Patent Publication Number: US-2022239829-A1

Title: Automatic guided image capturing and presentation of images

Description:
PRIORITY 
     This application is a continuation of U.S. patent application Ser. No. 16/985,670 by Piramuthu et al., entitled “Automatic Guided Capturing and Presentation of Images,” filed Aug. 5, 2020; which is a continuation of U.S. patent application Ser. No. 16/363,158 by Piramuthu et al., entitled “Automatic Guided Capturing and Presentation of Images,” filed Mar. 25, 2019, now U.S. Pat. No. 10,771,685, issued Sep. 8, 2020; which is a continuation of U.S. patent application Ser. No. 15/361,973 by Piramuthu et al., entitled “Automatic Guided Capturing and Presentation of Images,” filed Nov. 28, 2016, now U.S. Pat. No. 10,270,965, issued Apr. 23, 2019; which claims the priority benefit of U.S. Provisional Patent Application No. 62/263,433, filed Dec. 4, 2015; each of which is assigned to the assignee hereof and hereby incorporated by reference herein in its entirety. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings. 
       FIG. 1  is a block diagram illustrating functional components of the guided image capture in presentation system, according to some example embodiments. 
       FIG. 2  is a flowchart illustrating operations of a device in performing a method for automatic-guided image capturing and presentation, according to some example embodiments. 
       FIG. 3  is a flowchart illustrating operations of a device in performing a method for automatic-guided image capturing and presentation, according to some example embodiments. 
       FIG. 4  is a flowchart illustrating operations of a device in performing a method for automatic-guided image capturing and presentation, according to some example embodiments. 
       FIG. 5  is a diagram illustrating a method for automatic-guided capturing presentation of images, according to some example embodiments. 
       FIG. 6  is a network diagram illustrating a network environment suitable for facilitating automatic-guided image capturing and presentation, according to some example embodiments. 
       FIG. 7  is a block diagram illustrating components of a machine, according to some example embodiments, able to read instructions from a machine-readable medium and perform any one or more of the methodologies discussed herein. 
    
    
     DETAILED DESCRIPTION 
     Reference will now be made in detail to specific example embodiments for carrying out the inventive subject matter. Examples of these specific embodiments are illustrated in the accompanying drawings. It will be understood that these examples are not intended to limit the scope of the claims to the illustrated embodiments. On the contrary, they are intended to cover alternatives, modifications, and equivalents as may be included within the scope of the disclosure. In the following description, specific details are set forth in order to provide a thorough understanding of the subject matter. Embodiments may be practiced without some or all of these specific details. 
     Example embodiments described herein facilitate automatic-guided image capturing and presentation of item listings (e.g., item data records). Mobile device logic may execute the automatic-guided image capturing and presentation, independent of a network connection, allowing for enhanced creation of item listings and increased efficiency of network resources. Embodiments facilitate image guidance, automatic background removal, automatic completion of item listing forms, and online store management. 
     In example embodiments, a user of a mobile device (e.g., a seller) may use the mobile device to automatically create an item listing. A guided image capture and presentation system on a mobile device guides the user in capturing an image of an item with the camera of the mobile device, detects and automatically removes the background portion of the image, keeping only the portion of the image comprising the item of interest, and allows the user to further modify the image by manually altering the background portion of the image. 
     In additional example embodiments, the guided image capture presentation system facilitates automatic completion of item listing forms by inferring information from the captured image (e.g., category recognition, characteristic recognition such as color, pattern, and style). Automatic completion of item listing forms may also include inferring from a captured image of the item certain keywords associated with the item and title predictions. Multiple images may also be used by the system in automatic completion of item listings. Automatic completion of item listings may also include displaying to the user a list of items that potentially have similar title and/or price, assisting the user in constructing the title of the item listing and the price of the item. The guided image capture and presentation system may also allow the user to showcase the item from the item listing in an augmented reality environment, including a presentation layer for presenting the item on a virtual shelf or clothing rack to be viewed by potential buyers visiting the user&#39;s online store. Based on information inferred from the captured image, the guided image capture and presentation system may automatically choose a presentation layer in accordance with the category and/or characteristics of item (e.g., a virtual shelf to display shoes, a virtual rack to display clothing). The user of the mobile device may also manually choose a presentation layer for the item. Furthermore, the augmented reality environment may allow for customization of the presentation layer by the user (e.g., allowing the user to choose a style of virtual shelf, clothing rack, or wallpaper based on the user&#39;s preference or target audience). 
