PATENT DOCUMENT

Publication Number: US-8331685-B2
Application Number: US-15498708-A
Country: US
Kind Code: B2

Title: Defining a border for an image

Abstract:
Some embodiments provide a method that provides a display area for displaying an image that includes several of edges. The method provides a border drawing tool that in response to cursor movement across the image displays a search window about the cursor. The search window specifies a region to be searched to identify edges for use in defining a border for the image. In some embodiments, the size of the search window varies based on the speed of the cursor. The search window is a square box in some embodiments and a circle in other embodiments. The search window is centered at the cursor in some embodiments. In some embodiments, the display area is also for displaying the defined border over the image.

Claims:
1. A method comprising:
 providing a display area for displaying an image that comprises a plurality of edges; and 
 providing a border drawing tool that in response to movement of a location indicator across the image displays a search window about the location indicator, the search window specifying a region to search for edges in the image along which to define a border for display over the image, wherein a size of the search window varies based on a speed of the location indicator, 
 wherein when the border drawing tool identifies a plurality of edges within the search window for a particular location, the tool defines the border along an edge closest to the center of the search window. 
 
     
     
       2. The method of  claim 1 , wherein the size of the search window is proportional to the speed of the location indicator within a range of speeds. 
     
     
       3. The method of  claim 2 , wherein the search window has a minimum size and a maximum size. 
     
     
       4. The method of  claim 1 , wherein the search window is a square box. 
     
     
       5. The method of  claim 1 , wherein the search window is a circle. 
     
     
       6. The method of  claim 1 , wherein the display area is also for displaying the defined border over the image. 
     
     
       7. The method of  claim 1 , wherein the search window is centered at the location indicator. 
     
     
       8. A non-transitory computer readable medium storing a computer program which when executed by at least one processing unit defines a border for an image, the computer program comprising sets of instructions for:
 receiving a movement of a location indicator over the image; 
 searching for edges in the image within a region defined about the location indicator, wherein a size of the region defined about the location indicator varies based on a speed of the location indicator; 
 when edges are identified within the region, defining the border along an identified edge closest to the location indicator; and 
 when no edges are identified within the region, defining the border along the movement of the location indicator. 
 
     
     
       9. The non-transitory computer readable medium of  claim 8 , wherein the region has a property that changes based on the size of the region. 
     
     
       10. The non-transitory computer readable medium of  claim 8 , wherein the size of the region increases when the location indicator moves faster. 
     
     
       11. The non-transitory computer readable medium of  claim 8 , wherein the size of the region decreases when the location indicator slows down. 
     
     
       12. The non-transitory computer readable medium of  claim 8 , wherein the region is a square region centered about the location indicator. 
     
     
       13. The non-transitory computer readable medium of  claim 8 , wherein the computer program further comprises a set of instructions for performing an edge detection operation to identify relevant edges of the image when the image is opened by the computer program. 
     
     
       14. The non-transitory computer readable medium of  claim 8 , wherein the computer program further comprises a set of instructions for applying an image processing operation to the image to modify content of the image on one side of the defined border, wherein the image processing operation is a color correction operation that modifies pixel data of the image. 
     
     
       15. The method of  claim 1 , wherein the border is defined based on reference points defined over edges within the search window. 
     
     
       16. A method for defining a border for an image, the method comprising:
 receiving a movement of a location indicator over the image; 
 searching for edges in the image within a region defined about the location indicator, wherein a size of the region defined about the location indicator varies based on a speed of the location indicator; 
 when edges are identified within the region, defining the border along an identified edge closest to the location indicator; and 
 when no edges are identified within the region, defining the border along the movement of the location indicator. 
 
     
     
       17. The method of  claim 16 , wherein the region has a property that changes based on the size of the region. 
     
     
       18. The method of  claim 16 , wherein the size of the region increases when the location indicator moves faster. 
     
     
       19. The method of  claim 16 , wherein the size of the region decreases when the location indicator slows down. 
     
     
       20. The method of  claim 16 , wherein the region is a square region centered about the location indicator. 
     
     
       21. The method of  claim 16  further comprising performing an edge detection operation to identify relevant edges of the image when the image is opened. 
     
