Patent Abstract:
Peteye is the appearance of an unnatural coloration (not necessarily red) of the pupils in an animal appearing in an image captured by a camera with flash illumination. Systems and methods of detecting and correcting peteye are described. In one aspect a classification map segmenting pixels in the input image into peteye pixels and non-peteye pixels is generated based on a respective segmentation condition on values of the pixels. Candidate peteye pixel areas are identified in the classification map. The generating and the identifying processes are repeated with the respective condition replaced by a different respective segmentation condition on the pixel values.

Full Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
   This application relates to the following co-pending applications, each of which is incorporated herein by reference: U.S. patent application Ser. No. 10/424,419, filed Apr. 28, 2003, by Huitao Luo et al., and entitled “DETECTING AND CORRECTING RED-EYE IN A DIGITAL IMAGE;” U.S. patent application Ser. No. 10/653,019, filed on Aug. 29, 2003, by Huitao Luo et al., and entitled “DETECTING AND CORRECTING RED-EYE IN AN IMAGE;” and U.S. patent application Ser. No. 10/653,021, filed on Aug. 29, 2003, by Huitao Luo et al., and entitled “SYSTEMS AND METHODS OF DETECTING AND CORRECTING REDEYE IN AN IMAGE SUITABLE FOR EMBEDDED APPLICATIONS.” 
   BACKGROUND 
   Redeye is the appearance of an unnatural reddish coloration in the pupils of a person appearing in an image captured by a camera with flash illumination. Peteye is the appearance of an unnatural coloration (not necessarily red) of the pupils in an animal appearing in an image captured by a camera with flash illumination. Redeye and peteye are caused by light from the flash illumination reflecting off the retina and returning to the camera. Redeye typically results from light reflecting off blood vessels in the retina, whereas peteye typically results from light reflecting off a reflective layer of the retina. 
   Image processing techniques have been proposed for detecting and correcting redeye in color images of humans. These techniques typically are semi-automatic or automatic. Semi-automatic redeye detection techniques rely on human input. For example, in some semi-automatic redeye reduction systems, a user must manually identify to the system the areas of an image containing redeye before the defects can be corrected. Many automatic human redeye reduction systems rely on a preliminary face detection step before redeye areas are detected. A common automatic approach involves detecting human faces in an image and, subsequently, detecting eyes within each detected face. After the eyes are located, redeye is identified based on shape, coloration, and brightness of image areas corresponding to the detected eye locations. 
   Detecting and correcting peteye are significantly more difficult than detecting and correcting redeye because peteye may be any of a variety of colors and face detection cannot be used to localize peteyes in an image. In addition, the reflective retinal layer that is present in the eyes of many animals, such as dogs and cats, can cause a variety of peteye colors as well as brightly glowing large white peteyes. Although techniques for detecting and correcting redeye in images may be used to correct some peteyes, such systems and methods cannot satisfactorily detect and correct the majority of peteyes that appear in images. What are needed are systems and methods that are designed specifically to detect and correct peteyes in images. 
   SUMMARY 
   In one aspect of the invention, a classification map segmenting pixels in the input image into peteye pixels and non-peteye pixels is generated based on a respective segmentation condition on values of the pixels. Candidate peteye pixel areas are identified in the classification map. The generating and the identifying processes are repeated with the respective condition replaced by a different respective segmentation condition on the pixel values. 
   In another aspect of the invention, pixels in the input image are segmented into an animal-fur color class and a non-animal-fur color class. Candidate peteye pixel areas corresponding to respective clusters of pixels in the non-animal-fur color class are identified in the input image. Ones of the identified candidate peteye pixel areas are selected as detected peteye pixel areas. Ones of the pixels in the detected peteye pixel areas are recolored. 
   In another aspect of the invention, pixels in the input image are segmented into peteye pixels and non-peteye pixels based on a mapping of the input image pixels into a one-dimensional luminance space. Candidate peteye pixel areas are identified in the input image based on the segmented peteye pixels. Ones of the identified candidate peteye pixel areas are selected as detected peteye pixel areas. Ones of the pixels in the detected peteye pixel areas are recolored. 
   Other features and advantages of the invention will become apparent from the following description, including the drawings and the claims. 

   
     DESCRIPTION OF DRAWINGS 
       FIG. 1  is a block diagram of an embodiment of a system for detecting and correcting peteye in an image. 
       FIG. 2  is a block diagram of components of an embodiment of a peteye detection module. 
       FIG. 3  is a block diagram of components of an embodiment of an initial candidate peteye detection module. 
       FIG. 4  shows the various maps that are generated in an embodiment of a method of identifying initial candidate peteye pixel areas in an input image. 
       FIG. 5  is a histogram of animal fur colors obtained from a set of images and quantized into a set of predefined color ranges. 
       FIG. 6  is an image of a classification map that is derived by segmenting pixels in an image of a dog into an animal-fur color class and a non-animal-fur color class. 
       FIG. 7  is a block diagram of an embodiment of a single peteye verification classifier selecting candidate peteye pixel areas from a set of initial candidate peteye pixel areas. 
       FIG. 8A  is a diagrammatic view of an embodiment of a graphical user interface presenting an image of a dog and a user-controlled pointer overlayed on the image. 
       FIG. 8B  is a diagrammatic view of the graphical user interface shown in  FIG. 8A  after a user has moved the pointer over a candidate peteye pixel area. 
       FIG. 9  is a flow diagram of an embodiment of a method of correcting detected peteye pixels. 
       FIG. 10  shows a detected peteye pixel area and cropping lines for corner regions. 
       FIG. 11  is a flow diagram of an embodiment of a method of correcting redeye pixels in an image. 
       FIG. 12A  is an exemplary grayscale iris area surrounded by a neighborhood area. 
       FIG. 12B  is another exemplary grayscale iris area surrounded by a set of eight neighborhood areas. 
       FIG. 13A  shows inner and outer bounding regions derived from a peteye pixel area and a corresponding grayscale iris pixel area. 
       FIG. 13B  shows inner and outer peteye pixel corrections regions used in an embodiment of a method of correcting peteye in an image. 
       FIG. 14  is a flow diagram of an embodiment of a method of recoloring peteye pixels in detected peteye pixel areas. 
       FIG. 15  is a graph of darkening factors plotted as a function of a green color component value of a pixel of an input image. 
       FIG. 16  is a flow diagram of an embodiment of a method of correcting peteye pixel areas containing large glowing glint. 
       FIG. 17  is a diagrammatic view of a glint correction region inscribed in a peteye pixel area. 
   

