Automatic red-eye object classification in digital photographic images
AssigneeSeiko Epson Corporation
InventorsSusan Yang, Rastislav Lukac
Life of this patent
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Abstract
Automatic red-eye object classification in digital photographic images. A method for classifying a candidate red-eye object in a digital photographic image includes several acts. First, a candidate red-eye object in a digital photographic image is selected. Next, RGB pixels of the candidate red-eye object are converted into YUV pixels. Then, the YUV pixels satisfying a constraint that is a function of the YUV pixels are summed. Next, the sum is determined to be greater than or equal to a scaled version of the total number of YUV pixels in the candidate red-eye object. Finally, the candidate red-eye object is transformed into a true red-eye object.
Claims6
A method for classifying a candidate red-eye object in a digital image, the method comprising the acts of:
- i) selecting a candidate red-eye object in a digital image;
- ii) converting RGB pixels of the candidate red-eye object into YUV pixels;
- iii.a) summing the YUV pixels satisfying a first constraint that is a function of the YUV pixels, and iii.b) summing the number of pixels satisfying a second constraint:
- x′(r,s)3−x′(r,s)2>δ′min(x(r,s)2,x(r,s)3)
- where:
- x is the digital image with RGB pixels x(r,s)=[x(r,s)1, x(r,s)2, x(r,s)3];
- x′ is a YUV version of x with YUV pixels x′(r,s),=[x′(r,s)1, x′(r,s)2, x′(r,s)3];
- (r,s) are pixel locations;
- and
- δ′ is a tunable parameter;
- iv) determining that the sum of iii.a) is greater than or equal to a first scaled version of the total number of YUV pixels in the candidate red-eye object, and determining that the sum of iii.b) is less than or equal to a second scaled version of the total number of YUV pixels in the candidate red-eye object;
- and
- v) transforming the candidate red-eye object into a true red-eye object.
One or more computer-readable non-transitory media storing computer-readable instructions thereon which, when executed by a processor, implement a method for classifying a candidate red-eye object in a digital image, the method comprising the acts of:
- i) selecting a candidate red-eye object in a digital image;
- ii) converting RGB pixels of the candidate red-eye object into YUV pixels;
- iii.a) summing the YUV pixels satisfying a first constraint that is a function of the YUV pixels, and iii.b) summing the number of pixels satisfying a second constraint:
- x′(r,s)3−x′(r,s)2>δ′min(x(r,s)2,x(r,s)3)
- where:
- x is the digital image with RGB pixels x(r,s)=[x(r,s)1, x(r,s)2, x(r,s)3];
- x′ is a YUV version of X with YUV pixels x′(r,s)=[x′(r,s)1, x′(r,s)2, x′(r,s)3];
- (r,s) are pixel locations;
- and
- δ′ is a tunable parameter;
- iv) determining that the sum of iii.a) is greater than or equal to a first scaled version of the total number of YUV pixels in the candidate red-eye object, and determining that the sum of iii.b) is less than or equal to a second scaled version of the total number of YUV pixels in the candidate red-eye object;
- and
- v) transforming the candidate red-eye object into a true red-eye object.
Dependencies are inferred from the claim text.