     
       22. The method of  claim 16  further comprising applying an image processing operation to the image to modify content of the image on one side of the defined border, wherein the image processing operation is a color correction operation that modifies pixel data of the image.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This Application is related to the following applications: U.S. patent application Ser. No. 12/154,989, filed May 28, 2008, now published as U.S. Publication No. 2009/0297031; U.S. patent application Ser. No. 12/154,990, filed May 28, 2008, now issued as U.S. Pat. No. 8,280,171; and U.S. patent application Ser. No. 12/154,991, filed May 28, 2008, now published as U.S. Publication No. 2009/0297035. 
     FIELD OF THE INVENTION 
     The invention is directed towards image editing. Specifically, the invention is directed towards defining a border for an image. 
     BACKGROUND OF THE INVENTION 
     Image editing applications (as well as video and other media editing applications) provide users with the ability to modify digital images from their original state. Often a user will want to modify the color properties of an entire image, or more commonly, a selection of an image. For example, a user might want to increase the saturation in a selection to make the color more intense and thereby cause that selection stand out more in the image. Other color properties a user might want to change include hue, luminosity, etc. Modification of the color properties of a selection will be referred to as color correction. 
     In order to modify a selection of an image, a user must first be provided with a tool for defining the selection they want to modify. Some prior art selection tools base the selection on a color selected by a user. A user can specify (by selecting from a color palette or by clicking on a point in an image) what color they want to select, and the selection tool will define a selection as all pixels in the image within a threshold of the selected color. However, in some cases a user will only want to select some of the pixels of the selected color (e.g., if there are multiple faces in an image and a user wants to highlight one of the faces). Further, sometimes a desired selection will include multiple colors (e.g., a head with skin, hair, eyes, etc.). 
     Other prior art selection tools allow a user to draw a border around the area of the image the user wants to select for color correction. However, doing so is often a very difficult process as the border of the selection is defined by the exact movement of the cursor. This requires a user to move very slowly and carefully through the image. Therefore, there is a need for a selection tool that allows a user to move more quickly through the image yet still defines a border in the image in the appropriate location. 
     A further shortcoming of such prior art selection tools is the inability to correct a mistake. A user of such selection tools must be able to start at the beginning of a desired border and move a cursor all the way to the desired endpoint without making a mistake. If a mistake is made, the user must start the selection process over. This can be a very frustrating process for the user, especially if the border the user attempts to draw is long, and the user has to make multiple attempts to draw the border. Therefore, there is a need for a selection tool that allows a user to correct mistakes when attempting to define a border in an image. 
     A third shortcoming of the above prior art selection tools is that they define a border that does not allow for a natural transition from foreground to background. Some tools do not create a hard edge between a selection and the rest of the image, but apply a simple softening of the edge of a selection. However, these tools do not create the softening effect based on an intelligent algorithm that accounts for the actual nature of the border. When attempting to select an area such as a head with hair, it is nearly impossible to trace out every hair, but the ability to keep the hairs in the foreground is a useful feature. Furthermore, even at borders that are easier to select, an intelligent transition from the foreground to background that is specific to the border may be desirable. Therefore, there is a need for a user to be able to define an area as a transition section, and to determine the size and shape of the transition section. In addition, there is a need to be able to define an intelligent transition from foreground to background for a selection. 
     SUMMARY OF THE INVENTION 
     Some embodiments of the invention provide a method that defines a border as a cursor scrolls over an image. In some embodiments, an image-editing application identifies the edges of an image as the application loads the image for display and editing. Some embodiments apply a de-noise algorithm to the image before identifying the edges, such that only the most relevant edges of the image are maintained. 
     Some embodiments use the edges to define a border for the image. For instance, in some embodiments, the method (1) identifies edges in the vicinity of a cursor that is moved across the image, and (2) snaps the defined border to the identified edges in the vicinity. In some of these embodiments, the method displays a search window in which it searches for edges near the cursor. Some embodiments vary the size of the search window based on the speed of the cursor moving over the image. The faster the cursor movement, the larger the search window in some embodiments. 
     Some embodiments draw the defined border as a series of segments with points in between the segments. Some embodiments also receive input to delete a part of the defined border while the border is being defined. In some embodiments, the input to delete a part of the border is a cursor movement back over the previously defined border past at least one of the points on the border. 
     Some embodiments use the defined border to perform image-editing operations, such as color correction of a portion of an image or cutting out (i.e., cropping) a portion of an image, as well as other image-editing operations (e.g. adding textures and other effects, etc.). To assist in such operations, some embodiments generate a tunnel about the defined border. In some embodiments, the tunnel has a constant set width, with either side of the tunnel an equal distance from the border. In other embodiments, the tunnel&#39;s width is varied to avoid self-intersection. The method receives modifications to the tunnel after the tunnel is generated, in some embodiments. The modifications of some embodiments include changes to the width of the tunnel and modifications to the shape of one or both sides of the tunnel. 
     As mentioned above, some embodiments use the tunnel to perform image-editing operations. For instance, some embodiments use the tunnel to generate a foreground to background transition in an image. To implement this transition, some embodiments sample pixels on the exterior of the tunnel, and determine an alpha value for pixels inside the tunnel based on the sample pixels. The alpha values are determined based on an algorithm that compares image values of the sampled pixels to the image values of the pixels on the interior of the tunnel. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The novel features of the invention are set forth in the appended claims. However, for purpose of explanation, several embodiments of the invention are set forth in the following figures. 
         FIG. 1  conceptually illustrates an overall process performed by some embodiments of the invention. 
         FIG. 2  illustrates an image with a foreground, background, and transition section. 
         FIG. 3  illustrates identified edges of the image from  FIG. 2 . 
         FIG. 4  illustrates a border defined for the image from  FIG. 2 . 
         FIG. 5  illustrates a tunnel generated around the border from  FIG. 4   
         FIG. 6  illustrates a foreground to background transition generated within the tunnel of  FIG. 5 . 
         FIG. 7  illustrates an image-editing application of some embodiments. 
         FIG. 8  illustrates a process that pre-computes edges of an image in accordance with some embodiments. 
         FIG. 9  illustrates the selection of an image file to open. 
         FIG. 10  illustrates the opened image. 
         FIG. 11  illustrates a process of some embodiments that applies a de-noise algorithm to an image such that only the most relevant edges are identified. 
         FIG. 12  illustrates the edges of an image without a de-noise algorithm applied. 
         FIG. 13  illustrates the edges of the image with the de-noise algorithm applied. 
         FIG. 14  illustrates a process of some embodiments for defining a border of an image. 
         FIGS. 15 and 16  illustrate different size search windows of some embodiments for defining a border. 
         FIGS. 17-19  illustrate the edge searching process of some embodiments 
         FIG. 20  illustrates a defined border for an image that has a section snapped to edges of the image and a section not snapped to edges. 
         FIG. 21  illustrates a process of some embodiments that allows part of a defined border to be deleted during the definition process. 
         FIGS. 22-25  illustrate the definition of a border using the process of  FIG. 21 . 
         FIGS. 26-28  illustrate the modification of a defined border of an image. 
         FIG. 29  illustrates a process of some embodiments for generating a tunnel from a defined border. 
         FIG. 30  illustrates a defined border. 
         FIG. 31  illustrates a tunnel generated from the border of  FIG. 30 . 
         FIG. 32  illustrates a defined border. 
         FIG. 33  illustrates a tunnel generated from the border of  FIG. 32 . 
         FIG. 34  illustrates a modification to the width of the tunnel of  FIG. 31 . 
         FIG. 35  illustrates a modification to the width of the tunnel of  FIG. 33 . 
         FIG. 36  illustrates a tunnel. 
         FIG. 37  illustrates the tunnel of  FIG. 36  with several control points for modifying the tunnel. 
         FIGS. 38 and 39  illustrate the use of the control points to modify the tunnel of  FIG. 36 . 
         FIG. 40  illustrates a process of some embodiments for selecting a section of interest within an image. 
         FIGS. 41-44  illustrate the selection of a section of interest within an image. 
         FIG. 45  illustrates a process of some embodiments for generating alpha values for pixels in an image. 
         FIG. 46  illustrates a tunnel on a section of an image. 
         FIG. 47  illustrates foreground and background sample pixels around the tunnel of  FIG. 46 . 
         FIGS. 48 and 49  illustrate tunnels with different sample widths. 
         FIG. 50  illustrates calculated alpha values overlaid on the section of the image from  FIG. 46 . 
         FIG. 51  illustrates the alpha values of  FIG. 50 . 
         FIG. 52  illustrates an image-editing application of some embodiments with an alpha brush tool selected. 
         FIG. 53  illustrates a first alpha brush for adding alpha with a first radius. 
         FIG. 54  illustrates a second alpha brush for adding alpha with a second radius. 
         FIG. 55  illustrates a tool for modifying the softness of an alpha brush. 
         FIG. 56  illustrates an alpha brush for removing alpha. 
         FIG. 57  illustrates the use of the alpha brush from  FIG. 56 . 
         FIGS. 58-63  illustrate the selection, in multiple pieces, of a baby&#39;s head in an image. 
         FIG. 64  illustrates an image and color correction tools. 
         FIG. 65  illustrates the use of the color correction tools to modify the image. 
         FIG. 66  conceptually illustrates the software architecture of an image-editing application of some embodiments. 
         FIG. 67  conceptually illustrates a computer system with which some embodiments of the invention are implemented. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In the following description, numerous details are set forth for purpose of explanation. However, one of ordinary skill in the art will realize that the invention may be practiced without the use of these specific details. 
     I. Overview 
     Some embodiments of the invention provide a method that defines a border as a cursor scrolls over an image. In some embodiments, an image-editing application identifies the edges of an image as the application loads the image for display and editing. Some embodiments apply a de-noise algorithm to the image before identifying the edges, such that only the most relevant edges of the image are maintained. 
     Some embodiments use the edges to define a border for the image. For instance, in some embodiments, the method (1) identifies edges in the vicinity of a cursor that is moved across the image, and (2) snaps the defined border to the identified edges in the vicinity. In some of these embodiments, the method displays a search window in which it searches for edges near the cursor. Some embodiments vary the size of the search window based on the speed of the cursor moving over the image. The faster the cursor movement, the larger the search window in some embodiments. 
     Some embodiments draw the defined border as a series of segments with points in between the segments. Some embodiments also receive input to delete a part of the defined border while the border is being defined. In some embodiments, the input to delete a part of the border is a cursor movement back over the previously defined border past at least one of the points on the border. 
