of the intensity image into segments each having a local intensity maximum, (c) thresholding a corresponding portion of the digital image to identify regions exhibiting an intensity measure that is above a threshold value, (d) selecting a region from at least two regions of step (c) having the most extreme average intensity, and (e) identifying segments from step (b) intersecting the region selected at step (d) according to a pre-determined criterion, wherein step (c) uses a threshold obtained by identifying the highest intensity pixel inside the intensity image, computing the average intensity of the pixels inside a smaller rectangle centered on the highest intensity pixel, and assigning the threshold to the sum of the average intensity and a fixed quantity. - View Dependent Claims (8, 9, 10, 11, 12, 13)
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14. A method for detecting a redeye defect in a digital image containing an eye, the method comprising the following steps:
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(a) converting the digital image into an intensity image, including; (i) subjecting the intensity image to local averaging, and (ii) subjecting the intensity image to mean shift segmentation, and (b) segmenting at least a portion of the intensity image into segments each having a local intensity maximum, (c) thresholding a corresponding portion of the digital image to identify regions exhibiting an intensity measure that is above a threshold value, (d) selecting a region from at least two regions of step (c) having the most extreme average intensity, and (e) identifying segments from step (b) intersecting the region selected at step (d) according to a pre-determined criterion, wherein the average intensities of the two regions from step (c) having the highest and next highest average intensities differ by less than a predetermined amount, and the region selected at step (d) is the region having the highest average saturation. - View Dependent Claims (15, 16, 17, 18, 19)
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Specification