       FIG. 1  is a block diagram illustrating functional components of a guided image capture and presentation system (GIPS)  124 , according to some example embodiments. The  124  facilitates automatic-guided image capturing and presentation of item listings, as described above. Various functional modules are shown for the GIPS  124 , which is provided as part of a network system, as described in more detail with respect to  FIG. 6 . As is understood by skilled artisans in the relevant computer and Internet-related arts, each component (e.g., a module or engine) illustrated in  FIG. 1  may be implemented using hardware (e.g., a processor of a machine) or a combination of logic (e.g., executable software instructions) and hardware (e.g., memory and processor of a machine) for executing the logic. 
     The GIPS  124  is shown as including an image guidance module  104 , a background removal module  106 , a presentation module  108 , a listing module  210 , and database  112 , all configured to communicate with each other (e.g., via a bus, shared memory, a switch, or application programming interfaces (APIs)). The various modules of the GIPS  124  may, furthermore, access the database  122  via database servers, and each of the various modules of the GIPS  124  may be in communication with one or more third party applications, for example a social network platform. While the modules of  FIG. 1  are discussed in the singular sense, it will be appreciated that in other embodiments multiple modules may be employed. 
     The image guidance module  104  assists a user of a mobile device in capturing an image of an item for an item listing. For example, the image guidance module  104  guides the user to position the mobile device in an optimal direction from the item, detects the item through a camera of the mobile device and captures one or more image frames of the item. The image guidance module  104  estimates a background portion of the image and a foreground portion of the image for each captured image frame. For example, estimation of the background and foreground portions of the image may be accomplished by identifying a first region and a second region and determining that at least one area within the first region comprises a grouping of pixels that are different (e.g., different in color) from a grouping of pixels in the second region, or vice versa. In identifying the areas of differing groups of pixels, the image guidance module  104  estimates that the first region or second region comprises the item of interest. Additionally to estimating the location (e.g., location within the first region or second region) of the item of interest, the image guidance module  104  communicates instructions to the user of the mobile device for capturing an image of the item. In some embodiments, a user interface of the mobile device displays a viewfinder (e.g., a shape superimposed upon the user interface of the mobile device) for guiding the positioning of the item for image capturing. 
     As the user holds the mobile device at a distance from the object and moves the mobile device, the image guidance module  104  may display human-readable instructions (e.g., textual instructions) within the user interface instructing the user to move the mobile device such that the item will become completely included within a boundary of the viewfinder. For example, an inner boundary of the viewfinder may correspond to the aforementioned first region (e.g., foreground) and an outer boundary may correspond to the aforementioned second region (e.g., background). In other embodiments, instead of instructing the user to move the mobile device, the image guidance module  104  displays instructions instructing the user to increase or decrease lighting or to relocate objects, for example removing objects from the background portion of the image or separating two objects within the foreground portion of the image (e.g., the item of interest being proximate to another object). In some embodiments, guided image capturing processes each captured image frame and instructs the user of the mobile device in real-time. A captured image may be stored on the mobile device, for example, within a database (e.g., database  112 ) or a location within memory and may also be stored on a remote network-based system within a database or memory, for example, a network-based system similar to that described with respect to  FIG. 6 . 