     Some embodiments use the defined border to perform image-editing operations, such as color correction of a portion of an image or cutting out a portion of an image. To assist in such operations, some embodiments generate a tunnel about the defined border. In some embodiments, the tunnel has a constant set width, with either side of the tunnel an equal distance from the border. In other embodiments, the tunnel&#39;s width is varied to avoid self-intersection. The method receives modifications to the tunnel after the tunnel is generated, in some embodiments. The modifications of some embodiments include changes to the width of the tunnel and modifications to the shape of one or both sides of the tunnel. 
     As mentioned above, some embodiments use the tunnel to perform image-editing operations. For instance, some embodiments use the tunnel to generate a foreground to background transition in an image. To implement this transition, some embodiments sample pixels on the exterior of the tunnel, and determine an alpha value for pixels inside the tunnel based on the sample pixels. Some embodiments determine the alpha value based on an algorithm that compares image values of the sampled pixels to the image values of the pixels on the interior of the tunnel. 
       FIG. 1  illustrates the overall process  100  performed by some embodiments of the invention.  FIGS. 2-6  illustrate an example of the application of process  100  to an image  200 . Image  200  includes foreground  205  (a face and neck), background  210 , and transition section  215  (the hair area, which is mixed background and foreground). Process  100  starts at  105  by identifying edges in an image. Some embodiments apply a de-noise algorithm to the image before identifying the edges so as to identify only the most relevant edges.  FIG. 3  illustrates the edges  305  of image  200  identified at  105 . Some embodiments identify more or fewer edges; for example, some might identify one or more small edges in the hair area. 
     After identifying the edges, the process uses the edges to define (at  110 ) a border of the image  200  as a cursor traverses over an image.  FIG. 4  illustrates image  200  with defined border  405 . The border  405  is defined along some of the edges  305 , and includes segments  410  with points  415 . Note that the border does not necessarily need to be selected along an entire continuous edge or be completely enclosing an object. 
     From the border defined at  110 , the process generates (at  115 ) a tunnel based on the defined border.  FIG. 5  illustrates tunnel  505  on image  200 . The tunnel  505  is generated such that both sides follow the curve of defined border  405 . The tunnel  505  encloses the transition area  215 . Some embodiments generate a tunnel such that one or both sides do not exactly follow the curve of the selected border. Some embodiments allow the tunnel to be modified after it is generated. The tunnel can be modified in some embodiments by changing the width of the tunnel (i.e., the distance between the two sides) or by altering the shape of one or both sides of the tunnel. 
     Based on the tunnel generated at  115 , the process  100  generates a foreground to background transition. Some embodiments generate the foreground to background transition inside the tunnel based on the pixels outside the tunnel. Some embodiments define an alpha value for each pixel. The alpha values of some embodiments represent the extent to which a pixel is in the foreground.  FIG. 6  illustrates alpha values for the pixels in image  200 . The darker the pixel, the lower the alpha value. In  FIG. 6 , alpha values have been generated for the entire image in addition to the transition area inside the tunnel. Some embodiments use other methods of generating alpha values in some areas to complement the generation of alpha values from tunnels.  FIG. 6  illustrates foreground area  205  which is entirely white, background area  210 , which is entirely black, and transition area  215 , which includes a gradation from white to black. The lightness of a particular pixel indicates the extent to which the particular pixel is in the foreground. Part of the area that was within the tunnel in  FIG. 5  is entirely white because the pixels are similar in nature to those in the rest of the foreground. After generating the foreground to background transition, the process  100  ends. 
     Several more detailed embodiments of the invention are described in the sections below. Section II describes an image-editing application of some embodiments. Section III describes several embodiments for performing edge detection of an image. Next, Section IV describes several detailed embodiments for defining a border for an image. Section V describes several embodiments by which a tunnel is generated from a border. Section VI describes several embodiments for generating a foreground to background transition within an image. Next, Section VII describes some embodiments that perform color correction on an image. Finally, Section VIII describes a computer system with which some embodiments of the invention are implemented. 
     II. Image-Editing Application 
       FIG. 7  illustrates an image-editing application  700  in accordance with some embodiments of the invention. The image-editing application shown in  FIG. 7  provides (1) a main display window  705  to display an image, (2) a set of edge detection tools  710 , (3) a set of border definition tools  715 , (4) a set of tunnel and alpha generation tools  720 , and (5) a set of color correction tools  725 . Some embodiments provide more image-editing tools than those shown, while others provide only a subset of the tools shown in  FIG. 7 . 
     The main display window  705  displays an image that can be edited using the image-editing tools  710 - 725 . The set of edge detection tools  710  provides users with options for identifying edges in the image. The identified edges can be used for the definition of a border for the image. Some embodiments of the invention identify edges of an image as the image is loading and store the edges in memory for use in the subsequent definition of borders. The set of edge detection tools  710  of some embodiments includes a checkbox  730 , labeled “Edge”, that allows a user to re-identify the edges of a displayed image. In some embodiments, selection of the checkbox  730  causes the image-editing application to re-identify the edges and then display the image in an edge view. In the edge view, only the edges of the image are displayed in display window  705 , rather than the actual image. Some embodiments also provide a user interface tool (e.g., a button, or a checkbox) that re-identifies the edges but does not cause the image-editing application to display the image in the edge view. Some embodiments do not allow a user to view the image in the edge view (but still are able to identify the edges of the image). Some embodiments also provide checkbox  735 , labeled “Degrain prior Edge Detect”, that when selected causes the image-editing application to apply a de-noise algorithm to the image before re-identifying edges. Other edge identification tools provided by some embodiments include sliders  740  that allow for users to alter parameters of the edge identification and de-noise algorithms. In some embodiments, the sliders  740  affect the sensitivity of the edge identification and de-noise algorithms. In other words, the sliders affect the likelihood that a potential edge will be identified. Some embodiments set the sensitivity of the edge identification and de-noise algorithms as defaults and do not provide sliders  740 . Edge identification is described in detail in Section III. 
     The set of border definition tools  715  provides users with various options to use in order to define a border for the image by moving over the image in the display window  705  with a cursor. For example, some embodiments provide a checkbox  745 , labeled “Tunnel”, that allows a user to determine whether a tunnel will be generated around a border after the border is defined. Some embodiments provide a checkbox  750 , labeled “Moving Avg”, which allows a user to determine whether a search window will be displayed around the cursor while the user defines a border for the image. Definition of borders, including the definition and use of search windows is described in detail in Section IV. 
     The set of tunnel and alpha generation tools  720  allows users to (1) generate tunnels around defined borders, and (2) define, for each pixel in an image, the extent to which the pixel is in the foreground or background of the image. In some embodiments, the tunnel and alpha generation tools  720  include “Spline” button  755 , which allows users of the image-editing application to define a border of an image in the display area  705  as a spline curve. From the defined border, some embodiments generate a tunnel as two sides around the defined border. The “Move” button  760  provided by some embodiments enables a user to view the sides of a generated tunnel as modifiable curves, and modify the sides. In some embodiments, the modifiable curves are bezier splines. The slider  765  provided by some embodiments enables a user to modify the width of a tunnel, either before or after the tunnel is generated. Tunnel generation is also described in further detail in Section V. 
     Some embodiments generate alpha values based on a tunnel. Some embodiments sample pixels around the tunnel in order to generate the alpha values. Some embodiments of the image-editing application provide a slider  767  that enables a user to modify the width of the area from which pixels are sampled. Once the alpha values are generated, the image-editing application of some embodiments allows a user to toggle through different views that illustrate the alpha values in different ways. Some embodiments provide a drop-down menu  770  for selecting between the different views. Some embodiments provide a first view that illustrates the image in grayscale with the alpha value of a pixel denoted by the luminance of the pixel. Some embodiments provide a second view that overlays the alpha value of a pixel on the image. The overlay can be gray, red, blue, or green in some embodiments. Some embodiments provide a view that shows the image and any color correction based on the alpha. Alpha generation from a tunnel is discussed in detail below in Section VI. The image-editing application of some embodiments also provides a “Brush” button  775  that allows use of an alpha brush in conjunction with the alpha values. In some embodiments, the alpha brush can define pixels as either foreground, background, or somewhere in between, while in other embodiments the alpha brush can only define pixels as foreground pixels. 
     The set of color correction tools  725  provides a user with the ability to edit digital images. Color correction tools allow users to edit a selected section of a digital image. In some embodiments, the selected section for editing is selected using the other sets of tools such as border definition tools  715  and alpha generation tools  720 . Color correction tools  725  allow users to modify features of a selected section such as the hue and saturation of the pixels. Some embodiments include color wheel  780  that enables a user to shift the color of a selection towards one color or another. 
     It is to be understood by one skilled in the art that some embodiments of the image-editing application may include the functionalities as described but associated with different UI tools than those described. For example, one skilled in the art would recognize that a UI tool allowing a user to determine whether to graphically display a search window need not be a checkbox labeled “Moving Avg”, but could be a checkbox with a different label, a menu option, a selectable button, etc. Similarly, UI tools shown as selectable buttons could be checkboxes, menu options, or other types of UI tools. 
     The image-editing tools and processes that are described above and below can be incorporated into any image-editing application by way of a plug-in, applet, or direct function incorporated within the application itself. Accordingly, different image-editing applications, such as Apple Aperture®, Apple iPhoto®, Adobe Photoshop®, and Adobe Lightroom® may each implement one or more of the image-editing tools described herein. Additionally, the image-editing tools and processes described above and below can be incorporated within the functionality of any other application (e.g., video-editing applications, digital photo albums, etc.), or within an operating system (e.g., Microsoft Windows®, Apple Mac OS®, etc.). 
     III. Edge Identification 
     A. Pre-Computing Edges 
       FIG. 8  presents a process  800  that pre-computes edges of an image in accordance with some embodiments of the invention. Process  800  is performed by an image-editing application in some embodiments. Process  800  begins when it receives (at  805 ) a selection of an image to display. In some embodiments, this selection is received by way of a user choosing to open an image.  FIG. 9  illustrates a user selecting to open an image file  905 . The user highlights the file name of image file  905  and clicks on “Open” button  910  in order to select to open the file in some embodiments. 
