Patent Publication Number: US-10318102-B2

Title: 3D model generation from 2D images

Description:
BACKGROUND 
     In a digital medium environment, three-dimensional models may be used to support a variety of functionality. Examples of this functionality include use in games, support different views of objects being modeled in successive images to generate video, employed by a three-dimensional printer to print a physical representation of the model, and so forth. 
     Conventional techniques used to generate three-dimensional models, however, often required specialized knowledge on the part of a user to interact with a complicated user interface. Thus, these conventional techniques are often overwhelming to users, time consuming, and can also require significant space to support a user interface thereby limiting these techniques to use of feature-rich computing devices. In one conventional example, a user interacts with a user interface to generate simple shapes, such as a cube, sphere, and so on. The user then interacts with the shape to move, rotate, resize, extract, connect, and/or split the simple shapes to make objects. Although this approach is flexible, it involves a significant amount of time to perform through use of complicated user interfaces. 
     In another conventional example, sculpting is performed in which a user interacts with a mesh in a user interface in a manner similar to shaping physical clay. While this is somewhat intuitive, in actual practice this technique requires the user to have advanced sculpting skills and mastery of complicated tools. In yet another conventional example, a three-dimensional scan is performed by a 3D scanning machine. Thus, in this example a user is required to already have an example of the object being modeled, requires expensive machinery or otherwise a low quality model is formed with noise and errors. Thus, conventional 3D model generation techniques are unintuitive, expensive, and require extensive knowledge and therefore are limited to use in a limited number of instances. 
     SUMMARY 
     Techniques and systems are described to generate a three-dimensional model from two-dimensional images. In one example, a digital medium environment is configured to generate a three-dimensional model of an object from a plurality of images having different two-dimensional views of the object. A plurality of inputs are received, formed through user interaction with a user interface. Each of the plurality of inputs define a respective user-specified point on the object in a respective image of the plurality of images. A plurality of estimated points on the object are generated by a computing device. Each of the plurality of estimated points corresponds to a respective user-specified point and is defined for a different image of the plurality of images than the respective image defining the respective user-specified point. The plurality of estimated points is displayed in the user interface by the computing device. A mesh is generated of the three-dimensional model of the object by the computing device by mapping respective ones of the user-specified points to respective ones of the estimated points in the plurality of images. 
     This Summary introduces a selection of concepts in a simplified form that are further described below in the Detailed Description. As such, this Summary is not intended to identify essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items. Entities represented in the figures may be indicative of one or more entities and thus reference may be made interchangeably to single or plural forms of the entities in the discussion. 
         FIG. 1  is an illustration of an environment in an example implementation that is operable to employ model generation techniques described herein. 
         FIG. 2  depicts a system showing operation of a three-dimensional model generation system of  FIG. 1  in greater detail. 
         FIG. 3  is a flow diagram depicting a procedure in an example implementation in which a three-dimensional model is generated from two-dimensional images. 
         FIG. 4  depicts a user interface usable to specify a location and size of an object within images that is to be modeled. 
         FIG. 5  depicts a system in an example implementation in which a user-specified point in one image is used to estimate an estimated point in another image as part of generation of a three-dimensional model from two-dimensional images. 
         FIG. 6  depicts an example of simultaneous display of two-dimensional images, points, and real time display of a three-dimensional model that is generated from the points on the two-dimensional images. 
         FIGS. 7, 8, and 9  depict other examples of simultaneous display of two-dimensional images and real time display of a three-dimensional model for a variety of different types of objects. 
         FIG. 10  depicts a user interface usable to initiate functionality to mirror points such that a model is generated that includes portions of an object that are not viewable in images used to create the model. 
         FIG. 11  illustrates an example system including various components of an example device that can be implemented as any type of computing device as described and/or utilize with reference to  FIGS. 1-10  to implement embodiments of the techniques described herein. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     Conventional techniques used to generate three-dimensional models often involve interaction with complicated user interfaces and require advanced skills on the part of a user in order to generate the model. Consequently, these techniques are typically not employed by users lacking these skills, even though these users may have advanced skills in other areas of image generation. 
