Patent Publication Number: US-7724952-B2

Title: Object matting using flash and no-flash images

Description:
BACKGROUND 
   The discussion below is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. 
   Image processing or manipulation is growing in popularity. With the increased power and decrease cost of computers, home users would like to manipulate images for scrapbooks and the like. Likewise, professional editing studios have a need to manipulate images for a wide variety of commercial needs such as advertising and marketing. 
   One particular need is to have the ability to separate the foreground object(s) (hereinafter referred to as “foreground object” or “foreground”) from the background object(s) (hereinafter referred to as “background object” or “background”) of an image. In this manner for example, the foreground object can be removed from the original image and transferred to a second image, thus making it appear that the second image was taken with the foreground object extracted from the first image. 
   In order to separate the foreground object from the background, a matte is typically required. The most common approach for obtaining a matte is blue screen matting, in which a foreground object is captured in front of a known solid-colored background, usually blue or green. Blue screen matting is the standard technique employed in the movie and TV industries because a known background greatly simplifies the matting problem. However, blue screen matting requires an expensive well-controlled studio environment to reduce artifacts such as blue spill, backing shadows, and backing impurities. Such a system is not available to the typical consumer. In addition, blue screen matting is less suitable for outdoor scenes. 
   Other techniques include using a single natural image to obtain the matte. First, the input image is manually partitioned into three regions by the user: definitely foreground, definitely background, and unknown regions. These three regions are collectively referred to as the “trimap”. Then, the matte, foreground color, and background color are estimated for all pixels in the unknown region. 
   Although these methods and others have produced results, accurately separating the foreground object from the background object is difficult. This is particularly true when the foreground and the background are alike, or when highly textured foreground or background objects are present in the image, for example, the windblown hair of a person taken against a complex background. 
   SUMMARY 
   The Summary and Abstract are provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. The Summary and Abstract are not intended to identify key features or essential features of the claimed subject matter, nor are they intended to be used as an aid in determining the scope of the claimed subject matter. In addition, the claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background. 
   A method is described that allows a user to separate foreground objects from the background. In particular, the method uses flash/no-flash image pairs of the same scene. In order to separate the foreground objects from the background, a high quality matte that shows the demarcation of the foreground objects from the background objects is needed. Generation of the matte capitalizes on the dominant change in the appearance of the foreground objects, for example, caused by the flash to disambiguate the foreground objects from the background. 
   The matte can be generated using a joint Bayesian flash matting algorithm based on a flash-only image formed from the difference of the flash/no-flash image pair, and one of the images of the flash/no-flash image pair. The resulting high quality matte allows extraction even when the foreground and background are indistinguishable or the background has complex color distributions. 
   The method can be used both on scenes taken indoors as well as outdoors. In addition, the approach is automatable and can be implemented using consumer-level photographic and computing equipment; thus capable of being implemented by users without specialized training or knowledge. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a diagrammatic representation of a system for processing digital images. 
       FIGS. 2(   a )-( f ) are pictorial representations of images. 
       FIG. 3  is a block diagram of modules forming an image processing system. 
       FIG. 4  is a flow chart for processing images. 
       FIG. 5  is a flow chart for obtaining a trimap. 
       FIG. 6  is a block diagram of an exemplary computing environment. 
   

   DETAILED DESCRIPTION 
   A system  10  for capturing and processing images is illustrated in  FIG. 6 . System  10  includes an image capture device  12  and an image processing module  14  capable of receiving images obtained from image capture device  12 . The image capture device  12  includes any device such as, but not limited to, a CCD (charge-coupled device) photosensitive array. The image capture device  12  could be, for instance, an electronic camera or a camcorder. As will be explained below, one of the images is taken with a flash or lighting module  15  to provide additional light beyond ambient conditions. The flash module  15  can emit light in the visible or non-visible spectrum, whereby image capturing module such as the CCD photosensitive array is adapted to generate suitable signals in response to light in the visible and/or non-visible spectrum in order to generate the corresponding image. However, a particularly advantageous embodiment is realized when a flash module is used that emits visible light since the concepts herein described can be practiced using consumer oriented photographic and computer. processing equipment. 
   Image processing module  14  processes images in a manner discussed below so as to extract a selected portion from an image, allowing the user to manipulate the extracted portion, for example, by inserting it into another image. A display  18  can be provided for rendering images during processing or manipulation. 
   Images can be received from image capture device  12  (represented by arrow  16 ) using one or combination of many known technologies such as but not limited to a wired or wireless connection between image capture device  12  and image processing module  14 , or transfer of images using removable storage media. Image processing module  14  can be implemented in hardware and/or software, for example, as an application executable on a suitable computer system, an exemplary embodiment of which is provided below. 
   As is known, a new image I can be obtained from a foreground image F and a background image B elements according to the following compositing equation:
 