   DETAILED DESCRIPTION 
   In the following description, like reference numbers are used to identify like elements. Furthermore, the drawings are intended to illustrate major features of exemplary embodiments in a diagrammatic manner. The drawings are not intended to depict every feature of actual embodiments nor relative dimensions of the depicted elements, and are not drawn to scale. 
   I. System Overview 
   The embodiments that are described in detail below are designed specifically to detect and correct peteyes in images. As a result, these embodiments are capable of satisfactorily detecting and correcting the majority of peteyes that appear in images. Some of these embodiments are able to detect a wide variety of different peteyes using multiple classification maps that segment pixels into peteye pixels and non-peteye pixels. Each of the classification maps is generated based on a different respective segmentation condition on the values of the pixels, where each segmentation condition is selected to increase the contrast between the pixels typically contained in a respective type of peteye area and surrounding non-peteye pixels. In some embodiments, the contrast between peteye pixels and non-peteye pixels is increased by segmenting pixels into a specified animal-fur color class and a non-animal-fur color class. In addition, some of these embodiments apply type-specific peteye color correction processes to the peteye pixels in the detected peteye pixel areas to generate a corrected image. 
     FIG. 1  shows an embodiment of a system  10  for detecting and correcting peteye pixels in an input image  12  that includes a peteye detection module  14  and a peteye correction module  16 . The input image  12  may correspond to any type of image, including an original image that was captured by an image sensor (e.g., a video camera, a still image, or an optical scanner) or a processed (e.g., sub-sampled, filtered, reformatted, enhanced or otherwise modified) version of such an original image. 
   The peteye detection module  14  semi-automatically detects areas  18  in input image  12  likely to contain peteye. In particular, the peteye detection module  14  automatically detects candidate peteye pixel areas in the input image  12  and selects ones of the candidate peteye pixel areas as the detected peteye pixel areas  18  based on the user&#39;s selection of areas of the input image  12  coincident with respective ones of the candidate peteye pixel areas. The peteye correction module  16  automatically corrects the detected peteye areas  18  by applying type-specific peteye color correction processes to the peteye pixels in the detected peteye pixel areas  18  to generate a corrected image  20 . In some cases, multiple type-specific color correction processes will apply to a detected peteye area  18 . In these cases, the user may have the peteye correction module  16  apply multiple ones of the applicable type-specific color correction processes to the peteye pixels in the corrected ones of detected peteye pixel areas  18 . 
   In some embodiments, the peteye detection module  14  and the peteye correction module sequentially process the input image  12  with respect to each peteye type. In other embodiments, the peteye detection module  14  detects all peteye types in the input image  12  and then the peteye correction module  16  corrects the detected peteyes that are selected by the user. 
   In general, the peteye detection module  14  and the peteye correction module  16  are not limited to any particular hardware or software configuration, but rather they may be implemented in any computing or processing environment, including in digital electronic circuitry or in computer hardware, firmware, device driver, or software. The peteye detection module  14  and the peteye correction module  16  may be incorporated into any system or method in which such functionality is desired, including embedded environments, which typically have limited processing and memory resources. For example, the peteye detection module  14  and the peteye correction module  16  may be embedded in the hardware of any one of a wide variety of electronic devices, including digital cameras, printers, and portable electronic devices (e.g., mobile phones and personal digital assistants). 
   II. Detecting Peteye Pixel Areas 
   A. Peteye Detection Module Overview 
   Referring to  FIG. 2 , in some embodiments, the peteye detection module  14  includes an initial candidate detection module  22 , a candidate peteye verification module  24 , and a detected peteye pixel area selection module  25 . The initial candidate detection module  22  identifies a set of initial candidate peteye pixel areas  26  in the input image  12 , and the candidate peteye verification module  24  filters false alarms (i.e., candidate peteye pixel areas with a low likelihood of corresponding to actual peteyes in input image  12 ) from the set of initial candidate peteye pixel areas  26  to produce a set of candidate peteye pixel areas  27 . The detected peteye pixel area selection module  25  selects the set of detected peteye areas  18  from the set of candidate peteye pixel areas based on user input. 
   B. Initial Candidate Detection Module 
   1. Overview 
   As explained in detail below, in some embodiments, initial candidate detection module  22  identifies candidate peteye pixel areas using multiple classification maps that segment pixels into peteye pixels and non-peteye pixels based on different respective segmentation conditions. In this way, initial candidate detection module  22  ensures that there is a high likelihood that all of the actual peteyes in the input image  12  are included in the set of initial candidate peteye pixel areas  26 . 
   Referring to  FIG. 3 , in some embodiments, the initial candidate detection module  22  includes a classification map generation module  28  and a segmentation module  30 . The classification map generation module  28  generates multiple classification maps  32 , each of which segments pixels in the input image  12  into peteye pixels and non-peteye pixels. The segmentation module  30  segments the peteye pixels in the classification maps  32  into the initial candidate peteye pixel areas  26 . 
   The classification map generation module  28  generates each of the classification maps  32  based on a different respective segmentation condition on the values of the pixels. Each of the segmentation conditions is selected to increase the contrast between the pixels that typically are contained in a respective type of peteye area and surrounding non-peteye pixels. In the illustrated embodiments, the segmentation conditions are selected to increase the likelihood of identifying the following common types of peteyes: red peteyes (designated Type I); bright peteyes (designed Type II); non-pet-fur-color peteyes (designated Type III); very bright peteyes (designated Type IV); and bright peteyes with bright surroundings (designated Type V). In an exemplary sample of 227 images containing 402 peteyes, it was observed that Type I peteyes composed approximately 23% of the sample, Type II peteyes composed approximately 33% of the sample, Type III peteyes composed approximately 26% of the sample, Type IV peteyes composed approximately 12% of the sample, and Type V peteyes composed approximately 3% of the sample. 
   In the embodiments that are described in detail below: the segmentation condition for Type I peteyes is a threshold level of red contrast between red peteyes and their non-red neighbors; the segmentation condition for Type II peteyes is a first threshold level of luminance contrast between bright peteyes and their less bright neighbors; the segmentation condition for Type III peteyes is contrast between non-pet-fur color peteye pixels and their pet-fur colored neighbors, where white is a pet-fur color; the segmentation condition for Type IV peteyes is a second threshold level of luminance contrast between bright peteyes and their less bright neighbors, where the second threshold level of luminance contrast is higher than the first threshold level of luminance contrast used in the segmentation condition for Type II peteyes; the segmentation condition for Type V peteyes is contrast between non-pet-fur color peteye pixels and their pet-fur colored neighbors, where white is a non-pet-fur color. 
   2. Generating Classification Maps 
     FIG. 4  shows a set of classification maps  34 ,  36 ,  38 ,  40 ,  42  that are generated in an embodiment of a method of identifying the initial candidate peteye pixel areas  26  in the input image  12 . The classification maps  34 - 42  may be generated sequentially or in parallel. The classification map  34  segments pixels in the input image  12  into Type I peteye pixels and non-peteye pixels. The classification map  36  segments pixels in the input image  12  into Type II peteye pixels and non-peteye pixels. The classification map  38  segments pixels in the input image  12  into Type III peteye pixels and non-peteye pixels. The classification map  40  segments pixels in the input image  12  into Type IV peteye pixels and non-peteye pixels. The classification map  42  segments pixels in the input image  12  into Type V peteye pixels and non-peteye pixels. 
   a. Generating Classification Maps for Type I Peteyes 
   The classification map  34  for Type I peteyes is generated by producing a redness map  44  from the input image  12  and applying to the redness map  44  a redness threshold that segments the pixels of the input image  12  into Type I peteye pixels and non-peteye pixels. The redness map  44  may be produced by mapping the values of the pixels of the input image  12  into a one-dimensional redness color space. 
   In accordance with one redness color space model, the classification map generation module  28  converts the input image  12  into the CIE L*a*b* color space. The classification map generation module  28  then binarizes the L*a*b* color space representation of the input image  12  based on one or more of the contrast threshold curves that are described in U.S. patent application Ser. No. 10/653,019, filed on Aug. 29, 2003, by Huitao Luo et al., and entitled “DETECTING AND CORRECTING RED-EYE IN AN IMAGE,” to produce the classification map  34  for Type I peteyes. 
   In accordance with another redness color space model, the classification map generation module  28  initially computes measures of pixel redness in the input image  12  to generate the redness map  44 . Any one of a variety of different measures of pixel redness may be used to generate the redness map  44  from input image  12 . In some embodiments, the pixel redness measures are computed based on a ratio of a measure of a red component of pixel energy to a measure of total pixel energy. For example, in one implementation, pixel redness measures (R 0 ) are computed as follows: 
                   R   ⁢           ⁢   0     =         α   ·   r     +     β   ·   g     +     γ   ·   b         r   +   g   +   b   +   d               (   1   )               
where r, g, and b are red, green, and blue component pixel values of input image  12 , respectively, α, β and γ are weighting factors, and d is a prescribed constant with a value selected to avoid singularities and to give higher weights to bright pixels. In one exemplary implementation in which each of r, g, and b have values in the range of [0,255], α=204, β=−153, and γ=51, and d has a value of 1. Based on the mapping of equation (1), the redness of each pixel of input image  12  is mapped to a corresponding pixel of the redness map  44  having a redness value given by equation (1).
 