     Techniques and systems are described to generate three-dimensional models from two-dimensional images. In this way, users that are capable of creating two-dimensional images of an object may use these images to generate the three-dimensional model in an intuitive and efficient manner without having advanced knowledge of specialized tools. For example, a user first provides a plurality of images of an object. The user then specifies which views of the object are captured by respective ones of the images, e.g., front, back, side, top, bottom, and so forth. 
     The images are then displayed in a user interface such that a user may indicate correspondence of landmarks between the images that are to be used as a basis to generate a three-dimensional model through the landmarks and knowledge of the different views that are captured by respective images. For instance, a user may first indicate a point at an end of a nose of a face captured in a first one of the images (e.g., a front view), which is referred to as a user-specified point. The computing device then estimates an estimated point in a second one of the images, e.g., a side view. This estimation may be performed automatically and without user intervention in response to receipt of the user-specified point, upon receipt of a user input to initiate the estimation, and so forth. The estimated point is configured to be moved by the user, e.g., to correct an error in the estimate such that the end of the nose of the user is accurately reflected by the points. This process is then repeated by interacting with the different images to indicate correspondence between points which is aided by an initial estimation, thereby improving efficiency and accuracy. 
     The correspondence of the points and the knowledge of the different views in the images is then used to generate a mesh that maps the points to each other in three-dimensional space. A texture is then taken from the images and overlaid over the mesh to form a three-dimensional model of the object in the images. In this way, the three-dimensional model may be generated in an efficient, intuitive, and accurate manner from two-dimensional images that are typically readily available from users as part of content creation. Further discussion of these and other examples is included in the following sections. 
     In the following discussion, an example environment is first described that may employ the techniques described herein. Example procedures are then described which may be performed in the example environment as well as other environments. Consequently, performance of the example procedures is not limited to the example environment and the example environment is not limited to performance of the example procedures. 
     Example Environment 
       FIG. 1  is an illustration of an environment  100  in an example implementation that is operable to employ model generation techniques described herein. The illustrated environment  100  includes a computing device  102 , which may be configured in a variety of ways. 
     The computing device  102 , for instance, may be configured as a desktop computer, a laptop computer, a mobile device (e.g., assuming a handheld configuration such as a tablet or mobile phone as illustrated), and so forth. Thus, the computing device  102  may range from full resource devices with substantial memory and processor resources (e.g., personal computers, game consoles) to a low-resource device with limited memory and/or processing resources (e.g., mobile devices). Additionally, although a single computing device  102  is shown, the computing device  102  may be representative of a plurality of different devices, such as multiple servers utilized by a business to perform operations “over the cloud” as further described in relation to  FIG. 11 . 
     The computing device  102  is illustrated as including an image processing module  104 . The image processing module  104  is representative of functionality to transform images  106 ,  108  to create new or modified versions of the images  108 ,  108 , examples of which are illustrated as maintained in storage  110  of the computing device  102 . 
     Examples of image processing include removal of noise from an image, object removal, hole filling, application of filters (e.g., Gaussian blur), object recognition, semantic tagging, and other techniques that alter pixels included in the images  106 ,  108 , associate data with the images  106 ,  108  to support other functionality (e.g., image search), and so forth. Although functionality of the image processing module  104  is illustrated as being implemented solely on the computing device  102 , this functionality may also be further divided, such as provided in whole or in part “in the cloud” as further described in relation to  FIG. 11 . 
     An example of image processing functionality is illustrated as a three-dimensional (3D) model generation system  112 . The 3D model generation system  112  is representative of functionality usable to generate a 3D model  114  from the images  106 ,  108 . The images  106 ,  108 , for instance, may be two-dimensional images of an object capturing different views of the object, an example of which is output in a user interface  116  of the 3D model generation system  112 . 
     As illustrated in the user interface  116  of the system, for instance, a first image  118  captures a right-side view of a person&#39;s head, a second image  120  captures a front view of the person&#39;s head, a third image  122  captured a left-side view of the user&#39;s head, and a fourth image  124  captures a back view of the user&#39;s head. From these images  118 - 124 , the user may select points in one of the images for landmarks in one of the images  118 - 124 , e.g., tip of nose, corners of the user&#39;s eyes, hairline, etc. 