 I=αF +(1−α) B,   Eq. 1
 
where α is a matte (also known as “alpha channel”) used to define the foreground from the background. Conversely, the goal of image matting is to estimate α, F, and B from a given image I.
 
   One aspect herein described uses two images taken of the same scene from which the foreground and background can be automatically obtained. In particular, a first image of the scene is obtained with a first level of light intensity (herein also referred to as “no-flash” intensity) upon the scene, commonly, although not exclusively, at an ambient light level. A second image is then obtained at an increased light intensity (herein also referred to as “flash” intensity) such as from flash module  15 . The flash and no-flash images comprise an image pair and can be taken with the same image capture device  12  in near temporal proximity, limited only by the speed of the image capture device  12  and the time necessary to activate the flash module  15 . By taking the two images as close together as possible in time, problems created by the movement of objects in the scene and thus the image can be avoided. More specifically, as long as the movement of the line of demarcation between the exposures is not detectable by the human eye, then the movement of the object is negligible and considered static. Typically, a maximum time differential between exposures of about 1/30th of a second (i.e., the shutter speed of an electronic camera) will ensure negligible movement of the object. However, if the images captured are of high resolution, even very small object movements between snapshots can result in shifts of several pixels. These shifts cause visible artifacts at the boundaries; to remove them, some form of motion compensation is required. Of course, if the object of the image is stationary, then the images can be taken at any time. 
   In a manner discussed below, the foreground and background can be obtained from the image pair. This may entail obtaining a “trimap” having image portions defined as “definitely foreground,” “definitely background,” and possibly “unknown” regions. Image processor  14  generates a high-quality matte  20  that can be used to separate the foreground from the background even when the foreground and the background have similar colors or when the background is complex. The foreground color  21  is also estimated by image processor  14  for the image composition. In addition, no special studio environment is required for taking the images. 
   For a static foreground and a fixed camera, the matte α of the foreground can be assumed unchanged in the no-flash image I and the flash image I f . The compositing or matting equations for I and I f  can be represented as:
 
 I=αF +(1−α) B   Eq. 2
 
 I   f   =αF   f +(1−α) B   f   Eq. 3
 
where {F,B} are the ambient foreground and background colors, and {F f ,B f } are the flash foreground and background colors, respectively. Treating the flash as a point light source with intensity L, the radiance E due to direct illumination from the flash on surface point P in the scene is
 
 E=L ·ρ(ω i ,ω o )· r   −2 ·cos θ,
 
where ρ(ω i ,ω o ) is the surface BRDF (“Bidirectional Reflectance Distribution Function”), ω i  and ω o  are flash and view directions with respect to the local coordinate frame at P, r is the distance from the flash, and θ is the angle between the flash direction and the surface normal at P. This inverse square law explains why the flash intensity falls off quickly with distance r.
 
   When the image capture device  12  and flash module  15  are together and the background scene is distant from the image capture device  12 , the intensity change of the background in flash and no-flash images will be small, B f ≈B, realizing the following equation:
 
 I   f   =αF   f +(1−α) B.  
 
   Subtracting I f  from I yields a difference image I′ (foreground oriented) according to the following equation:
 
 I′=I   f   −I =α( F   f   −F )=α F′,   (4)
 
where F′=(F f −F) is the additional flash foreground color. Herein the difference image I′ is also referred to as the “flash-only” image. The flash-only image I′ is independent of how similar the foreground and background are or the complexity of the background.
 