     After receiving the selection of an image to display, the process  800  then computes (at  810 ) the edges of the image. In some embodiments, the process computes the edges using the Canny edge detection algorithm or a variant thereof. In other embodiments, the process uses different algorithms to compute the edges. Parameters for the edge computation can be set in some embodiments by the use of sliders  915 , which a user can modify before selecting to open the image. In some embodiments, the parameters modify the sensitivity of the edge detection algorithm. After computing the edges, the process stores (at  815 ) the edges in a memory of the computer on which the process is running. The edges can thus be accessed for other processes such as defining a border for the image. 
     At  820 , after the edges are computed, the process displays the image in the display window.  FIG. 10  illustrates the result of image file  905  being selected by a user.  FIG. 10  illustrates image  1005  displayed in display window  1010 . The edges of image  1005  are already computed before the image is displayed. At  825 , the pre-computed edges are used in the definition of a border of the displayed image. In some embodiments, the border is snapped to the edges as the border is defined by a cursor moving over the image. Border definition using the pre-computed edges is described in detail in Section IV. 
     B. Identifying Only Relevant Edges 
       FIG. 11  presents a process  1100  of some embodiments that applies a de-noise algorithm to an image so that only the most relevant edges are identified. Some edge detection algorithms will identify edges in images where there are not actually useful edges. For example, if an area of the image is cluttered with small and contrasting components, such as a shirt pattern, edge detection may detect many small and undesired edges in that area that a user would probably not want a border to snap to while defining the border. Process  1100  starts when an image is displayed at  1105 . In some embodiments, the edges are calculated when the image is initially displayed, as described above by reference to process  800 . At  1110 , the process determines whether a request to recompute edges has been received. In some embodiments, a request to recompute edges is received when a user selects a user interface (“UI”) tool that recomputes the edges of an image using an edge detection algorithm and displays the edges. Referring back to  FIG. 7 , checkbox  730  is such a UI tool in some embodiments. A user might wish to recompute edges after changing a parameter relating to the edge detection algorithm, such as by using one of the sliders  740 . In some embodiments, the user can use one of the sliders to affect the sensitivity of the edge detection algorithm. 
     If the process  1100  has not received a request to recompute edges, the process determines (at  1115 ) whether the image is still open. If the image is still open, the process returns to  1105  and continues displaying the image. If the image is not still open, process  1100  ends. If a user has closed the image, or opened a different image for editing, then the process would determine that the image is not still open. If, at  1110 , the process determines that a request to recompute edges has been received, the process then determines (at  1120 ) whether a de-noise functionality is selected. In some embodiments, a user selects the de-noise functionality by selecting a UI tool such as checkbox  735 . If the de-noise functionality is not selected, the process  1100  computes (at  1130 ) the edges of the image using the edge detection algorithm. In some embodiments, the process then displays (at  1135 ) the edges of the image.  FIG. 12  illustrates the edges of image  1205 . In  FIG. 12 , checkbox  1210  is selected in order to recompute and display the edges of image  1205 . Checkbox  1215  is not selected, indicating that the de-noise functionality is not selected. 
     If the process determines (at  1120 ) that the de-noise functionality is selected, the process then applies (at  1125 ) a de-noise algorithm to the image. A de-noise algorithm is applied to an image before performing edge identification such that the edge identification algorithm will only compute the most relevant edges of the image. In some embodiments, the de-noise algorithm is a bilateral or anisotropic filtering algorithm. Such an algorithm blurs the image only where a low frequency texture exists. In other words, areas in which a sharp edge exists will be left alone, while areas of the image with no sharp edges will be blurred. This will result in the relevant (i.e. real) edges being maintained while the areas without relevant edges are blurred. Some embodiments allow a user to modify at least one parameter of the de-noise algorithm. For example, some embodiments provide a slider such as one of the sliders  740  of  FIG. 7  for adjusting the sensitivity of the de-noise algorithm. After applying the de-noise algorithm to the image, the process then computes (at  1130 ) the edges of the image. Because the areas of the image not containing relevant edges are blurred with the de-noise algorithm, the edge detection algorithm is less likely to compute irrelevant (i.e. false) edges. The process  1100  then stores (at  1135 ) the edges in a memory of a computer on which the process is running. 
     After computing and storing the edges of the image, some embodiments display (at  1140 ) the edges of the image.  FIG. 13  illustrates the edges of image  1305 . In  FIG. 13 , checkbox  1310  is selected in order to recompute and display the edges of image  1305 . Checkbox  1315  is also selected, indicating that the de-noise functionality is selected and that the de-noise algorithm was applied before the shown edges were computed. Comparing  FIG. 12  and  FIG. 13 , it can be seen that area  1220  includes many small computed edges, whereas the corresponding area  1320  does not include any computed edges as a result of the de-noise algorithm having been applied. Overall, the edges of image  1305  are sharper and more defined than the edges of image  1205 . Some embodiments do not display the edges of the image, but instead only compute the edges and store them in memory. Some embodiments include an option as to whether to display the edges when the edges are re-computed. 
     IV. Border Definition 
     Some embodiments of the invention allow a user to define a border for an image. In some embodiments, the border automatically snaps to the edges of the image. In some embodiments, by snapping the border to the edges it is meant that a segment of the border is automatically drawn along an edge of the image in the vicinity of a cursor moving over the image, as opposed to following the exact path of the cursor. In some embodiments, the edges are computed as described above in Section III. When the edges are pre-computed, the border snaps to the pre-computed edges, as opposed to computing edges as the cursor moves over the image to define the border. 
     A. Searching for Edges While Defining a Border 
     Some embodiments of the invention search for identified edges while defining a border for an image. Some embodiments define a search area around a cursor in which to search for identified edges. Some embodiments base at least one property of the search area on an attribute of the cursor. The property of the search area is the size of the search area in some embodiments. Other embodiments base the orientation or shape of the search area on the attribute of the cursor. In some embodiments the attribute is the speed of the cursor. Other embodiments base the property of the search area on other attributes of the cursor, such as the acceleration. 
       FIG. 14  illustrates a process of some embodiments for defining a border for an image. The process begins by displaying (at  1405 ) an image. At  1410 , the process receives input to define a border for the image. In some embodiments, the input is a user placing a cursor over a point in the image and clicking a mouse button. The user holds the mouse button down while moving the cursor to make the selection of the border in some embodiments. In other embodiments, the user clicks and releases a mouse button in order to begin the definition of the border. 
     At  1415 , the process  1400  defines a search window for edges of the image based on the speed of the cursor. In some embodiments, the edges are pre-computed, with or without applying a de-noise algorithm, as described in Section III. The speed of the cursor is determined by how quickly the cursor moves over the image. In some embodiments, the process determines the speed based on pixels per unit of time, inches per unit of time, or other appropriate units for speed. The search window is used to search for edges to which the process automatically snaps the selected border. In some embodiments, snapping the border to an identified edge entails (1) identifying a second point along the edge in the vicinity of a first point (different than the second) over which the cursor is moving, and (2) drawing the border over the second point rather than having the border follow the movement of the cursor. 
     In some embodiments, the size of the search window is proportional to the speed of the cursor, such that if the cursor moves faster the search window is defined to be larger. This is a linear proportionality in some embodiments, while other embodiments use other relationships between the cursor speed and the search window size. Some embodiments define a proportional relationship over a range of search window sizes, but have a minimum and/or maximum search window size such that the search window is never too small (i.e., very close to zero) or too large (i.e., the entire image). 
     The shape of the search window is different in different embodiments. Some embodiments define a circular search window in which the radius of the circle is proportional to the speed of the cursor. Some embodiments, on the other hand, define a square search window in which the length of the sides of the square is proportional to the speed of the cursor. Some embodiments center the search window, whether circular, square, or other shape, at the point of the cursor. In some embodiments that center the search window at the cursor, the search window is not centered when the cursor is at the edge of the image. This allows for full utilization of the entire search window. Some embodiments use a search window that is not centered at the point of the cursor. 
     Some embodiments provide a graphical illustration of the search window for the user that allows the user to visualize the area in which the process is searching for edges. In some embodiments, an image-editing application provides an option to the user as to whether or not to display the graphical illustration.  FIG. 15  illustrates an image-editing application  1500 , an image  1505 , a border  1510  that is being selected, a cursor  1515 , and a search window  1520  with side length  1525 . The search window  1520  indicates the search window in which the image-editing application searches for edges within the image  1505 . The side length  1525  of box  1520  is related to the speed of the cursor  1515 . In the embodiment illustrated in  FIG. 15 , the search window  1520  is square and centered about the cursor. As mentioned above, in other embodiments the search window can be shaped and centered differently. In  FIG. 15 , side length  1525  (and thus search window  1520 ) is relatively small because the cursor is moving slowly. 
       FIG. 16  illustrates the same image  1505 , along with border  1610 , cursor  1615 , and search window  1620  with side length  1625 . Side length  1625  (and thus search window  1620 ) is larger than the side length  1525  (and thus search window  1520 ) illustrated in  FIG. 15 . This is on account of the cursor  1615  moving faster than the cursor  1515 . Because there are very few edges in the vicinity of cursor  1615 , and the baby&#39;s shoulder against the background forms a very clean edge, a user is likely to be moving cursor  1615  more quickly than cursor  1515 . The vicinity of cursor  1515  includes an edge at the bottom of the baby&#39;s chin, an edge where the baby&#39;s neck meets the bib, and an edge at the bottom of the stripe at the top of the bib. With more edges in the vicinity (i.e., the desired edge is not as clean an edge), a user is more likely to be moving cursor  1615  slowly so as to keep the cursor near the desired edge. 
     After defining a search window at  1415 , the process  1400  determines whether any identified edges of the image are within the search window. If at least one identified edge is within the search window, the process automatically snaps (at  1425 ) the border to an identified edge within the search window. If more than one identified edge is found within the search window, some embodiments snap the border to the edge closest to the cursor. Some embodiments determine whether a previously defined segment of the border is along an edge, and then snap the border to that edge if the edge continues within the search window. 
