Automated detection and correction of eye color defects due to flash illumination

Claim 1

A method of detecting for eye color defects of a subject in a digital image due to flash illumination, comprising the steps of: a) defining a spatial region within the digital image in which one or more eye color defects may exist, which region includes at least a portion of the subject's head; b) sampling pixels within such spatial region for their color content and comparing each such sample pixel with a plurality of threshold values which are representative of eye color defects to identify possible eye color defect pixels; c) segmenting the identified possible eye color defective pixels into one or more spatially contiguous groups; d) calculating a first score for each pixel of each segmented group and for each group based upon a plurality of features including group size, group shape, coloration, and brightness to identify eye color defect group candidates; e) selecting a seed pixel based on its score from each identified eye color defect group candidate and determining all the neighboring pixels which are within a predetermined score range of their neighboring pixels and those pixels in a group which represent a significant pixel score transition so that the determined transitions identify the outer boundary of an eye color defect group candidate; and f) calculating a second score for each pixel for each eye color defect group candidate based on a plurality of features including group size, group shape, coloration, and brightness to determine an actual eye color defect group.

Claim 2

A method of detecting for eye color defects of a subject in a digital image due to flash illumination, comprising the steps of: a) defining a spatial region within the digital image in which one or more eye color defects may exist, which region includes at least a portion of the subject's head; b) sampling pixels within such spatial region for their color content and comparing each such sample pixel with a plurality of threshold values which are representative of eye color defects to identify possible eye color defect pixels; c) segmenting the identified possible eye color defective pixels into one or more spatially contiguous groups; d) calculating a first score for each pixel of each segmented group and for each group based upon a plurality of features including group size, group shape, coloration, and brightness to identify eye color defect group candidates; e) selecting a seed pixel based on its score from each identified eye color defect group candidate and determining all the neighboring pixels which are within a predetermined score range of their neighboring pixels and those pixels in a group which represent a significant pixel score transition so that the determined transitions identify the outer boundary of an eye color defect group candidate; and f) calculating a second score for each pixel for each eye color defect group candidate based on a plurality of features including group size, group shape, coloration, and brightness to determine an actual eye color defect group.

Claim 8

A method of detecting for eye color defects of a subject in a digital image due to flash illumination, comprising the steps of: a) defining a spatial region within the digital image in which one or more eye color defects may exist, which region includes at least a portion of the subject's head; b) sampling pixels within such spatial region for their color content and comparing each such sampled pixel with a plurality of threshold values which are representative of eye color defects to identify possible eye color defect pixels; c) segmenting the identified possible eye color defect pixels into one or more spatially contiguous groups; d) calculating a first score for each pixel of each segmented group and for each group based upon a plurality of features including group size, group shape, coloration, and brightness to identify eye color defect group candidates; e) selecting a seed pixel based on its score from each identified eye color defect group candidate and determining all the neighboring pixels which are within a predetermined score range of their neighboring pixels and those pixels in a group which represent a significant pixel score transition so that the determined transitions identify the outer boundary of an eye color defect group candidate; f) calculating a second score for each pixel for each eye color defect group candidate based on a plurality of features including group size, group shape, coloration, and brightness; g) selecting the best group score of an eye color defect group candidate and comparing it relative to a predetermined threshold group score to determine whether a first eye color defect group is present and identifying it as corresponding to the most likely eye; and h) determining whether a second actual eye color defect group is present based on whether the area of one of the color defect group candidate's eye is within a predetermined threshold ratio of the area of the first actual eye color defect group corresponding to the most likely eye, and whether this group candidate is within a predetermined threshold angular subtense of the most likely eye along the predetermined horizontal axis of the subject's head.