     FIGS. 2(   a )-( c ) pictorially illustrate images so far discussed.  FIG. 2(   a ) is a pictorial representation of an image  30  of a toy lion  32  comprising the foreground of the image  30  in front of a background of shelves of books  34 . In particular,  FIG. 2(   a ) represents a flash image of the toy lion  32 , where features of the toy lion  32  are illustrated with solid lines, and where the background being less illuminated by the flash, is illustrated with dashed lines.  FIG. 2(   b ) represents a no-flash image  40  of the same scene as  FIG. 2(   a ), but where no flash is provided, hence the features of the toy lion  30  are also illustrated in dashed lines. Finally,  FIG. 2(   c ) represents a flash-only image  50  of the scene. In this image the toy lion  30  is again illustrated with solid lines, while the background is black representing that the background portion has been canceled out by taking the difference of image  30  from image  40  and as a consequence is very distinguishable from the foreground. However, it should be noted that the actual colors of the foreground of the flash-only image  50  typically do not correspond to the colors found in either image  30  or image  40 . 
     FIG. 3  illustrates modules of image processing module  14  to generate matte  20  and foreground color  21 , while  FIG. 4  illustrates a method  100  for generating matte  20  and foreground color  21 . In particular, at step  102 , image difference module  60  receives the flash image  30  and the no-flash image  40  and generates flash-only image  50 . Step  102  can include image or pixel alignment, if necessary. 
   Although the flash-only image  50  may appear to be sufficient by itself to generate matte  20 , the foreground matting problem is still under-constrained. In order to solve it, a trimap is helpful. Step  104  in  FIG. 4  represents obtaining a trimap. While some matting techniques typically assume a user-supplied trimap, method  200  illustrated in  FIG. 5  can produce a good initial trimap to substantially reduce user interaction. At step  202 , in a first pass, a global high threshold T is used to detect a foreground region Ω F   1  with high confidence of the flash-only image I′. The threshold T is set as the first local minimum of a histogram (128 bins) of the intensity of the flash-only image I′. The histogram can be smoothed using a Gaussian kernel (e.g., with a variance of 7) to reduce noise. In a second pass illustrated at step  204 , a lower threshold (e.g., 0.6 T) is used to detect foreground regions Ω F   2  with lower confidence. Ω F   2  is typically a set of connected components. At step  206 , components in Q F   2  that overlap with Ω F   1  are kept and all other isolated components in Ω F   2  are removed. The second pass of step  204  can effectively connect missed foreground regions from the first pass of step  202 . At step  208 , the trimap is computed by dilating or expanding the boundary of Ω F   1 ∪Ω F   2 . It should be noted the range of dilation can vary depending on the object under consideration in the image. For example, objects with fur, the range of dilation can be 5-20 pixels; while objects with solid boundaries, the range of dilation can be 2 pixels. In some cases, the user may need to interactively adjust only the threshold T and dilation width to produce a reasonable-looking trimap. For very complex image cases, the trimap can be rendered on display  18 , where for example, areas considered background are represented in black, foreground in white and unknown regions in grey. Using a suitable interface such as a paint-style interface the user can optionally touch up the trimap by changing the classification of one or more areas on the trimap at step  210 . In  FIG. 3 , steps  202 ,  204 ,  206 ,  208  and optionally step  210  are performed by trimap generation module  64  to generate a trimap  66 . 
   It should also be noted at times, the flash image is too bright and the no-flash image too dark for image composition. The user can either adjust the brightness or color of the recovered foreground color for a better composition by using any image editing software, or apply continuous flash adjustment, such as described by G. Petschnigg et al. in “Digital photography with flash and no-flash image pairs”, published in Proceedings of ACM SIGGRAAPH 2004, 664-672, to interpolate estimated foreground colors from flash/no-flash images. 
   Referring back to the matte generation problem, a straightforward approach to solving the foreground flash matting problem is to apply the Bayesian matting algorithm in two separate steps:
         1) estimate α and F′ from equation (4); and   2) using the recovered α, estimate F and B from Equation (2) or F f  and B f  from equation (3).       