   In other embodiments, the redness map  44  is computed using different respective measures of redness. For example, in one exemplary implementation, pixel redness measures (R 0 ) for the redness map  44  are computed as follows: R 0 =(255·r)/(r+g+b+d) when r&gt;g, r&gt;b; otherwise R 0 =0. Other representative redness measures (R 1 , R 2 , R 3 , R 4 ) that may be used to compute the redness map  44  are expressed in equations (2)-(5) below: 
                   R   ⁢           ⁢   1     =       r   2         (     r   +   g   +   b   +   1     )     2               (   2   )                 R   ⁢           ⁢   2     =       r   2         (     g   +   b     )     2               (   3   )                 R   ⁢           ⁢   3     =       r   +   b       (     r   +   g   +   b   +   1     )               (   4   )                 R   ⁢           ⁢   4     =     Cr       (     Cb   +   1     )     2               (   5   )               
where r, g, and b are red, green, and blue component pixel values of input image  12 , respectively, and Cr and Cb are the red and blue chrominance component pixel values of the input image  12  in the YCbCr color space.
 
   Next, the classification map generation module  28  binarizes the redness map  44  to produce the classification map  34 . In some implementations, the redness map  44  is binarized by applying a linear adaptive threshold filter to the redness map  44 . In one exemplary implementation of a linear adaptive threshold filter, the value of each pixel in the redness map  44  is compared with the average of its neighboring pixels, where the neighborhood is defined as a square d×d pixel window, centered at the current pixel. The window size d is defined with respect to the original image size (h×w) as follows:
 
 d =min( h, w )/13  (6)
 
where h and w are the height and width of the original input image. If the current pixel has a higher redness value than its neighborhood average, the filter output is one; otherwise the output is zero.
 
   b. Generating Classification Maps for Type II Peteyes 
   The classification map  36  for Type II peteyes is generated by producing a luminance map  46  from the input image  12  and applying to the luminance map  46  a luminance threshold that segments the pixels of the input image  12  into Type II peteye pixels and non-peteye pixels. The luminance map  46  may be produced by mapping the values of the pixels of the input image  12  into a one-dimensional luminance color space. 
   In accordance with one luminance color space model, the classification map generation module  28  initially computes measures of pixel luminance in the input image  12  to generate the luminance map  46 . Any one of a variety of different measures of pixel luminance may be used to generate the luminance map  46  from input image  12 . In some embodiments, the pixel luminance measures L are computed as follows: 
                 L   =         u   ·   r     +     v   ·   g     +     w   ·   b       x             (   7   )               
where r, g, and b are red, green, and blue component pixel values of input image  12 , respectively, u, v, and w are weighting factors, and x is a prescribed constant.
 