       FIGS. 17-19  illustrate a searching process of some embodiments.  FIG. 17  illustrates a search window  1700  comprising  144  pixels  1705  (a square search window with a side length of 12 pixels). Inside the search window are edges  1715  and  1720 . Also illustrated is the searched area  1710 . The embodiments illustrated in these figures use a spiraling area to identify the edge to which the border should be snapped. Once the spiral  1710  finds an edge, the process snaps the border to the found edge. In  FIG. 17 , the searched area  1710  is only one pixel. Because there is no edge that includes the pixel covered by  1710 , the process continues searching.  FIG. 18  illustrates the search area after seven pixels have been searched. The arrow inside the search area indicates the order in which the pixels were searched. At this point, the spiraling search area  1710  has still not yet come across an edge, and therefore continues searching.  FIG. 19  illustrates the search area  1710  once it has found edge  1715 . Because edge  1715  is the first edge found by search area  1710 , the process snaps the border to edge  1715  in the illustrated embodiments. Thus, the illustrated embodiments would not snap the border to edge  1720 , though some other embodiments might do so. In the illustrated embodiments, the search window indicates a maximum size to which the search area  1710  can grow. Some embodiments use a spiraling search area with a thickness of more than one pixel for faster searching. The search spiral is dependent on the size of the search window in some embodiments. 
     If no identified edges are found within the search window, the process  1400  draws (at  1430 ) the border along the movement of the cursor.  FIG. 20  illustrates image  1505  and border sections  2010  and  2015 . Border section  2010  is snapped to identified edges; specifically, the edges formed by the baby&#39;s face and shoulder against the background. Border section  2015  is not snapped to any identified edges, but instead follows the movement of the cursor through the background of the image. 
     After drawing the border at either  1425  or  1430 , the process proceeds to  1435  and determines whether the current cursor speed is changed from the speed used to initially define the search window at  1415 . If the cursor speed is changed, the process redefines (at  1440 ) the search window based on the cursor speed. In embodiments for which the search window size is proportional to the cursor speed, the process enlarges the search window if the cursor speed is increased and reduces the search window if the cursor speed is decreased. After redefining the search window based on the new cursor speed, the process proceeds to  1445  and determines if input to define the border is still being received. In some embodiments, if a mouse button is still held down then input to define the border is still being received. Other embodiments begin and end the definition of a border with a click and release of a mouse button, such that the mouse button is not held down during the definition process. If at  1435  the process  1400  determines that the cursor speed is not changed, the process proceeds directly to  1445 . If border definition input is still being received, the process proceeds to  1420  to continue drawing the border. If border definition input is no longer being received, the process ends. 
     B. Deleting Part of a Border 
     Some embodiments of the invention delete at least one segment of a previously defined border. In some embodiments, segments are deleted during the process of defining the border. Some embodiments delete segments of the border when the cursor used to define the border moves back over the previously defined border. Other embodiments delete the border when the cursor moves to within a threshold distance of the previously defined border. Other embodiments use other mechanisms to delete segments of the border, such as a combination of the direction of the cursor and the proximity of the cursor to the border. 
       FIG. 21  presents a process  2100  performed by some embodiments to define a border of an image that allows part of the border to be deleted during the definition process. Process  2100  starts by displaying (at  2105 ) an image. At  2110 , the process receives input to define a border of the image. In some embodiments, the input is from a user placing a cursor over a point in the image and clicking a mouse button. The user holds the mouse button down while moving the cursor over the image to define the border in some embodiments. In other embodiments, the user clicks and releases a mouse button in order to begin defining the border. At  2115 , the process displays the selected border as a set of segments with reference points between the segments. Some embodiments require only one segment to be drawn before any of the border can be deleted, while other embodiments require any number of segments greater than one (e.g., a minimum of two, three, or more segments). Some embodiments use process  1400  to search for identified edges in the image and snap the border to the edges while defining the border. 
       FIG. 22  illustrates a border of some embodiments being defined with process  2100 .  FIG. 22  illustrates image  2205 , cursor  2210 , and border  2215 . Border  2215  as displayed includes border segments  2220  and reference points  2225 . The reference points  2225  are in between segments  2220 . Border  2215  was drawn with the cursor  2210  starting at position  2230 , and is snapped to edges in the vicinity of the path taken by the cursor moving over the image from  2230  to the shown cursor position at  2235 . 
     In some embodiments, the border includes both snapped points (i.e., points that are drawn along identified edges) and non-snapped points (i.e., points that are drawn along the cursor movement). The reference points  2225  are drawn at each of the snapped points in some embodiments, while in other embodiments the reference points  2225  are drawn at a subset of the snapped points. In yet other embodiments, the reference points  2225  are drawn equidistant from each other. Still other embodiments draw the reference points  2225  at varying distances along the border based on how fast the cursor moves to draw the border. Some embodiments define the border as a set of coordinates, as described below. 
     At  2125 , process  2100  determines whether input to define the border is still being received. In some embodiments, if the mouse button is still held down then input to define the border is still being received. Other embodiments begin and end definition of the border with a click and release of a mouse button, such that the mouse button is not held down during the definition process. If input to define the border is no longer being received, the process  2100  ends, as the border definition process is finished. If border definition input is still being received, the process determines (at  2130 ) whether the cursor is moving away from the last reference point. The last reference point is the most recently drawn reference point on the border. In  FIG. 22 , the last reference point is at  2235 . If the cursor is moving away from the last reference point, the process continues drawing (at  2135 ) the border, displaying new segments and reference points in between the segments.  FIG. 23  illustrates new segments  2335  with reference points that have been added to border  2215  as a user continues to draw the border by moving the cursor away from the previously defined border. After  2135 , the process returns to  2125  to determine whether border definition input is still being received. 
     If, at  2130 , the process determines that the cursor is not moving away from the last reference point, the process proceeds to  2140  and determines whether the cursor has moved back over the previously defined border. The previously defined border includes all of the border that has been drawn and not yet deleted. In doing so, in some instances a user would essentially be retracing the already-drawn border in the direction opposite which the border was drawn. A user might also loop back to a point along the previously defined border. If the cursor has not moved back over the previously defined border, the process returns to  2125  to determine whether border definition input is still being received. If the cursor has moved back over the border, the process deletes (at  2145 ) at least one segment of the border. When segments are deleted, this sets a new reference point as the last reference point for the purposes of  2130 . 
     Some embodiments do not require a user to actually retrace the border opposite the direction in which the border was drawn, but will delete at least one segment if the cursor is moved back onto the previously defined border, even if the cursor is moved in a more roundabout way than a directly retracing the border. If a user places the cursor over the previously defined border more than one segment back from the end of the border, some embodiments delete all segments of the border beyond the location of the cursor. After  2145 , the process returns to  2125  to determine whether border definition input is still being received. The process also returns to  2125  from  2140  if it determines that the cursor has not moved back over the previously defined border. For example, if a user holds down the mouse button but does not move the cursor, then in some embodiments no new segments of the border would be drawn, and no segments would be deleted, but border definition input would still be being received. 
     In the process described immediately above, some embodiments define the border as an ordered set of coordinates while the border is being defined. For example, each pixel along the defined border is assigned an (x, y) pair. When the cursor moves over a pixel with a particular (x, y) pair that is already in the set of coordinates, the process deletes all the coordinates in the ordered set that come after the particular coordinate pair. Some embodiments delete all the coordinates when the cursor moves within a threshold distance of the particular coordinate pair. 
     In  FIG. 23 , some of the new segments  2335  are snapped to the baby&#39;s bib. However, it is more likely the case that a user is attempting to define a border around the entire baby and would prefer to have the border run down the baby&#39;s arm.  FIGS. 24 and 25  illustrate the use of process  2100  to delete segments of a border and then define new segments.  FIG. 24  illustrates the border  2215  after a user has moved the cursor back over the previously defined border to point  2435 , thereby deleting a number of the segments  2335  that were incorrectly drawn.  FIG. 25  illustrates the border after the user has continued the selection of the border  2215  down the baby&#39;s arm.  FIG. 25  illustrates new segments  2535 . From the position of the cursor  2210  in  FIG. 25 , the user can either finish selection of the border or continue down the baby&#39;s arm. 
     Some embodiments allow a user to modify a border after the border is defined. In some embodiments, the border will attempt to automatically snap to edges as the border is modified. If a user determines that it would be preferable for a border defined along edges to not run along an edge for at least one point, the user can move the border off of the edge. Some embodiments use such a border to define a selection with no transition, and thus do not generate a tunnel or a foreground to background transition from the defined border. 
     In some embodiments, the border is treated as a parametrizable curve with several modifiable points. A parametrizable curve is a curve that is defined about certain definition points by a particular equation or set of equations. This is in contrast to a raster curve, which is defined by the set of all pixels that make up the curve. In some embodiments, the parametrizable curve can be modified by moving the definition points. In some embodiments (e.g., embodiments using bezier splines), the definition points for modifying the curve lie on the curve. In other embodiments (e.g., embodiments using b-splines), the definition points for modifying the curve lie off of the curve. In addition to the definition points, some embodiments (e.g., embodiments using bezier splines) define the parametrizable curve based on tangents to the curve at the specific points as well. Users can add definition points to the curve in some embodiments, and then modify the curve based on the new point. 
       FIG. 26A  illustrates an image  2605  through which a border  2610  has been defined. Defined border  2610  includes definition point  2615  in addition to several other definition points. In some embodiments, the definition points can be dragged by a cursor to modify a defined border. In the embodiments illustrated, the border  2610  is a bezier spline, but is shown without tangents for purposes of simplifying the illustration in the figure.  FIG. 26B  illustrates a border  2650  as a b-spline, in which definition points  2655  are not on the border. Image  2605  includes edge  2620  in addition to several other edges.  FIG. 26  also illustrates cursor  2625 , which is at definition point  2615 .  FIG. 27  illustrates the result of a user dragging the definition point  2615  down and to the right using cursor  2625  to modify border  2610 .  FIG. 27  illustrates an example of a border being modified such that the modified border no longer runs along an edge of an image.  FIG. 27  also illustrates edge  2720 , along which the border had been running when the border was initially defined.  FIG. 28  illustrates the result of a user dragging the definition point  2615  up and to the left using cursor  2625  to modify border  2610 .  FIG. 28  illustrates an example of a border being modified such that the modified border snaps to a different edge than initially defined. In  FIG. 28 , the border has snapped to edge  2620 , while the border was initially snapped to edge  2720 . 