     These user-specified points are then used by the 3D model generation system  112  to estimate corresponding estimated points in other ones of the images  118 - 124 , which are moveable to correct any errors in estimation. In this way, the 3D model generation system  112  learns which points in the images  118 - 124  correspond to each other, which is used to generate a mesh from the points and from this mesh, the 3D model  114  of the object (e.g., the user&#39;s head) included in the image  116 . In one or more implementations, a rendered output  126  of the 3D model  114  is displayed in real time by the 3D model generation system  112  such that a user may view a result of indication of these correspondences and thus may decide when a 3D model  114  having sufficient detail has been achieved in a non-modal manner, e.g., that would involve navigation away from the user interface  116 . Further discussion of an example of generation of the 3D model  114  is described in the following and shown in corresponding figures. 
       FIG. 2  depicts a system  200  showing operation of the 3D model generation system  106  of  FIG. 1  in greater detail.  FIG. 3  depicts a procedure  300  in an example implementation in which a three-dimensional model is generated from two-dimensional images. In the following, the discussion refers to both the system  200  and the procedure  300  as well as examples of the user interfaces in the figures that follow. 
     Aspects of each the procedure may be implemented in hardware, firmware, or software, or a combination thereof. The procedure is shown as a set of blocks that specify operations performed by one or more devices and are not necessarily limited to the orders shown for performing the operations by the respective blocks. 
     The 3D model generation system  106  first obtains a plurality of images  106 ,  108 , to generate the three dimensional model  108 . The techniques described herein are usable to generate the 3D model  114  from two images having different views of an object to be modeled (e.g., through use of mirroring as further described below), although additional images may also be employed as illustrated in  FIG. 1 . 
     To begin with in this example, one or more image identification inputs are received that describe a respective view of the object that is captured by respective ones of the plurality of images (block  302 ). A user, for instance, may interact with the user interface  116  to upload images and provide image identification inputs  202  to also specify a relationship that the images have in relation to each other by specifying which view of the object is captured by the respective images. 
     As shown in  FIG. 1 , a first image  118  captures a right-side view of a person&#39;s head, a second image  120  captures a front view of the person&#39;s head, a third image  122  captured a left-side view of the user&#39;s head, and a fourth image  124  captures a back view of the user&#39;s head. In this way, an image identification module  204  may determine that the first, second, third, and fourth images  118 - 124  capture different views of the object (e.g., the user&#39;s head) along a single plane at ninety degree increments, e.g., a module implementing logic to identify images using hardware such as a processor, memory, fixed or programmable integrated circuit, and so forth. From this, the image identification module  204  may construct a three-dimensional space and a relationship of the first, second, third, and fourth images  118 - 124  to that space. A variety of different views and relationships between views of objects to be modeled may be expressed via the image identification inputs  202 , such as a particular angle between the views, specification of vectors in a three-dimensional space (e.g., by drawing arrows in a three-dimensional sphere in which each corresponds to a receive view captured by an image), use of pre-configured views (e.g., top, bottom, front, back, three-quarter view), and so forth. 
     In order to obtain the accurate point data, the image identification inputs  202  may also specify a location and a size of the object in respective ones of the plurality of images (block  304 ). As shown in an example implementation  400  of  FIG. 4 , for instance, the user interface  116  includes an image  402  having a front view of an object, e.g., a user&#39;s head. The user interface  402  is configured to specify a location and size of the object  402  within the image  402 . In this example, this includes specifying a top and bottom end of the object  404 , a left and right end of the object  406 , and a center of the object  408 . In this way, the image identification module  204  may utilize objects in the images that have different sizes within the images through comparison of these locations and bounding boxes formed from the specified outputs. Thus, the image identification module  204  may dynamically address a greater range of images than was previously possible using conventional techniques that required matching sizes of objects within images in order to generate a 3D model. 
     This information is then provided by the image identification module  204  to the point manager module  206 . The point manager module  206  is representative of functionality usable to indicate correspondence of points in respective images  106 ,  108  in the user interface  116 , e.g., a module implementing logic to indicate identify and indicate corresponds of points using hardware such as a processor, memory, fixed or programmable integrated circuit, and so forth. As part of this, a plurality of inputs is received that are formed through user interaction with a user interface  116 . Each of the plurality of inputs defines a respective user-specified point  208  on the object in a respective image of the plurality of images (block  306 ). 
       FIG. 5 , for instance, depicts a system  500  in an example implementation in which a user-specified point in one image is used to generate an estimated point in another image as part of generation of a three-dimensional model from two-dimensional images. This system  500  is illustrated using first, second, and third stages  502 ,  504 ,  506  of interaction via a user interface  116  with first and second images  508 ,  510 . 