   In one exemplary implementation in which each of r, g, and b have values in the range of [0,255], u=77, v=150, w=29, and x=256. Based on the mapping of equation (7), the luminance of each pixel of the input image  12  is mapped to a corresponding pixel of the luminance map  46  having a luminance value given by equation (7). 
   Next, the classification map generation module  28  binarizes the luminance map  46  to produce the classification map  36 . In some implementations, the luminance map  46  is binarized by applying a linear adaptive threshold filter to the luminance map  46 . In one exemplary implementation, the value of each pixel in the luminance map  46  is compared with the average of its neighboring pixels, where the neighborhood is defined as a square d×d pixel window, which is centered at the current pixel, and the window size d is defined with respect to the is original image size (h×w) in accordance with equation (6) above. If the current pixel has a higher luminance value than its neighborhood average, the filter output is one; otherwise the output is zero. 
   c. Generating Classification Maps for Type III Peteyes 
   The classification map  38  for Type III peteyes is generated by producing an animal-fur color map  48  from the input image  12  and labeling pixels in the animal-fur color map  48  classified in a specified animal-fur color class as non-peteye pixels and pixels in the animal-fur color map  48  classified in a specified non-animal-fur color class as Type III peteye pixels. The animal-fur color map  48  may be produced by mapping the values of the pixels of the input image  12  into a quantized color space having a finite set of specified colors each of which is defined by a respective color range. In some embodiments, the animal-fur color map  48  is produced by mapping the pixels in the input image  12  into a quantized color space consisting of a set of twenty-seven non-overlapping quantized color bins. 
   It has been discovered from an analysis of a sample of images of animals that animal-fur colors typically can be classified into a small class of possible animal fur colors. In particular, each image in the sample was cropped to remove non-fur-coated areas and the resulting cropped images were mapped to a quantized color space defined by a set of twenty-seven color names (or bins).  FIG. 5  shows a histogram of the number of pixels in a sample of sixty-two cropped images classified into respective ones of the twenty-seven color bins. As shown by the histogram, most of the animal-fur color images were classified into a small set of the possible color bins: brown, flesh, and five levels of gray, including white (which corresponds to bin number  0 ). Equations (13)-(15) below provide an exemplary definition of these animal-fur colors in which the five levels of gray are defined in terms of a single luminance range. The remaining animal-fur colors that were observed (i.e., olive, maroon, cyan, and pale yellow) were found to correspond to specular reflections from animal fur, reflections from other parts of the image (e.g., sky or grass) near the animals, and image artifacts or compression artifacts. 
   Next, the classification map generation module  28  binarizes the animal-fur color map  48  to produce the classification map  38 . In this process, pixels classified in one of the seven possible animal-fur color bins are segmented into a non-peteye class and pixels classified in any of the other (non-animal-fur) color bins are segmented into a Type III peteye class. 
   In some embodiments, the classification map generation module  28  produces the classification map  38  directly from the input image without producing the animal-fur color map  48  in accordance with the following process: 
   1. Convert the input image  12  into the YCrCb color space. For example, in some embodiments, if the input image  12  originally is specified in the RGB color space, the input image pixels are mapped into the YCrCb color space as follows:
 
 Y= 0.299 ·r+ 0.587 ·g+ 0.112 ·b   (8)
 
 Cr= 0.713266·( r−Y )+128  (9)
 
 Cb= 0.564334·( b−Y )+128  (10)
 
where r, g, and b are red, green, and blue component pixel values of input image  12 , respectively, and Y, Cr, and Cb are the component pixel values in the YCrCb color space.
 
   2. Calculate the chroma and hue for each of the input image pixels as follows: 
   
     
       
         
           
             
               
                 Chroma 
                 = 
                 
                   1.88085 
                   · 
                   
                     
                       
                         Cr 
                         · 
                         Cb 
                       
                       + 
                       
                         Cb 
                         · 
                         Cb 
                       
                     
                   
                 
               
             
             
               
                 ( 
                 11 
                 ) 
               
             
           
           
             
               
                 Hue 
                 = 
                 
                   
                     0.708333 
                     · 
                     Arc 
                   
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     Tangent 
                   
                   ⁢ 
                   
                     ( 
                     
                       Cr 
                       Cb 
                     
                     ) 
                   
                 
               
             
             
               
                 ( 
                 12 
                 ) 
               
             
           
         
       
     
   
   3. Segment pixels of the input image  12  into the non-peteye class if one of the following conditions is true: 
   a. the pixel is in a gray color range defined by:
 
Chroma&lt;25; or  (13)
 
   b. the pixel is in a brown color range defined by:
 
(Chroma&lt;120) AND (Y&lt;120) AND (Hue≧254 OR Hue≦45); or  (14)
 
   c. the pixel is in a flesh color range defined by:
 
(Chroma&lt;115) AND (Y≧120) AND (10≦Hue≧45).  (15)
 