     V. Generation of a Tunnel from a Defined Border 
     Some embodiments of the invention generate a two-dimensional tunnel about a portion of the image based on movement of a cursor through the portion of the image. In some embodiments, the tunnel defines a boundary within the image. Rather than a hard edge, the tunnel is a transitional edge or region that can be multiple pixels wide. The tunnel of some embodiments is generated as a pair of deformable curves. Some embodiments allow both curves to be modified either together or separately. 
       FIG. 29  presents a process  2900  of some embodiments for generating a tunnel from a defined border. Process  2900  begins by displaying (at  2905 ) an image. At  2910 , the process receives input defining a border for the image. In some embodiments, the border is defined as described in Section IV. Other embodiments may define the border differently. Some embodiments treat the defined border as a spline curve. In some embodiments, the border is smoothed before a tunnel is generated from the border.  FIG. 30  illustrates a defined border.  FIG. 30  illustrates image  3005 , border  3010 , and slider  3015 . In some embodiments, the user defines the border by placing the cursor at point  3020  and holding the cursor down while moving to point  3025 . In some embodiments, the process generates the tunnel after the cursor is released with no further user input required. 
     After receiving input to define the border, the process  2900  determines (at  2915 ) an initial width for the tunnel that will be generated. Some embodiments determine the initial width based on a UI tool that can be modified by a user. For example, in some embodiments the initial width is based on the setting of a linear slider tool. Referring to  FIG. 30 , the slider  3015  is the linear slider tool of some embodiments that is used to determine the initial width of the tunnel. Some embodiments use other ways to define the initial width of the tunnel, such as a numeric input from a user. 
     After determining the initial width for the tunnel, the process determines (at  2920 ) whether the tunnel will intersect with itself if the two sides of the tunnel have the same shape (that of the defined border). If a border is defined such that it is traced in one direction and then continues back in the opposite direction near the previously defined section, then depending on the initial width it might be impossible to generate a tunnel with both sides having the same shape. If generating the tunnel with both sides having the same shape will not cause the tunnel to intersect itself, the process generates (at  2925 ) the tunnel at the width determined at  2915 . The tunnel is generated such that the sides of the tunnel run parallel to the defined border and are equidistant from the defined border.  FIG. 31  illustrates image  3005  with tunnel  3110  generated at a constant width from border  3010 . Slider  3015  is set at a first distance from the left side that is one-fourth of the distance to the right side of the slider. As the defined border  3015  is mostly straight, generating tunnel  3015  with the two sides having the same shape (and therefore the tunnel having a constant width) does not result in the tunnel intersecting itself. 
     If the process  2900  determines at  2920  that the tunnel will intersect itself when generated with the two sides having the same shape, then the process generates (at  2930 ) the tunnel, varying the shape of one or both sides where necessary to avoid self-intersection. The process attempts to draw the tunnel at as constant a width as possible, and will modify the shape of one or both sides of the tunnel in order to keep the width as close to constant as possible without having the tunnel self-intersect. In some embodiments, the modification is kept as minimal as possible.  FIG. 32  illustrates a defined border  3210  displayed on image  3205 , along with slider  3215 . Slider  3215  is set near the halfway point to determine the width of a tunnel to be generated from border  3210 .  FIG. 33  illustrates the tunnel  3310  with non-constant width generated from the defined border  3210 . The primary tunnel width is set by slider  3215 . Tunnel  3310  has sides  3320  and  3325 . Because of the shape of border  3210 , tunnel  3310  is generated with the two sides  3320  and  3325  shaped differently. Specifically, within area  3330 , side  3325  is pinched down as compared to side  3320 . The width of the tunnel is kept as close to constant as possible, although the width does decrease slightly close to where side  3325  comes to a point. 
     Once the tunnel is generated, the process determines (at  2935 ) whether any modifications to the tunnel are received. Some embodiments allow modifications to the width of the tunnel. The modifications of some embodiments can also be modifications to the shape of one or both sides of the tunnel. 
       FIG. 34  illustrates the result of modifying the width of tunnel  3110  from  FIG. 31  using the slider tool  3015 . In some embodiments, slider tool  3015  is used to both determine the initial width of a tunnel and to modify the width of the tunnel after the tunnel is generated. In  FIG. 34 , tunnel  3110  is substantially wider than in  FIG. 31  because the user has moved slider  3015  from the one-fourth mark to just past the three-fourths mark. Because border  3010  was defined such that it was primarily straight, the tunnel  3110  as shown in  FIG. 34  after the modification to the width would look the same had slider  3015  been set at just past the three-fourths point when the tunnel was initially generated. 
       FIG. 35  illustrates the result of modifying the width of tunnel  3310  from  FIG. 33  using the slider tool  3215 , which is near the three-quarter point in  FIG. 35 . Not only are the sides of tunnel  3310  further apart, but side  3325  has become even more pinched down in the area  3530 . This is the result of the process attempting to keep the width as constant as possible while moving slider  3215 . Thus, in some embodiments modifying the width of the tunnel can not only modify the width but also the shape of one or both sides of the tunnel. 
     Some embodiments allow modifications directly to the shape of the sides of the tunnel. The sides of the tunnel are defined as parametrizable curves in some embodiments. In some embodiments, the parametrizable curves are bezier splines that are made up of a set of modifiable points.  FIG. 36  illustrates image  3605 , tunnel  3610 , cursor  3615 , and UI item  3620  (in the embodiment shown, a button labeled “Move”). Tunnel  3610  encircles the baby&#39;s hand.  FIG. 36  illustrates a user utilizing the cursor  3615  to select UI item  3620 .  FIG. 37  illustrates a close-up of tunnel  3610  after the user has clicked on UI item  3620  which displays the two sides  3705  and  3710  of the tunnel as bezier spline curves. The spline curves include control points and tangents, including corresponding inner and outer control points  3715  and  3720  and tangents  3725  and  3730 . The outer tangent  3725  is longer than the inner tangent  3730  because tangent  3725  is on the exterior of a curve while corresponding tangent  3730  is on the interior of the curve. In some embodiments, a user can manipulate the spline curve in order to modify the tunnel. Modifications to one spline do not affect the other spline in some embodiments. The control points, such as points  3715  and  3720 , can move that point on the spline in any direction in the image. In some embodiments, moving a control point on one spline causes both splines to move according to the movement of the control point. When a control point is moved, in some embodiments nearby points are moved as well according to the mathematics of bezier spline curves, which is well known to one of ordinary skill in the art. 
       FIG. 37  also illustrates cursor  3735  over a point on the outer spline  3705 . In some embodiments, users can add control points to one or both spline curves. A user can click on a section of the curve in some embodiments in order to add a control point at that point. In some embodiments, adding a control point also adds a corresponding tangent at the control point.  FIG. 38  illustrates control point  3815  and tangent  3820  that have been added to outer spline  3705 . Control point  3815  has been moved inwards. For reasons discussed in Section IV, it is advantageous in some embodiments to not have any of the baby&#39;s ear inside the tunnel, which moving point  3815  inwards helps in realizing. Note that there is no corresponding control point at  3825  on the inner spline  3710 .  FIG. 38  also illustrates outer tangent  3825  and corresponding inner tangent  3830 . Outer tangent  3825 , which had been longer than inner tangent  3830 , is now shorter as a result of the application of bezier spline mathematics to the movement of point  3815 . In some embodiments, clicking on the endpoints of a tangent allows the user to modify the orientation of the tangent. Some embodiments allow a user to modify the length of a tangent by directly using the endpoints of the tangent, as opposed to by moving the control point. In  FIG. 38 , outer tangent  3825  is no longer parallel to corresponding inner tangent  3830 . This is the result of the left endpoint of tangent  3825  being dragged downwards so as to modify the nearby section of the outer spline.  FIG. 39  illustrates splines  3705  and  3710  after control point  3915 , which is the control point associated with tangent  3825 , is moved inwards. These modifications to the tunnel produced the advantageous result of having the baby&#39;s ear entirely outside the tunnel. 
     Returning to process  2900 , if the process receives (at  2935 ) a modification to the tunnel, the process modifies (at  2940 ) the tunnel in accordance with the received modifications. In some embodiments, the modifications include those described above such as modifications to the width of the tunnel or modifications to one or both of the bezier splines. If no modifications are received at  2935 , or after the tunnel is modified (at  2940 ) in accordance with any received modifications, the process determines at  2945  whether to continue allowing modifications to the tunnel. In some embodiments, if a user has generated alpha values for pixels inside the tunnel, then the tunnel can no longer be modified. If a user has started to define a new border in order to generate a new tunnel, then in some embodiments the previous tunnel cannot receive any more modifications. If the process determines to continue receiving modifications, the process returns to  2935 . If not, the process  2900  ends. 
     VI. Generation of Foreground to Background Transition 
       FIG. 40  conceptually illustrates a process  4000  performed by some embodiments for selecting a section of interest within an image. The process  4000  begins at  4005  by displaying an image.  FIG. 41  illustrates an image  4100 . The image  4105  includes a section of interest  4105  and transition region  4110 . In the transition region,  4110 , some pixels are part of the section of interest and some are not. At  4010 , the process defines a border for the image about the section of interest. In some embodiments, the border is defined in the manner described in Section IV above. Other embodiments define the border in different ways. For example, some embodiments use a first-level heuristic to define the border that does not involve a drawing tool. In some embodiments, the border is a curvilinear border.  FIG. 42  illustrates a border  4210  defined about the section of interest  4105 . Because of the complex nature of the image data at the transition region, the simple border is unable to adequately capture all of the data that is within the section of interest. 
     At  4015 , the process  4000  generates a transition tunnel region about the section of interest from the defined border. Some embodiments generate the tunnel region as described in Section V above.  FIG. 43  illustrates a tunnel region  4310  generated about the section of interest  4105 . Unlike border  4210 , the interior of the tunnel  4310  includes all of the pixels from the transition region  4110 . Finally, at  4020 , the process  4000  analyzes image data to determine which pixels in the tunnel region are part of the section of interest. Some embodiments compare pixels on the either side of the tunnel (some of which are defined as being in the section of interest and some of which are defined as not being in the section of interest) to pixels on the interior of the tunnel, and classify the interior pixels based on the comparison. Some embodiments generate alpha values for each of the interior pixels.  FIG. 44  illustrates pixels in black  4405  that are defined as being in the section of interest and pixels in white that are defined as not being in the section of interest. 