   In theory, this two-step approach yields a good matte from equation (4) no matter how similar the foreground and background are and how complex the background. In practice, however, the foreground flash matting equation (4) may be poorly conditioned when ∥F′∥ is nearly zero. This is possible if a dark flash-only foreground color F′ is obtained for instance, when the foreground has low reflectivity (ρ(ω i ,ω o )≈0), or if the surface normal is nearly perpendicular to the flash direction (θ≈90°). Another problem is pixel saturation, e.g., a highlight or white pixel may change very little between two images because it is saturated or nearly saturated. While such ill-conditioned pixels may constitute a small fraction of all unknown pixels, human eyes are sensitive to incorrect local discontinuities. 
   It has been found that these ill-conditioned pixels may be well-conditioned in the no-flash image I or flash image I f , and in a further embodiment, an improved matte  20  can be obtained by using a joint Bayesian flash matting algorithm that uses information from both images. 
   Joint Bayesian Processing 
   By way of example, joint Bayesian processing, which is represented in method  100  at step  106  performed by generation module  68 , will be described using the no-flash image I in Equation (2) (although the flash image could also be used) and the flash-only image I′ in Equation (4). At step  108 , for each unknown pixel in the trimap, a log likelihood function L(α,F,B,F′|I,I′) of its unknown variables {α,F,B,F′} is maximized, given the observation {I,I′}: 
                     arg   ⁢       max     α   ,   F   ,   B   ,     F   ′         ⁢     L   ⁡     (     α   ,   F   ,   B   ,       F   ′     ❘   I     ,     I   ′       )           =     arg   ⁢       max     α   ,   F   ,   B   ,     F   ′         ⁢     {       L   ⁢     (       I   ❘   α     ,   F   ,   B     )       +     (         I   ′     ❘   α     ,     F   ′       )     +     L   ⁢     (   F   )       +     L   ⁡     (   B   )       +     L   ⁡     (     F   ′     )       +     L   ⁡     (   α   )         }           ,           Eq   .           ⁢     (   5   )                 
where L(·) is the log of probability P(·). The term L(I,I′) is ignored because it is constant, and the log likelihood for matte L(α) is assumed to be constant since no appropriate prior exists for a complicated matte distribution.
 
   The first two log likelihoods on the right hand side of Equation (5) measure the fitness of solved variables {α,F,B,F′} with respect to matting Equations (2) and (4):
 
 L ( I|α,F,B )=−∥ I−αF −(1−α) B∥/σ   I   2 ,
 
 L ( I′|α,F ′)=−∥ I′−αF ′∥/σ   I′   2 ,
 
where σ I   2  and σ I′   2  are noise variances of images I and I′ respectively. By default and in one embodiment, these two variances are set to be the same, e.g., σ I   2 =σ I′   2 =32.
 
   The statistics of foreground colors are represented as an oriented Gaussian distribution. The log likelihood L(F) is modeled as
 
 L ( F )=−( F−  F   ) T Σ F   −1 ( F−  F   ),  Eq. (6)
 
where {  F , Σ F   −1 } are the mean and covariance matrix of the estimated Gaussian distribution, respectively. The background term L(B) is defined in a similar way with {  B ,Σ B   −1 }.
 
   For the foreground color F′, an estimate is obtained for the oriented Gaussian distribution {  F′ , Σ F′   −1 } in the flash-only image I′. Thus,
 
 L ( F ′)=−( F′−  F′   ) T Σ F′   −1 ( F′−  F′   ).  Eq. (7)
 
Taking the partial derivatives of (5) with respect to α and {F,B,F′} and equating them to zero results in
 