     FIG. 6  shows a classification map  38  that is derived by segmenting pixels in an image of a dog exhibiting a pair of Type III peteyes  50 ,  52  into an animal-fur color class (shown by the gray pixels) and a non-animal-fur color class (shown by the white pixels). As shown in the figure, at least for Type III peteyes, there is high contrast between the white pixels in the classification map  38  that correspond to the non-animal-fur color peteyes  50 ,  52  and the surrounding gray pixels that correspond to animal-fur color pixels. 
   d. Generating Classification Maps for Type IV Peteyes 
   The classification map  40  for Type IV peteyes is generated in the same way that the classification map  36  for Type II peteye is generated, except that the luminance threshold used to binarize the luminance map is increased to a higher empirically determined threshold value. For example, in some implementations, if the luminance value of a current pixel is higher than the average neighborhood luminance by an empirically determined additive or multiplicative scale factor, the current pixel is classified as a potential Type IV peteye pixel and set to one in the classification map  40 ; otherwise the current pixel is classified as a non-Type IV peteye pixel and set to zero in the classification map  40 . 
   e. Generating Classification Maps for Type V Peteyes 
   The classification map  42  for Type V peteyes is generated in the same way that the classification map  38  for Type III peteye is generated, except that white pixels (e.g., pixels with red, green, and blue component values all equal to 255 in an 8-bit RGB color space representation) are classified as non-animal-fur color pixels. 
   3. Identifying Initial Candidate Peteye Pixel Areas 
   In the illustrated embodiment, the classification maps  34 - 42  are passed to the segmentation module  30 , which generates the set of initial candidate peteye pixel areas  26  by generating objects for all the pixels set to one in the classification maps. The segmentation module  30  segments the candidate peteye pixels into peteye and non-peteye classes based on pixel connectivity using any one of a wide variety of pixel connectivity algorithms. Each pixel area that is segmented into the peteye class is labeled as a candidate peteye area. In the embodiments illustrated herein, each candidate peteye area is represented by a boundary rectangle (or box). In other embodiments, the candidate peteye pixel areas may be represented by non-rectangular shapes. 
   C. Candidate Peteye Verification Module 
   As explained in detail below, the candidate peteye verification module  24  ( FIG. 2 ) classifies the candidate peteye pixel areas based on consideration of multiple features in parallel using a machine learning framework to verify that candidate peteye pixel areas correspond to actual peteyes in input image  12  with greater accuracy and greater efficiency. 
     FIG. 7  shows an implementation of the candidate peteye verification module  24  that is implemented by a single peteye verification classifier  54 . The single peteye verification classifier filters candidate peteye pixels from the initial set  26  based on a projection of input image data into a feature space spanned by multiple features to generate feature vectors respectively representing the candidate peteye pixel areas  26  in the feature space. Candidate peteye pixel areas in the initial set  26  are filtered out based on the generated feature vectors. In particular, the single peteye verification classifier  54  classifies the candidate peteye pixel area into the set of candidate peteye pixel areas  27  or a set of false alarms  56 . 
   Additional details regarding the structure and operation of the single peteye verification classifier  54 , as well as a description of the feature vectors that are used by the single peteye verification classifier  54  to classify the initial candidate peteye pixel areas  26 , can be obtained from the description of the single-eye verification classifier contained in U.S. patent application Ser. No. 10/653,019, filed on Aug. 29, 2003, by Huitao Luo et al., and entitled “DETECTING AND CORRECTING RED-EYE IN AN IMAGE.” 
   D. Selecting Ones of the Candidate Peteye Pixel Areas as Detected Pixel Areas 
   As explained above, the detected peteye pixel area selection module  25  selects the set of detected peteye areas  18  from the set of candidate peteye pixel areas  27  based on user input. In particular, the detected peteye pixel area selection module  25  selects ones of the candidate peteye pixel areas  27  as the detected peteye pixel areas  18  based on the user&#39;s selection of areas of the input image  12  that are coincident with respective ones of the candidate peteye pixel areas  27 . 
     FIG. 8A  shows an embodiment of a graphical user interface  58  presenting an image  60  of a dog and a user-controlled pointer  62  overlayed on the image.  FIG. 8A  also shows a bounding box  64  that is superimposed on a portion of the eye of the dog shown in the image  60 . The bounding box  64  contains one of the candidate peteye pixel areas  27  that were verified by the candidate peteye verification module  24 . In some embodiments, the bounding box  64  is hidden from the user. In other embodiments, the bounding box  64  is visible to the user. 
     FIG. 8B  shows the graphical user interface  58  after a user has moved the pointer  62  into an area of the image  60  that coincides with the candidate peteye pixel area defined by the bounding box  64 . In response to the user&#39;s selection of the area of the image  60  specified by the pointer  62  (e.g., by pressing a button on an input device, such as a computer mouse), the detected peteye pixel area selection module  25  selects the candidate peteye pixel area  27  corresponding to the bounding box  64  as one of the detected peteye areas  18 . 
   III. Peteye Correction 
   Referring to  FIG. 9 , after the detected peteye pixel areas  18  have been selected, the pixels within the detected peteye pixel areas  18  are classified as peteye pixels and non-peteye pixels (block  66 ). The pixels within each of the detected peteye pixel areas  18  that are classified as peteye pixels are then recolored (block  68 ). 
   Referring to  FIG. 10 , in some embodiments, before the detected peteye pixel areas  18  are classified, the corners of the detected redeye pixel areas  18  are cropped to form an octagonal shape that approximates the shape typical of animal eye pupils. The amount by which the corners are cropped is empirically determined. In one exemplary illustration, the side dimension of each corner region corresponds to 15% of the corresponding side (horizontal or vertical) dimension of the detected redeye pixel areas  18 . 
   A. Classifying Peteye Pixels 
     FIG. 11  shows an embodiment of a process of classifying peteye pixels in the detected peteye pixel areas  18 . In some embodiments, the pixels within each of the detected peteye pixel areas  18  are classified independently of the other peteye pixel areas. In these embodiments, pixel classification also is performed per pixel and per pixel line without any reference to (or coherence with) adjacent (above or below) pixel lines. 
   In some embodiments, a number of fast heuristics are applied to the candidate peteye areas to eliminate false alarms (i.e., candidate peteye pixel areas that are not likely to correspond to actual peteye areas), including aspect ratio inspection and shape analysis techniques. For example, in some implementations, atypically elongated candidate peteye areas are removed. 
   In the embodiment shown in  FIG. 11 , the detected peteye pixel area  18  is skipped and the next detected peteye pixel area  18  is processed ( FIG. 11 , block  70 ), if the detected peteye pixel area  18  is atypically large ( FIG. 11 , block  72 ). In some implementations, a detected peteye pixel area  18  is considered to be atypically large if any dimension (e.g., width or height) is larger than an empirically determined number of pixels. 
   The detected peteye pixel area  18  also is skipped if the aspect ratio of the detected peteye pixel area  18  is outside of an empirically determined valid range of aspect ratio values (block  74 ). The aspect ratio includes the ratio of width-to-height of the corresponding bounding box and the ratio of height-to-width of the corresponding bounding box. In some implementations, the valid range of aspect ratio values is from 1:2 to 2:1. 
   The pixels in the detected peteye pixel areas that are not too large and that have an aspect ratio within the specified valid range, are classified as candidate peteye pixels and non-candidate peteye pixels line-by-line based on horizontal coherence ( FIG. 11 , block  76 ). In some implementations, if a given peteye pixel is located adjacent to a pixel previously classified as a candidate peteye pixel and has a value (i.e., a redness value for Type I peteyes or a luminance value for Type II and Type IV peteyes) that is greater than an empirically determined, type-specific threshold value, then the given pixel also is classified as a candidate peteye pixel. In these implementations, the pixels of Type III peteyes are not classified by horizontal coherence. 
   Referring to  FIGS. 11 and 12A , the pixels in the current detected peteye pixel area  18  are classified as candidate peteye pixels and non-candidate peteye pixels based on regions that are derived from a detected peteye pixel area  18  and a corresponding grayscale iris pixel area ( FIG. 11 , block  78 ). In some embodiments, a detected peteye pixel area  18  is represented by a rectangle  80  and the associated iris is represented as a square  82 . The iris is assumed to share the same center with the detected peteye pixel area  80 . Note that each of the detected peteye area  80  is not necessarily identical to its associated grayscale iris area  82 . In some embodiments, the square grayscale iris area  82  is computed over a grayscale plane using the following search algorithm. 
   Initially, a grayscale map is computed by mapping the pixels of input image  12  in accordance with a grayscale mapping G, given by G=MIN(G 1 , G 2 ), where MIN is a function that outputs the minimum of G 1  and G 2 , which are given by:
 