       FIG. 45  conceptually illustrates a process  4500  performed by some embodiments to generate alpha values for pixels in an image. The process  4500  is performed by some embodiments of process  4500  to generate a foreground to background transition for at least a section of an image. The process  4500  begins at  4505  by displaying an image. At  4510 , the process receives input to define of a border for the image. In some embodiments, the border is defined in the manner described in Section IV above. 
     At  4515 , the process  4500  generates a tunnel around the border. Some embodiments generate the tunnel in the manner described above in Section V.  FIG. 46  illustrates a portion of an image  4605 .  FIG. 46  illustrates tunnel  4610 , which divides the portion of the image into three sections: foreground  4615 , background  4620 , and transition section  4625 . In some embodiments, the process determines which side is the background and which side is the foreground based on the direction in which a cursor is traced over the image to define the border. For example, if one imagines walking along the border as the cursor is traced over the image, some embodiments refer to the right side as the foreground and the left side as the background, or vice versa. Some embodiments allow a user to toggle this function such that the foreground and background switch sides. The toggle function can be used before or after drawing the border in some embodiments. Some embodiments enable a user to generate a foreground to background transition for one portion of an image, then generate a foreground to background transition for a second portion of the image. For example,  FIG. 46  illustrates one section of a baby&#39;s head; it would be possible in some embodiments to generate a foreground to background transition within tunnel  4610 , then select a new border starting where  4610  ends. In some such embodiments, the foreground and background are defined based on the definition for the previously selected border. 
     After generating the tunnel, the process  4500  samples (at  4520 ) foreground and background pixels from the outside of the tunnel. In some embodiments, sampling a pixel involves determining chromatic properties of the pixel, such as the pixel&#39;s RGB values or luminance and chrominance values. Some embodiments define a rectangle that includes the tunnel and then sample pixels within the rectangle. In other embodiments, only a narrow strip of pixels just outside the tunnel is used for sample pixels. Some embodiments provide a user interface tool that allows a user to view the regions from which pixels are sampled. This allows a user in some embodiments to manipulate the sides of the tunnel, as described above in Section V, so as to sample pixels from the correct regions. 
       FIG. 47  illustrates tunnel  4610 , foreground samples  4715 , and background samples  4720 . The samples  4715  and  4720  are just along the edges of the tunnel (i.e., immediately outside the tunnel). In some embodiments, the region for taking samples extends further out from the tunnel. Some embodiments provide a user interface tool that allows a user to modify the width of the sampled region (i.e., the distance the sampled region extends outward from the tunnel).  FIG. 48 and 49  illustrate zoomed-in views of two tunnels ( 4805  and  4905 , respectively) with two different sample widths.  FIG. 48  illustrates foreground sample  4810  and background sample  4815  immediately exterior to the tunnel  4805 . In this figure, slider  4820  is set far to the left, indicating a thin sample region.  FIG. 49  illustrates foreground sample  4910  and background sample  4915  immediately exterior to the tunnel  4905 . In this figure, slider  4820  is set far to the right, indicating a wide sample region. Accordingly, samples  4910  and  4915  are from a much wider region than samples  4810  and  4815 . Sampling from a wider region is more computationally intensive, but can provide a greater degree of accuracy in some cases. Some embodiments do not allow a user to determine the sample width, but instead set a default width for the sample regions. 
     Some embodiments sample every pixel within the sampled region. Other embodiments only sample a fraction of the pixels within the region, such as every other pixel or every third pixel, for faster computation. Some embodiments also allow a user to directly modify the section of the image from which pixels are sampled (either for foreground or background, or both) without modifying the tunnel. 
     After sampling the pixels, the process  4500  determines (at  4525 ) an alpha generation algorithm to generate the foreground to background transition inside the tunnel. In some embodiments, the process always uses the same algorithm. The algorithm is a random walks algorithm in some embodiments. In other embodiments, the process selects between more than one algorithm. Some embodiments select an algorithm based on a user decision as to which algorithm to use. Some embodiments prompt the user to select an algorithm, while other embodiments rely on a predefined selection by the user. Other embodiments select an algorithm based on the sampled pixels. For example, if the background is a bluescreen, an algorithm that creates a sharper foreground to background transition might be advantageously selected. 
     After the alpha generation algorithm is determined, the process  4500  proceeds to  4530  to start generating alpha values. At  4530 , the process selects a pixel inside the tunnel. The process then calculates (at  4535 ) an alpha value for the selected pixel. The alpha value for the selected pixel is calculated using the alpha generation algorithm determined at  4525 . The alpha value is calculated by comparing the pixel values of the samples to the pixel values of the selected pixel. As mentioned above, the pixel values of some embodiments are the chromatic properties of the pixel, such as RGB values or luminance and chrominance values. The calculated alpha value of some embodiments is a value from 0 to 1, inclusive. The alpha value gives the extent to which the pixel is part of the foreground and part of the background. In some embodiments, a value of 0 indicates the pixel is entirely in the background and a value of 1 indicates the pixel is entirely in the foreground. An alpha generation algorithm that would be used for a bluescreen background to create a sharper foreground to background transition would thus be more likely to calculate alpha values close to 0 and 1, as opposed to in the 0.25-0.75 range. 
     After calculating the alpha value for the selected pixel, the process  4500  determines (at  4540 ) whether any pixels remain inside the tunnel. If no more pixels remain, the process has finished alpha generation, and thus ends. If more pixels remain, the process returns to  4530  to select a new pixel inside the tunnel. The process continues calculating alpha values for pixels until all the pixels inside the tunnel have an alpha value. Some embodiments calculate the alpha value for each pixel based on the pixel values for the selected pixel and the sampled pixels. Other embodiments use the pixel values of the previously selected pixels and the alpha values calculated for those pixels to modify the algorithm for the calculation of alpha values for the remaining pixels. 
     Some embodiments of the image-editing application illustrate the calculated alpha values in at least one way. Some embodiments illustrate the calculated alpha values in multiple ways, such as red, green, blue, or gray overlays, showing only the alpha values in grayscale, or not displaying the alpha values at all.  FIG. 50  illustrates calculated alpha values overlaid on the image  4605 . In foreground section  4615 , all pixels are colored gray as this entire section has an alpha value of 1. In background section  4620 , the overlay has no change because the alpha values are all 0. The alpha values for the foreground section  4615  and background section  4620  are defined as 1 and 0, respectively, during the sampling process. In the transition section  4625 , the overlay illustrates that some of the alpha values are the same as the foreground (i.e., a value of 1) and some are the same as the background (i.e., a value of 0), while some are somewhere in between 0 and 1 (e.g., the pixels in the small encircled area  5030 ). Some embodiments define all pixels with an initial alpha value of zero (i.e., purely background), and only those areas defined otherwise as being at least partly in the foreground are given a nonzero alpha value. 
     Some embodiments of the image-editing application can also provide a view that only shows the alpha values.  FIG. 51  illustrates such a view for the alpha values from  FIG. 50 . In  FIG. 51 , alpha values of 1 are shown as white and alpha values of 0 are shown as black. Areas for which an alpha values have not been calculated are designated as having an alpha value of 0, and are thus also black. The pixels within transition section  4625  have alpha values ranging from 0 to 1, and thus the section has pixels with varying shades of gray in addition to black and white. Some embodiments show alpha differently, for example with the foreground (alpha=1) black and the background (alpha=0) white. 
     Some embodiments allow a user to define alpha values for sections of an image with an alpha brush in addition to generating alpha values with the use of tunnels.  FIG. 52  illustrates the image-editing application of some embodiments with the alpha brush tool selected using button  5205 . In some embodiments, selecting the alpha brush tool causes the image-editing application to provide the user interface items  5210 . UI items  5210  allow a user to select whether the alpha brush will add alpha (i.e., define areas as foreground with alpha value equal to 1) or remove alpha (i.e., define areas as background with alpha value equal to 0). 
     In some embodiments, the alpha brush is circular, while in other embodiments the alpha brush is a different shape (e.g., a square). The alpha brush of some embodiments can have a varying size. In some embodiments, a user clicks on a point in the image to start the creation of an alpha brush, and drags outward from the point to determine the radius of the alpha brush.  FIG. 53  illustrates an alpha brush  5305  with a radius  5310 . Alpha brush  5305  also includes control point  5315 . In some embodiments, after creating an alpha brush, a user clicks inside the control point to move the alpha brush around the image, thereby defining areas covered by the brush as either foreground or background. The alpha brush  5305  also includes the transition area  5320 . The transition area of some embodiments transitions from an alpha value that is defined by most of the brush to the alpha value of zero outside the radius of the brush. In some embodiments, areas of the image that are only covered by the transition area will be defined to have an alpha between 0 and 1.  FIG. 53  also illustrates indicator  5325  that indicates whether the alpha brush is being used to add or remove alpha. Indicator  5325  is a plus sign, indicating that the brush is currently being used to add alpha. 
       FIG. 54  illustrates alpha brush  5405  with a radius  5410 . Radius  5410  is larger than radius  5310 . As compared to brush  5305 , alpha brush  5405  is better for defining large sections of an image to have an alpha equal to 1. For example, if selecting a large foreground,  5405  is ideal for selecting the interior of the foreground more quickly, but would not be especially useful around the edges of the foreground. 
       FIG. 55  illustrates alpha brush  5505  with pop-up slider tool  5510 . In some embodiments, pop-up slider  5510  is displayed when a user clicks on the S at the upper left of the alpha brush. The pop-up slider of some embodiments adjusts the softness of the alpha brush. The softness of some embodiments defines the width of the transition area at the edge of the brush (e.g.,  5320  in  FIG. 53 ). A user can click on the large bar  5515  and drag it from side to side to increase or decrease the softness of the alpha brush. 
     As mentioned above, in some embodiments the alpha brush can also be used to remove alpha.  FIG. 56  illustrates alpha brush  5605  having radius  5610 . Alpha brush  5605  defines areas covered by the brush as having an alpha of 0 (i.e., as background). Like brushes  5305  and  5405 , alpha brush  5605  includes a control point  5615  in the center for moving the brush around the image.  FIG. 56  also illustrates indicator  5625 , which is a minus sign in this figure to indicate that the alpha brush  5605  is for removing alpha.  FIG. 57  illustrates the use of alpha brush  5605  to remove alpha. Before the use of brush  5605 , the entire area of an image shown in  FIG. 57  had an alpha value of 1. However, due to the use of cursor  5720  to move the brush  5605  around the image, area  5715  (the area covered by the brush  5605 ) has an alpha value of 0 while area  5710  remains with an alpha value of 1. 
     VII. Performing Color Correction on a Selection 
     The image-editing application of some embodiments uses the calculated alpha values to perform color correction on a selected area of an image. The selection is defined by the calculated alpha values. In some embodiments, the extent to which color correction applies to a particular pixel is defined by the alpha value for that pixel. In some embodiments, the extent to which color correction applies to a particular pixel is given by the equation:
 