                   α   =               σ     I   ′     2     ⁡     (     F   -   B     )       T     ⁢     (     I   -   B     )       +       σ   I   2     ⁢     F   ′T     ⁢     I   ′                   σ     I   ′     2     ⁡     (     F   -   B     )       T     ⁢     (     F   -   B     )       +       σ   I   2     ⁢     F   ′T     ⁢     F   ′             ⁢     
     ⁢   and           Eq   .           ⁢     (   8   )                       [             ∑   F     -   1       ⁢           ⁢       +   I     ⁢           ⁢     α   2     ⁢     /     ⁢     σ   I   2               I   ⁢           ⁢     α   ⁡     (     1   -   α     )       ⁢     σ   I   2           0             I   ⁢           ⁢     α   ⁡     (     1   -   α     )       ⁢     σ   I   2               ∑   B     -   1       ⁢           ⁢       +   I     ⁢           ⁢     α   2     ⁢     /     ⁢     σ   I   2             0           0       0           ∑     F   ′       -   1       ⁢           ⁢       +   I     ⁢           ⁢     α   2     ⁢     /     ⁢     σ     I   ′     2               ]     ⁡     [         F           B             F   ′           ]       ⁢     
     =     [               ∑   F     -   1       ⁢           ⁢     F   _       +     I   ⁢           ⁢   α   ⁢     /     ⁢     σ   I   2                       ∑   B     -   1       ⁢           ⁢     B   _       +       I   ⁡     (     1   -   α     )       ⁢     /     ⁢     σ   I   2                       ∑     F   ′       -   1       ⁢           ⁢       F   ′     _       +       I   ′     ⁢   α   ⁢     /     ⁢     σ     I   ′     2               ]       ,           Eq   .           ⁢     (   9   )                 
where I is the 3×3 identity matrix and 0 the 3×3 zero matrix, which is illustrated at step  110 , and comprises one embodiment for determining the maximum arguments. For example, to maximize (5), estimations are made iteratively of α and {F,B,F′} using equations (8) and (9) until changes between two successive iterations are negligible (below a selected threshold). At the beginning of optimization, {F,B,F′} are set to {  F ,  B ,  F′ }.
 
   Note that equation (8) is not a simple linear interpolation. It can adaptively select a well-conditioned matting equation from equation (2) or equation (4) to estimate the matte α. If equation (2) is ill-conditioned (e.g., F≈B), the estimate will be dominated by equation (4), i.e., α≈F′ T I′/F′ T F′. Alternatively, if equation (4) is ill-conditioned (e.g., F′≈0), will be automatically estimated by equation (2), i.e., α≈(F−B) T (I−B)/(F−B) T (F−B). Thus, the underlying mechanism for this method selectively combines information from two images, robustly producing high-quality matting results. It should be noted that the matting results obtained by this procedure cannot be obtained by just combining two single-image matting results. However, it should be noted that a complex background may result in biased statistics to the level that equation (2) should not be relied upon fully. In these cases, σ I   2  can be adjusted, where higher values of σ I   2  will result in less reliance on the no-flash image. 
     FIG. 2(   d ) is a pictorial representation of a matte for the toy lion  32 , where portions of the matte representing the toy lion  32  are white and the background is black. Although there is the assumption that only the appearance of foreground is dramatically changed by the flash, in practice, it has been found joint Bayesian flash matting is robust enough to handle small appearance changes in the background caused by flash.  FIG. 2(   e ) is an enlarged portion of the matte of  FIG. 2(   d ) where it can be seen that the matte includes the fine fur edges of the toy lion  32  with minimal or any artifacts. 
   When the image processing module  14  further includes optional image combining module  70 , new images can be generated as illustrated at step  112 . In particular image combining module  70  uses the generated matte  20  and foreground color  21  to separate the foreground from the background, wherein the selected portion can then be applied or incorporated into to other images. In the example illustrated, a new background image  65  is used with the foreground image from the no-flash image  40  to form a new image  75 . For example,  FIG. 2(   f ) illustrates incorporation of the toy lion  32  into new image  75 . 
   One exemplary method for obtaining images is provided below. The image capture device  12  (herein a digital camera) is set up to take the no-flash image, i.e., the focal length, the shutter speed and aperture are set at appropriate values to take a no-flash image of the foreground. The flash module  15  is then activated with a subsequent flash image taken. To obtain a high-quality matte, mounting the camera on a tripod is beneficial to obtain pixel-aligned images. Dynamic scenes can be taken depending on the speed in which flash/no-flash pair images can be acquired. 
   The images can be acquired in raw format and then converted into another format such as an 8-bit linear TIFF format. Disabling white balancing, gamma correction, and other non-linear operations in the raw conversion utility is beneficial so that the two images are converted identically. In one embodiment, a Canon EOS-20D digital camera and a Canon Speedlite 580EX external flash are suitable devices for the image capture device  12  and flash module  15 , respectively. 
   To maximize change in the foreground between the flash and no-flash images, it may be beneficial to set the camera exposure compensation to −1 stop so the no-flash image is slightly under-exposed. Before capturing the flash image, the flash exposure compensation is set to +1 stop and the camera&#39;s though-the-lens light metering is allowed to determine the best flash intensity. 
   Current digital cameras are capable of continuous capture, typically between three and five frames per second. The capturing process above can be adapted to take advantage of this feature. When capturing fine details of a live subject, such as human hair in an outdoor scene, the flash is first activated. Two images are then taken using the camera&#39;s “continuous mode.” The first captured image is the flash image. The second one will be the no-flash image because the flash appears only instantaneously for the first image, and will be in the process of recharging. 
   In flash photography, shadows are caused by two factors: depth discontinuities within the foreground object (i.e., portions of the foreground object can cause shadows on other portions of the object), or significant displacement between the flash unit and camera&#39;s optical center. Shadow pixels will erroneously result in F′≈0, thus degrading the matting results. If the foreground objects do not contain large internal depth discontinuities, the errors caused by shadow pixels are small and can be reduced by joint Bayesian matting method above. However, for a foreground object with large internal depth discontinuities, one solution is to use a ring-flash (e.g., a Canon Macro Ring Lite MR-14EX ring-flash) to produce a relatively shadow-free flash image. 
   If a flash mechanism such as the ring discussed above is unavailable or does not solve the shadowing problem, an alternative technique is to use a normal external flash module and capture multiple flash images {I 1   f , . . . , I N   f } by evenly varying the positions of the flash around the image capture device&#39;s center. In one embodiment, four images are sufficient (one each at the left, right, top, and bottom positions). A practically shadow-free flash image Ī f  is created using a pixel-wise maximum operation:
 