 G 1=0.299 ×r+ 0.587 ×g+ 0.114 ×b   (13)
 
 G 2=0.299×(255 −r )+0.587 ×g+ 0.114 ×b   (14)
 
where r, g and b are red, green and blue values for each pixel within the region and the grayscale values are obtained for each pixel and averaged over the region. In this grayscale mapping, G 1  is a standard grayscale mapping computed from (r, g, b), whereas G 2  is the grayscale mapping computed from (255-r, g, b). The grayscale mapping G 2  handles instances of “glowing” peteyes (i.e., when a peteye appears much brighter than its surroundings). In accordance with the above approach, such atypical “glowing” peteyes are mapped to a grayscale channel that allows them to be treated in the same way as typical peteyes.
 
   Next, a search is performed over the computed grayscale map to locate one or more areas corresponding to irises. In this search, it is assumed that the iris area  82  shares the same center with its detected peteye area  80 . The size of the iris area  82  is determined based on a comparison of a candidate square box (box  8  in  FIG. 12B ) with each of its eight nearest neighbors (boxes  0 - 7  in  FIG. 12B ). In one implementation, an initial area that encompasses the surrounding areas  0 - 7  is partitioned into nine equal-sized nearest neighbor boxes (numbered  0 - 8 ). The size of the final optimal grayscale box  82  (or square) is determined by selecting a size that maximizes the grayscale contrast between the center box (box  8 ) and its surrounding neighbors (boxes  0 - 7 ). In this search process, only one variable is involved: the side length of the center box. In one implementation, a brute force search technique is used to determine the final size of grayscale iris area  82 . 
   Referring to  FIGS. 11 and 13A , the peteye pixel areas are classified with respect to an inner bounding region  84  and an outer bounding region  86 , which are derived from the grayscale iris area  82 . The inner bounding region  84  is centered at the center of the detected peteye pixel area  18  being processed and has dimensions (e.g., width and height) that correspond to the average of the dimensions of the detected peteye pixel area  18  and its corresponding grayscale iris area  82 . The outer bounding region  86  also is centered at the center of the detected peteye pixel area  18 . In one implementation, the dimensions of the outer bounding region  86  are 50% larger than the corresponding dimensions of the inner bounding region  84  if the inner bounding region  84  is larger than two pixels; otherwise, the dimensions of the outer bounding region  86  are 200% larger than the corresponding dimensions of the inner bounding region  84 . 
   The pixels between the inner and outer bounding regions  84 ,  86  are classified as either candidate peteye pixels or non-candidate peteye pixels based on application of a grayscale threshold to the computed grayscale values of the pixels as follows. In some implementations the green channel in RGB color space is used to approximate the grayscale values of pixels. In one implementation, the applied grayscale threshold corresponds to the average of (1) the average of the grayscale values within the inner bounding region  84  and (2) the average of the grayscale values between the inner and outer bounding regions  84 ,  86 . For example, if the average of the gray values within the inner bounding region  84  is 90 and the average of the gray values outside the inner bounding region  84  but within the outer bounding region  86  is 120, then the average gray value, which is (90+120)/2=105, is the grayscale threshold used to segment the pixels between the inner and outer bounding regions  84 ,  86 . Pixels between the inner and outer bounding regions  84 ,  86  having grayscale values below the computed grayscale threshold are classified as candidate peteye pixels. 
   All of the pixels within the outer bounding region  86  shown in  FIG. 13A  that have been classified as candidate peteye pixels in the process blocks  76  and  78  of  FIG. 11  are classified as peteye pixels based on connectivity, with stringent requirements to remove fragments, outliers, and noise. 
   Referring to  FIGS. 11 and 13B , a peteye pixel correction region  88  that encompasses (or encircles) all pixels within the outer bounding region  86  that are classified as peteye pixels is identified ( FIG. 11 , step  90 ). In some implementations, the peteye pixel correction region  88  has an elliptical shape. In the illustrated example, the peteye pixel correction region  88  has a circular shape. In addition to the peteye pixel correction region  88 , a peteye pixel smoothing region  92  surrounding the peteye pixel correction region  88  is computed. In the example illustrated in  FIG. 13B , the peteye pixel smoothing region  92  is defined by a circular boundary  94  that is concentric with the peteye pixel correction region  88  and has a radius that is 50% larger than the radius of the peteye pixel correction region  88 . 
   Referring back to  FIG. 11 , after the classified peteye pixels have been classified ( FIG. 11 , blocks  76 ,  78 ) and the peteye pixel correction and smoothing regions  88 ,  92  have been identified ( FIG. 11 , block  90 ), the pixels in the detected peteye pixel areas  18  that have been classified as peteye pixels are recolored ( FIG. 11 , block  96 ). The process is repeated until all the detected peteye pixel areas have been corrected ( FIG. 11 , block  98 ). 
   B. Recoloring Peteye Pixels 
   The peteye pixels are corrected in accordance with a Type-specified pixel correction process shown in  FIG. 14 . 
   1. Recoloring Peteye Pixels in Type I Pixel Areas 
   If the detected peteye pixel area  18  is a Type I peteye pixel area ( FIG. 14 , block  100 ), the peteye pixels in the peteye pixel correction region  88  are corrected as follows. 
   Color values of the peteye pixels are corrected by desaturating ( FIG. 14 , block  102 ) and darkening ( FIG. 14 , block  104 ) original color values in accordance with color correction darkening factors and weights that are computed for the peteye pixels to be corrected in accordance with the process described below. The darkening factors and weights indicate how strongly original color values of the peteye pixels are to be desaturated (i.e., pushed towards neutral or gray values). As explained in detail below, these two factors vary with pixel location relative to the center of the peteye pixel correction region  88  to give a smooth transition between the pixels in the input image  12  that are changed and those that are not to avoid artifacts. 
   The darkening factors are computed based on luminance (or gray) values of the input image pixels. In one implementation, the darkening factors are computed based on the graph shown in  FIG. 15 , where the luminance (or gray) level of each peteye pixel is assumed to vary over a range of [lum min , lum max ]=[0, 1]. In one implementation, the green color channel is used to estimate luminance values. Other implementations may use different estimates or measures of luminance values. In the illustrated implementation, the minimum darkening factor (m i ) is set to 0.6 and the maximum darkening factor (m f ) is set to 1.0. These parameters may be set to different values in other implementations. In this formulation, the darkening factor values decrease with the darkness levels of the pixels. That is, lower (i.e., darker) luminance (or gray) values are associated with lower darkening factors. Since the darkening factors influence pixel values in a multiplicative way in the implementation described below, darker pixels (i.e., pixels with lower luminance values) identified as peteye pixels are darkened more than lighter pixels (i.e., pixels with higher luminance values). 
   The weights (wt) are set for a given peteye pixel based on the number of peteye pixels that neighbor the given pixel. For example, in one implementation, the weights may be set as follows: 
                 wt   =     {         0           peteye   ⁢           ⁢   neighbors     =   0             .33             peteye   ⁢           ⁢   neighbors     =   1     ,   2   ,   3             .67             peteye   ⁢           ⁢   neighbors     =   4     ,   5   ,   6             1             peteye   ⁢           ⁢   neighbors     =   7     ,   8                     (   15   )               
where “peteye neighbors” corresponds to the number of peteye pixels that neighbor the given pixel being assigned a weighting factor. In this formulation, peteye pixels near the center of the peteye pixel correction region  88  are assigned higher weights than peteye pixels near the boundaries of the peteye pixel correction region  88 .
 