Result= F*a+B *(1 −a ),  (1)
 
where F is an image where a color correction function has been applied and B is the original image without any color correction function applied. Thus, if a pixel has an alpha value of 1, color correction will be fully applied to the pixel. If a pixel has an alpha value of 0.75, then color correction will affect the pixel only three-fourths as much as it affects a pixel with an alpha value of 1. Of course, pixels with an alpha value of 0 will not be affected at all by color correction.
 
     Some embodiments also use the selection for cutouts. For example, a selection defined by alpha values can be pasted into a new image. The cutouts of some embodiments are defined similarly to color correction. The alpha value defines the extent to which the pixel is part of the selection that is pasted into the new image. 
     As mentioned above, some embodiments allow a user to generate alpha values from multiple selected borders. This can be advantageous in that it allows a selection of an entire object in multiple pieces.  FIGS. 58-62  illustrate the selection, in multiple pieces, of a baby&#39;s head in image  5805 .  FIG. 58  illustrates a first tunnel  5810 .  FIG. 59  illustrates alpha overlay  5910  generated from tunnel  5810  as well as alpha overlay  5915  generated from a second tunnel (not shown).  FIG. 60  illustrates the alpha overlay after the entire border of the head has been selected, in numerous sections. As can be seen in  FIG. 60 , a substantial region  6010  in the middle of the head does not have an assigned alpha value (i.e., would be treated as having an alpha value of zero). In such a situation, an alpha brush may be used in some embodiments to fill in the middle section. Some embodiments use the alpha brush described above in Section VI.  FIG. 61  illustrates the use of alpha brush  6110  to fill in area  6010 .  FIG. 62  illustrates the result of using the alpha brush with the entire head selected.  FIG. 63  shows the alpha values for the head and surrounding area that were generated by use of the multiple selections and the alpha brush. 
     Once a section of an image is entirely selected, color correction can be applied to the section if the user so desires.  FIG. 64  illustrates the entirety of image  5805  including head  6410 , along with color correction tools  6415 . Color correction tools  6415  include color wheel  6420  and sliders  6425 . The various color correction tools can be used to affect a selected portion of an image. In  FIG. 64 , the baby&#39;s head  6410  is selected, although the alpha overlay is not shown because the image is in color correction mode (i.e., showing only the image with any color corrections, and not any alpha values).  FIG. 65  illustrates that color correction tools have been used to alter the color of the baby&#39;s head. The setting for the color wheel  6420  has been moved to the blue region, and saturation has been greatly increased using the saturation slider, one of the sliders  6420 . As can be seen, the head  6410  is a different shade in  FIG. 65  than in  FIG. 64 , due to the use of color correction tools. 
     VIII. Computer System and Software 
     Many of the above-described tools and applications are implemented as software processes that are specified as a set of instructions recorded on a machine readable medium (also referred to as computer readable medium). When these instructions are executed by one or more computational element(s) (such as processors or other computational elements like ASICs and FPGAs), they cause the computational element(s) to perform the actions indicated in the instructions. Computer is meant in its broadest sense, and can include any electronic device with a processor. Examples of computer readable media include, but are not limited to, CD-ROMs, flash drives, RAM chips, hard drives, EPROMs, etc. 
     In this specification, the term “software” is meant in its broadest sense. It can include firmware residing in read-only memory or applications stored in magnetic storage which can be read into memory for processing by a processor. Also, in some embodiments, multiple software inventions can be implemented as sub-parts of a larger program while remaining distinct software inventions. In some embodiments, multiple software inventions can also be implemented as separate programs. Finally, any combination of separate programs that together implement a software invention described here is within the scope of the invention. 
       FIG. 66  conceptually illustrates the software architecture  6600  of some embodiments of the invention.  FIG. 66  illustrates an image-editing engine  6605 , a border drawing module  6615 , an edge detector  6625 , and a cursor driver  6635 . In some embodiments, the cursor driver  6635  is part of operating system  6645 .  FIG. 66  also illustrates original image data  6610 , edge data,  6620  border data  6630 , image-editing operation data  6640 , and modified image data  6650 . 
     The edge detector  6625  uses the original image data  6610  and identifies edges in the original image to produce edge data  6620 . The edge identification process is described in detail in Section III above. The edge data  6620  is passed to the border drawing module  6615 , which combines the edge data  6620  with input from the cursor driver  6635  to define a border  6630 . The processes performed by the border drawing module  6615  for defining the border, including the generation of varying size search windows, are described in detail in Section IV above. 
     The border drawing module  6615  passes the defined border  6630  to the image-editing engine, which also receives the original image  6610  and the image-editing operation data  6640  as input. The image-editing operation data is color correction operations, such as hue or saturation adjustments, in some embodiments. From the border  6630 , the image-editing engine  6605  of some embodiments determines alpha values for at least a section of the image. The image-editing engine  6605  applies the image-editing operations  6640  to the original image  6610  to produce a modified image  6650 . How the image-editing operations  6640  are applied is based on the border  6630  (and alpha values generated based on the border). The processes performed by the image-editing engine are described in detail above in Sections V-VII. 
       FIG. 67  conceptually illustrates a computer system with which some embodiments of the invention are implemented. Computer system  6700  includes a bus  6705 , a processor  6710 , a graphics processing unit (GPU)  6720 , a system memory  6725 , a read-only memory  6730 , a permanent storage device  6735 , input devices  6740 , and output devices  6745 . 
     The bus  6705  collectively represents all system, peripheral, and chipset buses that communicatively connect the numerous internal devices of the computer system  6700 . For instance, the bus  6705  communicatively connects the processor  6710  with the read-only memory  6730 , the GPU  6720 , the system memory  6725 , and the permanent storage device  6735 . 
     From these various memory units, the processor  6710  retrieves instructions to execute and data to process in order to execute the processes of the invention. Some instructions are passed to and executed by the GPU  6720 . The GPU  6720  can offload various computations or complement the image processing provided by the processor  6710 . Such functionality can be provided using CoreImage&#39;s kernel shading language. 
     The read-only-memory (ROM)  6730  stores static data and instructions that are needed by the processor  6710  and other modules of the computer system. The permanent storage device  6735 , on the other hand, is a read-and-write memory device. This device is a non-volatile memory unit that stores instructions and data even when the computer system  6700  is off. Some embodiments of the invention use a mass-storage device (such as a magnetic or optical disk and its corresponding disk drive) as the permanent storage device  6735 . 
     Other embodiments use a removable storage device (such as a floppy disk or ZIP® disk, and its corresponding disk drive) as the permanent storage device. Like the permanent storage device  6735 , the system memory  6725  is a read-and-write memory device. However, unlike storage device  6735 , the system memory is a volatile read-and-write memory, such a random access memory. The system memory stores some of the instructions and data that the processor needs at runtime. In some embodiments, the invention&#39;s processes are stored in the system memory  6725 , the permanent storage device  6735 , and/or the read-only memory  6730 . 
     The bus  6705  also connects to the input and output devices  6740  and  6745 . The input devices enable the user to communicate information and select commands to the computer system. The input devices  6740  include alphanumeric keyboards and pointing devices. The output devices  6745  display images generated by the computer system. For instance, these devices display a graphical user interface. The output devices include printers and display devices, such as cathode ray tubes (CRT) or liquid crystal displays (LCD). 
     Finally, as shown in  FIG. 67 , bus  6705  also couples computer  6700  to a network  6765  through a network adapter (not shown). In this manner, the computer can be a part of a network of computers (such as a local area network (“LAN”), a wide area network (“WAN”), or an Intranet, or a network of networks, such as the Internet. For example, the computer  6700  may be coupled to a web server (network  6765 ) so that a web browser executing on the computer  6700  can interact with the web server as a user interacts with a graphical user interface that operates in the web browser. 
     Any or all components of computer system  6700  may be used in conjunction with the invention. For instance, in some embodiments the execution of the image-editing functions are performed by the GPU  6720  instead of the CPU  6710 . However, a common limitation of the GPU  6720  is the number of instructions that the GPU  6720  is able to store and process at any given time. Therefore, some embodiments adapt the instructions for implementing the image-editing processes so that these processes fit onto the instruction buffer of the GPU  6720  for execution locally on the GPU  6720 . Additionally, some GPU do not contain sufficient processing resources to execute the processes of some embodiments and therefore the processor executes the instructions. One of ordinary skill in the art would appreciate that any other system configuration may also be used in conjunction with the present invention. 
     While the invention has been described with reference to numerous specific details, one of ordinary skill in the art will recognize that the invention can be embodied in other specific forms without departing from the spirit of the invention. For instance, while the Apple Mac OS® environment is used to create some of these examples, a person of ordinary skill in the art would realize that the invention may be practiced in other operating environments such as Microsoft Windows®, UNIX, Linux, etc., and applications such as Adobe Photoshop®, Adobe Lightroom®, Apple iPhoto®, etc., without the use of these specific details. Also, some of the examples may be executed on a GPU or CPU of a computer system depending on the computing resources available on the computer system or alternatively on any electronic device that is able to view images. The examples have discussed application of the various image editing functions to images, but each of the above examples are extensible to apply to other forms of visual media such as video. Thus, one of ordinary skill in the art would understand that the invention is not to be limited by the foregoing illustrative details, but rather is to be defined by the appended claims.

Metadata:
Filing Date: 20080528
Publication Date: 20121211
Grant Date: 20121211
Priority Date: 20080528
Inventors: PETTIGREW DANIEL
CANDELA DAN
Assignee: APPLE INC
CPC Classifications: [{"code": "G06T7/13", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T2207/10016", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/30201", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/10016", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/20096", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/20096", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T7/13", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/30201", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2200/24", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T11/20", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T2200/24", "inventive": false, "first": false, "tree": "[]"}]
Family ID: 41381419