     At the first stage  502 , an input specifying a location of a user-specified point  512  on an object in the second image  510  is received, the input caused through interaction with the user interface  116 . A user, for instance, may use a cursor control device (e.g., a mouse or trackpad), touchscreen functionality of a display device, and so on to note a landmark of a user&#39;s chin as the user-specified point  512 . 
     In response, the point manager module  206  employs a point estimation module  210  implemented at least partially in hardware to generate an estimated point  514  on the object, which may or may not be done automatically and without user intervention. For example, this estimation may be performed automatically and without user intervention responsive to receipt of the user-specified point, performed in response to initiation of functionality by a user, and so forth. The point estimation module  210  is representative of functionality to form estimated points  212  based on the three-dimensional space learned from the image identification module  204 , e.g., through knowledge of where the user-specified point  208  is located on the second image  510  to estimate a corresponding location on the first image  508 . This may be performed in a variety of ways. 
     In one example, the point estimation module  210  uses one or more templates  214  to perform the estimation. The templates  214 , for instance, may act as rough models that are used to estimate the points. The templates  214  may be selected automatically and without user intervention  210 , e.g., through use of object detection functionality to estimate a type of object being modeled and then select from a plurality of preconfigured templates  214  to select which of the templates best corresponds to the object. In another example, the templates  214  are selected by a user through interaction with the user interface  116 , e.g., to select a “face,” “car,” “figurine,” or other object. In another example, the templates  214  could be generated or improved by the current user defined points. In another example, the templates  214  could be generated or improved by the landmark detection algorithm from the input images. 
     Regardless of how the template is selected or even how the estimated point is estimated, the estimated point  514  is then displayed in the user interface  116  on the first image  508  as shown at the second stage  504  of  FIG. 5 . Thus, in this example a user has specified a user-specified point  512  on the second image  510  which causes an automatic display of an estimated point  514  on the first image  508 , both of which are displayed simultaneously such that a user need not flip “back and forth” between user interfaces, although that example may also be utilized. Because the second point  514  is estimated, it may lack some accuracy in indicating a corresponding landmark in the object to be modeled, e.g., the user&#39;s chin. Accordingly, a user may utilize a cursor control device, touchscreen, or other input to move the estimated point  514  to a desired location on the object, an example of which is shown at the third stage  506  of  FIG. 5 . The user-specified points  512  may also be moved and moved again by a user as desired. 
     This process may continue to estimate a plurality of estimated points on the object automatically and without user intervention, with each of the estimated points corresponding to a respective user-specified point for a different image of the plurality of images than the respective image defining the respective user-specified point (block  308 ), which are then displayed in the user interface (block  310 ). Color coding or other indicia may be used to show which estimated point is “newly added” in response to addition of a user-specified point on another one of the images and thus indicate correspondence between points in the images. Further, user-specified points may be added  208  interchangeably to either the first or second images  508 ,  510  with the estimated points  212  being automatically generated for the other one of the first or second images  508 ,  510 . In this way, a user may interact with the images in a dynamic, efficient (either image may receive inputs), and intuitive manner to specify a plurality of points. 
     A mesh of the three-dimensional model of the object is then generated as a mapping of respective ones of the user-specified points to respective ones of the estimated points in the plurality of images (block  312 ). As shown in  FIG. 2 , the user-specified points  208  and the estimated points  212  (whether further moved in the user interface  116  or not) are provided to a mesh generation module  216 , e.g., a module implementing logic to generate a mesh using hardware such as a processor, memory, fixed or programmable integrated circuit, and so forth. The mesh generation module  216  is representative of functionality to compute a mesh  218  in three dimensions that links pairs of the user-specified points and estimated points  212  to other pairs in three-dimensional space through knowledge of a relationship of the images  106 ,  108  to views of the object being modeled, i.e., the formation of the three-dimensional space and the relationship of the images to that space. In this way, the mesh  218  forms a frame of the model to be formed. In one or more implementations, the mesh generation module  216  is configured to normalize locations of pairs of user-specified points and estimated points as part of generation of the mesh, and thus limit potential errors in either one of the points. 