   In some RGB color space implementations, the color values (red, green, blue) of each input image pixel identified as a peteye pixel are corrected to the final color values (R 1 , G 1 , B 1 ) as follows: 
   If (mask=1), tmp=dark[green−grn min ] 
   Else tmp=1
 
 R   1 =( wt*tmp *green+(1 −wt )*red)
 
 G   1 =( wt*tmp *green+(1 −wt )*green)
 
 B   1 =( wt*tmp *green+(1 −wt )*blue)
 
In these embodiments, it is assumed that the color components of the input image pixels are defined with respect to the RGB color space. These embodiments readily may be extended to other color space representations. It is noted that if wt=1, pixel values are pushed all the way to neutral (i.e., the pixel values are set to the same shade of gray). If wt=0, none of the color component values of the corresponding pixel are changed. In this implementation, lower luminance pixels (i.e., smaller green values) generally are pushed darker than higher luminance pixels, which have their luminance unchanged.
 
   The original color values of peteye pixels in the peteye pixel smoothing region  92  are corrected in a similar way as the peteye pixels in the pixel correction region  88 , except that the relative amount of correction varies from 90% at the boundary with the peteye pixel correction region  88  to 20% at the boundary  94  of the peteye pixel smoothing region  92 . This smoothing or feathering process reduces the formation of disjoint edges in the vicinity of the corrected peteyes in the corrected image. 
   Referring back to  FIG. 14 , if the user is satisfied with the results of the desaturating and darkening recoloring processes ( FIG. 14 , block  106 ), the peteye pixel correction process is terminated ( FIG. 14 , block  108 ). If the user is not satisfied with the results ( FIG. 14 , block  106 ), the user may have the peteye pixel detection and correction system  10  re-perform the peteye detection process described above on the corrected image  20 . The user may then re-select the previously selected peteye pixel area  64  ( FIGS. 8A and 8B ) using, for example, the user interface shown in  FIGS. 8A and 8B . If the selected area corresponds to a detected peteye pixel area  18 , the peteye correction module  16  will recolor the peteye pixels in the detected peteye pixel area  18  in accordance with the process shown in  FIG. 11 . Since Type I peteye pixel areas have already been corrected, the newly detected peteye pixel areas should correspond to one or more of the other types of peteye pixel areas (i.e., peteye Types II-V). 
   In some embodiments, a user who is not satisfied with the peteye pixel correction results may select an undo command to return the image to its previous state. 
   2. Recoloring Peteye Pixels in Type II or Type IV Pixel Areas 
   Referring to  FIG. 14 , if the detected peteye pixel area  18  is a Type II peteye pixel area or a Type IV peteye pixel area ( FIG. 14 , block  110 ), the peteye pixels in the peteye pixel correction region  88  are corrected as follows. 
   Initially, the color values of the peteye pixels in Type II and Type IV peteye pixel areas are corrected by desaturating ( FIG. 14 , block  112 ) and darkening ( FIG. 14 , block  114 ) the original color values in accordance with the processes described above in connection with peteye pixels in Type I peteye pixel areas. 
   Referring to  FIGS. 14 ,  16 , and  17 , After the desaturating and darkening the peteye pixel areas ( FIG. 14 , blocks  112 ,  114 ), the peteye correction module  16  processes the detected peteye pixel areas as follows to correct large glowing glint. 
   In this process, the pixels in the detected peteye pixel area are classified based on glint (block  116 ). In one implementation, peteye pixel areas are classified as containing large glowing glint if the percentage of the non-peteye pixels in an oval glint correction region  118  inscribed in a boundary box  80  corresponding to the detected peteye pixel area  18  is greater than a heuristically determined threshold (see  FIG. 17 ). In another implementation, peteye pixel areas are classified as containing large glowing glint if the average luminance value computed over the oval glint correction region  118  is greater than a heuristically determined threshold. In another implementation, peteye pixel areas are classified as containing large glowing glint if the average luminance value computed over the oval glint correction region  118  is greater than the average luminance value computed over the regions of the boundary box  80  surrounding the oval glint correction region  118  by a heuristically determined threshold. 
   If a detected peteye pixel area is classified as containing large glowing glint ( FIG. 14 , block  120 ), the peteye correction module  16  performs glint correction as follows ( FIG. 14 , block  122 ). 
   Referring to  FIG. 16 , initially, the center (C i ,C j ) of the glint correction region  118  is computed ( FIG. 16 , block  124 ;  FIG. 17 ). In one implementations, the center (C i C j ,) of the glint correction region  118  is the location of the pixel with the maximal luminance value. In instances in which there are multiple pixels with the maximal luminance value, the pixel location that is closest to the average of the locations of the pixels with the maximal luminance value is selected as the center (C i ,C j ) of the glint correction region  118 . For each pixel (i,j) within the oval glint correction region  118 , the distance D 1  to the center (C i ,C j ) of the glint correction region  118  is determined. The darkening factor a for each pixel is computed as follows ( FIG. 16 , block  126 ): 
                 α   =     1.0   -     0.3   ⁢       (       D   ⁢           ⁢   1       D   ⁢           ⁢   2       )     0.005                 (   16   )               
where D 2 =(A 2 +B 2 ) 1/2 , and A and B correspond to one-half of the lengths the semiminor and semimajor axes of the oval glint correction region  118 , respectively. The pixels within the glint correction region  118  are darkened in accordance with the computed darkening factors as follows ( FIG. 16 , block  128 ):
 Red FINAL =α·Red INITIAL   (17) Green FINAL =α·Green INITIAL   (18) Blue FINAL =α·Blue INITIAL   (19) 
where Red FINAL , Green FINAL , and Blue FINAL  are the final darkened red, green, and blue color values for the glint corrected pixel, and Red INITIAL , Green INITIAL , and Blue INITIAL  are the initial red, green, and blue color values of the pixel after the desaturating and darkening recoloring processes shown in blocks  112 ,  114  of  FIG. 14 .
 