Claim 9

A method of detecting for eye color defects of a subject in a digital image due to flash illumination, comprising the steps of: a) defining a spatial region within the digital image in which one or more eye color defects may exist, which region includes at least a portion of the subject's head; b) sampling pixels within such spatial region for their color content and comparing each such sampled pixel with a plurality of threshold values which are representative of eye color defects to identify possible eye color defect pixels; c) segmenting the identified possible eye color defect pixels into one or more spatially contiguous groups; d) calculating a first score for each pixel of each segmented group and for each group based upon a plurality of features including group size, group shape, coloration, and brightness to identify eye color defect group candidates; e) selecting a seed pixel based on its score from each identified eye color defect group candidate and determining all the neighboring pixels which are within a predetermined score range of their neighboring pixels and those pixels in a group which represent a significant pixel score transition so that the determined transitions identify the outer boundary of an eye color defect group candidate; f) calculating a second score for each pixel for each eye color defect group candidate based on a plurality of features including group size, group shape, coloration, and brightness; g) selecting the best group score of an eye color defect group candidate and comparing it relative to a predetermined threshold group score to determine whether a first eye color defect group is present and identifying it as corresponding to the most likely eye; and h) determining whether a second actual eye color defect group is present based on whether the area of one of the color defect group candidate's eye is within a predetermined threshold ratio of the area of the first actual eye color defect group corresponding to the most likely eye, and whether this group candidate is within a predetermined threshold angular subtense of the most likely eye along the predetermined horizontal axis of the subject's head.

Claim 15

A method of detecting and correcting for eye color defects of a subject in a digital image due to flash illumination, comprising the steps of: a) defining a spatial region within the digital image in which one or more eye color defects may exist, which region includes at least a portion of the subject's head; b) sampling pixels within such spatial region for their color content and comparing each such sampled pixel with a plurality of threshold values which are representative of eye color defects to identify possible eye color defect pixels; c) segmenting the identified possible eye color defect pixels into one or more spatially contiguous groups; d) calculating a first score for each pixel of each segmented group and for each group based upon a plurality of features including group size, group shape, coloration, and brightness to identify eye color defect group candidates; e) selecting a seed pixel based on its score from each identified eye color defect group candidate and determining all the neighboring pixels which are within a predetermined score range of their neighboring pixels and those pixels in a group which represent a significant pixel score transition so that the determined transitions identify the outer boundary of an eye color defect group candidate; f) calculating a second score for each pixel for each eye color defect group candidate based on a plurality of features including group size, group shape, coloration, and brightness; g) selecting the best group score of an eye color defect group candidate and comparing it relative to a predetermined threshold group score to determine whether a first eye color defect group is present and identifying it as corresponding to the most likely eye; and h) determining whether a second actual eye color defect group is present based on whether the area of one of the color defect group candidate's eye is within a predetermined threshold ratio of the area of the first actual eye color defect group corresponding to the most likely eye, and whether this group candidate is within a predetermined threshold angular subtense of the most likely eye along the predetermined horizontal axis of the subject's head; i) correcting the defective color in each eye color defect group by: (i) determining the correct resolution and whether each pixel at the corrected resolution is an eye color defect pixel; (ii) categorizing the eye color defect pixels at the corrected resolution into either body, border, or glint categories; and (iii) correcting the eye color defect pixels in the body, border, or glint categories.

Claim 16

A method of detecting and correcting for eye color defects of a subject in a digital image due to flash illumination, comprising the steps of: a) defining a spatial region within the digital image in which one or more eye color defects may exist, which region includes at least a portion of the subject's head; b) sampling pixels within such spatial region for their color content and comparing each such sampled pixel with a plurality of threshold values which are representative of eye color defects to identify possible eye color defect pixels; c) segmenting the identified possible eye color defect pixels into one or more spatially contiguous groups; d) calculating a first score for each pixel of each segmented group and for each group based upon a plurality of features including group size, group shape, coloration, and brightness to identify eye color defect group candidates; e) selecting a seed pixel based on its score from each identified eye color defect group candidate and determining all the neighboring pixels which are within a predetermined score range of their neighboring pixels and those pixels in a group which represent a significant pixel score transition so that the determined transitions identify the outer boundary of an eye color defect group candidate; f) calculating a second score for each pixel for each eye color defect group candidate based on a plurality of features including group size, group shape, coloration, and brightness; g) selecting the best group score of an eye color defect group candidate and comparing it relative to a predetermined threshold group score to determine whether a first eye color defect group is present and identifying it as corresponding to the most likely eye; and h) determining whether a second actual eye color defect group is present based on whether the area of one of the color defect group candidate's eye is within a predetermined threshold ratio of the area of the first actual eye color defect group corresponding to the most likely eye, and whether this group candidate is within a predetermined threshold angular subtense of the most likely eye along the predetermined horizontal axis of the subject's head; i) correcting the defective color in each eye color defect group by: (i) determining the correct resolution and whether each pixel at the corrected resolution is an eye color defect pixel; (ii) categorizing the eye color defect pixels at the corrected resolution into either body, border, or glint categories; and (iii) correcting the eye color defect pixels in the body, border, or glint categories.