     A texture module  220  is then employed by the 3D model generation system  106  to overlay a texture formed from the images  106 ,  108  over the mesh  218  to form the 3D model  114 , which is then output in the user interface  116 . As in the other example, the texture module  220  may be implemented as a module using hardware such as a processor, memory, fixed or programmable integrated circuit, and so forth. An example  600  of this process is shown in an example implementation of  FIG. 6 . In this example, the user interface  116  includes four two-dimensional images  602 ,  604 ,  606 ,  608  and corresponding points as indicated above. The user interface  116  also includes a real time display  610  of the three dimensional model  114  that is formed as the user-inputs are received to estimate the estimated inputs, and is further updated based on subsequent movement of either ones of the inputs. In this way, through use of the mesh and overlaid texture a user may view a display of the model as inputs are provided in order to modify the inputs as desired as well as to determine when a desired level of detail is achieved. 
     A variety of different objects may be modeled in three dimensions from two-dimensional images.  FIG. 8  depicts an example  800  of a user&#39;s head taken from different views captured in four images.  FIG. 9  depicts an example  1100  of a three-dimensional model of a cheeseburger modeled from images. 
     Three-dimensional models formed using the techniques described herein may also be combined to form larger models. For example, a pencil drawing of a cartoon character&#39;s head may be used to form a three-dimensional model of the head. This head may then be combined with other three-dimensional models that are also made by this system to form an entire body of the cartoon character. 
     The 3D model generation system  106  may also include functionality to automatically generate points for points of the model that are not viewable in the images. For example, two different views from a side of an object (e.g., a face) as shown in an example  1000  of  FIG. 10  may be “mirrored” by the 3D model generation system  106  in order to complete a model. Illustrated examples include of mirror functionality include left-to-right, right-to-left, and front-to-back which may be selected by a user to automatically “fill in” other portions of the 3D model  114  as desired even if those portions are not viewable in the images  106 ,  108 . 
     Example System and Device 
       FIG. 11  illustrates an example system generally at  1100  that includes an example computing device  1102  that is representative of one or more computing systems and/or devices that may implement the various techniques described herein. This is illustrated through inclusion of the 3D model generation system  106 . The computing device  1102  may be, for example, a server of a service provider, a device associated with a client (e.g., a client device), an on-chip system, and/or any other suitable computing device or computing system. 
     The example computing device  1102  as illustrated includes a processing system  1104 , one or more computer-readable media  1106 , and one or more I/O interface  1108  that are communicatively coupled, one to another. Although not shown, the computing device  1102  may further include a system bus or other data and command transfer system that couples the various components, one to another. A system bus can include any one or combination of different bus structures, such as a memory bus or memory controller, a peripheral bus, a universal serial bus, and/or a processor or local bus that utilizes any of a variety of bus architectures. A variety of other examples are also contemplated, such as control and data lines. 
     The processing system  1104  is representative of functionality to perform one or more operations using hardware. Accordingly, the processing system  1104  is illustrated as including hardware element  1110  that may be configured as processors, functional blocks, and so forth. This may include implementation in hardware as an application specific integrated circuit or other logic device formed using one or more semiconductors. The hardware elements  1110  are not limited by the materials from which they are formed or the processing mechanisms employed therein. For example, processors may be comprised of semiconductor(s) and/or transistors (e.g., electronic integrated circuits (ICs)). In such a context, processor-executable instructions may be electronically-executable instructions. 
     The computer-readable storage media  1106  is illustrated as including memory/storage  1112 . The memory/storage  1112  represents memory/storage capacity associated with one or more computer-readable media. The memory/storage component  1112  may include volatile media (such as random access memory (RAM)) and/or nonvolatile media (such as read only memory (ROM), Flash memory, optical disks, magnetic disks, and so forth). The memory/storage component  1112  may include fixed media (e.g., RAM, ROM, a fixed hard drive, and so on) as well as removable media (e.g., Flash memory, a removable hard drive, an optical disc, and so forth). The computer-readable media  1106  may be configured in a variety of other ways as further described below. 