Ī f =max{I 1   f , . . . , I N   f }.
 
The flash image Ī f  is then used in the method discussed above.
 
   It should be noted in scenes where the background and foreground are relatively close, the background change in the flash image may be too significant to be ignored. In such cases, the flash module  15  can be positioned such that it illuminates the background and not the foreground, in order to alter the appearance of the background instead between the flash and no-flash images. Assuming the foreground is virtually unaffected by the flash, the following matting equations are relevant: 
           {             I   =       α   ⁢           ⁢   F     +       (     1   -   α     )     ⁢   B                     I   f     =       α   ⁢           ⁢   F     +       (     1   -   α     )     ⁢     B   f                 ,           
where it is assumed there is no change on the foreground color, yielding F f ≈F . Similar to equation (4), a background oriented matting equation is represented as:
   I′=I   f   −I =(1−α)( B   f   −B )=(1−α) B′,    
   where B′=(B f −B) is the additional flash background color. In a manner similar to that discussed above joint Bayesian flash matting maximizes the likelihood L(α,F,B,B′|I,I′). 
   In some situations, a more specialized flash unit is useful for non-static objects such as hair that may move around in outdoor scenes. Current flash units, such as the Canon EX series Speedlite flashes, usually support a high-speed sync mode that allows the use of a higher shutter speed (e.g., 1/2000 sec) than camera&#39;s X-sync speed. (X-sync speed is the camera&#39;s top shutter speed that can be used with any flash. Typically, it varies from 1/60 sec to 1/250 sec). 
   The capture process is as follows. The no-flash image is captured under normal conditions to provide the correct colors, while the flash image should be taken under very short exposure to ensure that the flash affects mostly the foreground object. The order the images are taken is optional. In this case, equations for the no-flash image I and flash image I f  are as follows: 
           {             I   =       α   ⁢           ⁢   F     +       (     1   -   α     )     ⁢   B                     I   f     =     α   ⁢           ⁢     F   f               ,           
where it is assumed that the background color B f ≈0 in the “quick-flash” image I f . Joint Bayesian processing is used to maximize the likelihood L(α,F,B,F f |I,I f ). However, this process is not always applicable because it requires a relatively dark ambient illumination and a flash with high-speed sync mode.
 