   The original color values of peteye pixels in the peteye pixel smoothing region  92  are corrected in a similar way as the peteye pixels in the pixel correction region  88 , except that the relative amount of correction varies from 90% at the boundary with the peteye pixel correction region  88  to 20% at the boundary  94  of the peteye pixel smoothing region  92 . This smoothing or feathering process reduces the formation of disjoint edges in the vicinity of the corrected peteyes in the corrected image. 
   Referring back to  FIG. 14 , if the user is satisfied with the results of the desaturating and darkening recoloring processes ( FIG. 14 , block  130 ), the peteye pixel correction process is terminated ( FIG. 14 , block  108 ). If the user is not satisfied with the results ( FIG. 14 , block  130 ), the user may have the peteye pixel detection and correction system  10  re-perform the peteye detection process described above on the corrected image  20 . The user may then re-select the previously selected peteye pixel area  64  ( FIGS. 8A and 8B ) using, for example, the user interface shown in  FIGS. 8A and 8B . If the selected area corresponds to a detected peteye pixel area  18 , the peteye correction module  16  will recolor the peteye pixels in the detected peteye pixel area  18  in accordance with the process shown in  FIG. 11 . Since Type I, II, and IV peteye pixel areas have already been corrected, the newly detected peteye pixel areas should correspond to one or more of the other types of peteye pixel areas (i.e., peteye Types III and V). 
   In some embodiments, a user who is not satisfied with the peteye pixel correction results may select an undo command to return the image to its previous state. 
   3. Recoloring Peteye Pixels in Type III or Type V Pixel Areas 
   Referring to  FIG. 14 , if the detected peteye pixel area  18  is a Type III pixel area or a Type V peteye pixel area ( FIG. 14 , block  132 ), the peteye pixels in the peteye pixel correction region  88  are corrected as follows. 
   Initially, the color values of the peteye pixels in Type III and Type V peteye pixel areas are corrected by desaturating ( FIG. 14 , block  134 ) the original color values in accordance with the desaturation process described above in connection with peteye pixels in Type I peteye pixel areas. 
   If the proportion of non-pet-fur color pixels in the detected peteye pixel area constitutes less than an empirically determined threshold (e.g., 40%) (FIG.  14 , block  136 ), the peteye pixel correction process is terminated ( FIG. 14 , block  108 ). If the proportion of non-pet-fur color pixels in the detected peteye pixel area is greater than the threshold ( FIG. 14 , block  136 ), the peteye pixels are darkened ( FIG. 14 , block  138 ) in accordance with the darkening process described above in connection with peteye pixels in Type I peteye pixel areas. In addition, the pixels in the detected peteye pixel area are classified based on glint ( FIG. 14 , block  140 ) and if a detected peteye pixel area is classified as containing large glowing glint ( FIG. 14 , block  142 ), the peteye correction module  16  performs glint correction ( FIG. 14 , block  144 ) in accordance with the glint correction process described above in connection with peteye pixels in Type III and Type V peteye pixel areas. 
   The original color values of peteye pixels in the peteye pixel smoothing region  92  are corrected in a similar way as the peteye pixels in the pixel correction region  88 , except that the relative amount of correction varies from 90% at the boundary with the peteye pixel correction region  88  to 20% at the boundary  94  of the peteye pixel smoothing region  92 . This smoothing or feathering process reduces the formation of disjoint edges in the vicinity of the corrected peteyes in the corrected image. 
   IV. CONCLUSION 
   The embodiments that are described in detail herein are designed specifically to detect and correct peteyes in images. As a result, these embodiments are capable of satisfactorily detecting and correcting the majority of peteyes that appear in images. Some of these embodiments are able to detect a wide variety of different peteyes using multiple classification maps that segment pixels into peteye pixels and non-peteye pixels. Each of the classification maps is generated based on a different respective segmentation condition on the values of the pixels, where each segmentation condition is selected to increase the contrast between the pixels typically contained in a respective type of peteye area and surrounding non-peteye pixels. In some embodiments, the contrast between peteye pixels and non-peteye pixels is increased by segmenting pixels into a specified animal-fur color class and a non-animal-fur color class. In addition, some of these embodiments apply type-specific peteye color correction processes to the peteye pixels in the detected peteye pixel areas to generate a corrected image. 
   Other embodiments are within the scope of the claims.

Technology Classification (CPC): 6