     Input/output interface(s)  1108  are representative of functionality to allow a user to enter commands and information to computing device  1102 , and also allow information to be presented to the user and/or other components or devices using various input/output devices. Examples of input devices include a keyboard, a cursor control device (e.g., a mouse), a microphone, a scanner, touch functionality (e.g., capacitive or other sensors that are configured to detect physical touch), a camera (e.g., which may employ visible or non-visible wavelengths such as infrared frequencies to recognize movement as gestures that do not involve touch), and so forth. Examples of output devices include a display device (e.g., a monitor or projector), speakers, a printer, a network card, tactile-response device, and so forth. Thus, the computing device  1102  may be configured in a variety of ways as further described below to support user interaction. 
     Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, such modules include routines, programs, objects, elements, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. The terms “module,” “functionality,” and “component” as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors. 
     An implementation of the described modules and techniques may be stored on or transmitted across some form of computer-readable media. The computer-readable media may include a variety of media that may be accessed by the computing device  1102 . By way of example, and not limitation, computer-readable media may include “computer-readable storage media” and “computer-readable signal media.” 
     “Computer-readable storage media” may refer to media and/or devices that enable persistent and/or non-transitory storage of information in contrast to mere signal transmission, carrier waves, or signals per se. Thus, computer-readable storage media refers to non-signal bearing media. The computer-readable storage media includes hardware such as volatile and non-volatile, removable and non-removable media and/or storage devices implemented in a method or technology suitable for storage of information such as computer readable instructions, data structures, program modules, logic elements/circuits, or other data. Examples of computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, hard disks, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other storage device, tangible media, or article of manufacture suitable to store the desired information and which may be accessed by a computer. 
     “Computer-readable signal media” may refer to a signal-bearing medium that is configured to transmit instructions to the hardware of the computing device  1102 , such as via a network. Signal media typically may embody computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as carrier waves, data signals, or other transport mechanism. Signal media also include any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. 
     As previously described, hardware elements  1110  and computer-readable media  1106  are representative of modules, programmable device logic and/or fixed device logic implemented in a hardware form that may be employed in some embodiments to implement at least some aspects of the techniques described herein, such as to perform one or more instructions. Hardware may include components of an integrated circuit or on-chip system, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a complex programmable logic device (CPLD), and other implementations in silicon or other hardware. In this context, hardware may operate as a processing device that performs program tasks defined by instructions and/or logic embodied by the hardware as well as a hardware utilized to store instructions for execution, e.g., the computer-readable storage media described previously. 
     Combinations of the foregoing may also be employed to implement various techniques described herein. Accordingly, software, hardware, or executable modules may be implemented as one or more instructions and/or logic embodied on some form of computer-readable storage media and/or by one or more hardware elements  1110 . The computing device  1102  may be configured to implement particular instructions and/or functions corresponding to the software and/or hardware modules. Accordingly, implementation of a module that is executable by the computing device  1102  as software may be achieved at least partially in hardware, e.g., through use of computer-readable storage media and/or hardware elements  1110  of the processing system  1104 . The instructions and/or functions may be executable/operable by one or more articles of manufacture (for example, one or more computing devices  1102  and/or processing systems  1104 ) to implement techniques, modules, and examples described herein. 
     The techniques described herein may be supported by various configurations of the computing device  1102  and are not limited to the specific examples of the techniques described herein. This functionality may also be implemented all or in part through use of a distributed system, such as over a “cloud”  1114  via a platform  1116  as described below. 
     The cloud  1114  includes and/or is representative of a platform  1116  for resources  1118 . The platform  1116  abstracts underlying functionality of hardware (e.g., servers) and software resources of the cloud  1114 . The resources  1118  may include applications and/or data that can be utilized while computer processing is executed on servers that are remote from the computing device  1102 . Resources  1118  can also include services provided over the Internet and/or through a subscriber network, such as a cellular or Wi-Fi network. 
     The platform  1116  may abstract resources and functions to connect the computing device  1102  with other computing devices. The platform  1116  may also serve to abstract scaling of resources to provide a corresponding level of scale to encountered demand for the resources  1118  that are implemented via the platform  1116 . Accordingly, in an interconnected device embodiment, implementation of functionality described herein may be distributed throughout the system  1100 . For example, the functionality may be implemented in part on the computing device  1102  as well as via the platform  1116  that abstracts the functionality of the cloud  1114 . 
     CONCLUSION 
     Although the invention has been described in language specific to structural features and/or methodological acts, it is to be understood that the invention defined in the appended claims is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as example forms of implementing the claimed invention.