   In another approach, a two-camera system with a common virtual camera center and a beam-splitter can be used. This system could also be electronically set up to capture flash/no-flash images in rapid succession. One camera is triggered slightly later than the other, with the delay being only the flash duration (typically 1 msec). The flash is activated so that only the first triggered camera records its effect. Another possibility would be to use a single programmable imaging camera with the exposure time of each pixel being independently controlled. 
   Exemplary Computing Environment 
     FIG. 6  illustrates an example of a suitable computing system environment  300  on which the concepts herein described may be implemented. The computing system environment  300  is again only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the description below. Neither should the computing environment  300  be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment  300 . 
   In addition to the examples herein provided, other well known computing systems, environments, and/or configurations may be suitable for use with concepts herein described. Such systems include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. 
   The concepts herein described may be embodied in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Those skilled in the art can implement the description and/or figures herein as computer-executable instructions, which can be embodied on any form of computer readable media discussed below. 
   The concepts herein described may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both locale and remote computer storage media including memory storage devices. 
   With reference to  FIG. 6 , an exemplary system includes a general purpose computing device in the form of a computer  310 . Components of computer  310  may include, but are not limited to, a processing unit  320 , a system memory  330 , and a system bus  321  that couples various system components including the system memory to the processing unit  320 . The system bus  321  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a locale bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) locale bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus. 
   Computer  310  typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer  310  and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer  300 . 
   The system memory  330  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  331  and random access memory (RAM)  332 . A basic input/output system  333  (BIOS), containing the basic routines that help to transfer information between elements within computer  310 , such as during start-up, is typically stored in ROM  331 . RAM  332  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  320 . By way o example, and not limitation,  FIG. 6  illustrates operating system  334 , application programs  335 , other program modules  336 , and program data  337 . 
   The computer  310  may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,  FIG. 6  illustrates a hard disk drive  341  that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive  351  that reads from or writes to a removable, nonvolatile magnetic disk  352 , and an optical disk drive  355  that reads from or writes to a removable, nonvolatile optical disk  356  such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive  341  is typically connected to the system bus  321  through a non-removable memory interface such as interface  340 , and magnetic disk drive  351  and optical disk drive  355  are typically connected to the system bus  321  by a removable memory interface, such as interface  350 . 
   The drives and their associated computer storage media discussed above and illustrated in  FIG. 6 , provide storage of computer readable instructions, data structures, program modules and other data for the computer  310 . In  FIG. 6 , for example, hard disk drive  341  is illustrated as storing operating system  344 , application programs  345 , other program modules  346 , and program data  347 . Note that these components can either be the same as or different from operating system  334 , application programs  335 , other program modules  336 , and program data  337 . Operating system  344 , application programs  345 , other program modules  346 , and program data  347  are given different numbers here to illustrate that, at a minimum, they are different copies. 
   A user may enter commands and information into the computer  310  through input devices such as a keyboard  362 , a microphone  363 , and a pointing device  361 , such as a mouse, trackball or touch pad. Other input devices (not shown) may include a scanner or the like. These and other input devices are often connected to the processing unit  320  through a user input interface  360  that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port or a universal serial bus (USB). A monitor  391  or other type of display device is also connected to the system bus  321  via an interface, such as a video interface  390 . 
   The computer  310  may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer  380 . The remote computer  380  may be a personal computer, a hand-held device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  310 . The logical connections depicted in  FIG. 6  include a locale area network (LAN)  371  and a wide area network (WAN)  373 , but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
   When used in a LAN networking environment, the computer  310  is connected to the LAN  371  through a network interface or adapter  370 . When used in a WAN networking environment, the computer  310  typically includes a modem  372  or other means for establishing communications over the WAN  373 , such as the Internet. The modem  372 , which may be internal or external, may be connected to the system bus  321  via the user-input interface  360 , or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer  310 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG. 6  illustrates remote application programs  385  as residing on remote computer  380 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
   It should be noted that the concepts herein described can be carried out on a computer system such as that described with respect to  FIG. 6 . However, other suitable systems include a server, a computer devoted to message handling, or on a distributed system in which different portions of the concepts are carried out on different parts of the distributed computing system. 
   Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not limited to the specific features or acts described above as has been held by the courts. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.