     The background removal module  106  automatically removes the background portion of an image, leaving only the portion of the image comprising the item of interest. Similar to the guided image capturing described above, background removal may be performed in real-time on the mobile device. Additionally, the background module  106  may also automatically remove the background portion of an image frame or a group of image frames. In some embodiments, the background removal module  106  accesses a captured image frame of an item and detects a boundary of the item within the image, separating the item from the image background. In detecting the boundary of the item, the background removal module  106  makes an initial estimate of a binary mask, the binary mask comprising a first region and a second region of the image, for example the aforementioned first region and second region with respect to the image guidance module  104 . For both the first region and the second region, the background removal module  106  computes a color histogram and computes a probability per pixel using the color histogram. In computing the probability per pixel, the background removal module  106  creates a probability map. The background removal module  106  may smooth the probability map by using domain transform geodesic filtering and obtaining a binary mask using a threshold selection method. The threshold selection method assigns pixels having a value (e.g., a RGB value) above a determined threshold value to one binary value and pixels having a value below the determined threshold value to another binary value. 
     The background removal module  106  may also perform manual editing of an image by receiving a user input on the mobile device. In some embodiments, a binary mask of an image is created and the user of the mobile device manually edits the binary mask by creating a scribble, further defining the outline of the item of interest and further separating the background portion of the image from the item of interest. A scribble is defined as a certain type of user input created by the user bringing an object within a predefined proximity to the user interface of the mobile device (e.g., touching the screen of the mobile device with a finger), causing a portion of the user interface (e.g., representing the image) to be highlighted or otherwise differentiated from the remaining portion of the image. After receiving a user input scribble, the background removal module  106  uses domain transform geodesic filtering to smooth the scribble. The background removal module  106  scales the smoothed scribble to a value above or below a threshold value. For example, the smoothed scribble may be scaled to a value above or below a determined threshold value corresponding to the threshold value used in creating the binary mask using the threshold selection method described above with respect to automatic background removal. The scaled scribble is added or subtracted from the probability map created during automatic background removal, depending on the value of the scribble being above or below the determined threshold value, obtaining a binary mask from the probability map. The background removal module  106  may also cause the scribble to shrink or grow in size covering portions of the background to be removed. Additionally, the background removal module  106  may also allow the scribble to snap to a detected boundary of the item of interest within the image. In some embodiments the binary mask obtained after the scaling of the scribble is unique from the binary mask created during automatic background removal. 
     The listing module  110  automatically fills item listing forms to create an item listing. For example, the automatic item form listing is accomplished by inferring information from an image of an item (e.g., characteristics of an item including color, style, category, brand name). In some embodiments, information regarding the item is inferred from the image or image frames captured during the guided image capturing described above with respect to the image guidance module  104 . In automatically completing item listing forms, the listing module  110  also includes displaying to the user a list of items that potentially have similar title and/or price, assisting the user in constructing the title of the item listing and the price of the item. When an item listing form is completed, the item listing is created, including the image of the item, information inferred from the image regarding the item, and any additional input information received from the user of the mobile device. The guided image capture and presentation system  124  uses a completed item listing to display the item within an augmented reality environment of a seller&#39;s online store. 
     The presentation module  108  causes items (e.g., a captured image of the item as described above) to be presented in an augmented reality environment including a presentation layer. For example, the presentation module  108  may present an item on a virtual shelf or clothing rack to be viewed by potential buyers visiting the user&#39;s online store. In some embodiments, based on information inferred from the image of the item, the listing module  110  automatically chooses a presentation layer in accordance with the category and/or characteristics of item (e.g., a virtual shoe rack to display shoes, a virtual clothing rack to display clothing, or a virtual shelf). The user of the mobile device may also manually choose a presentation layer for the item. Furthermore, the augmented reality environment allows for customization of the presentation layer by the user (e.g., allowing the user to choose a style of virtual shelf, clothing rack, or wallpaper based on the user&#39;s preference or target audience). Enhancing the augmented reality experience for a viewer of an online store (e.g., a visiting buyer or the seller), the presentation module  108  allows for the addition of a virtual sale indicator, for example, a “red sale tag.” The virtual sale indicator allows a seller to draw attention from viewers of the online store toward a specific item, for example, indicating that an item within the seller&#39;s online store is being offered at a special Price. In some embodiments, when viewing the presentation layer of a seller&#39;s online store, a viewer may select a displayed item (e.g., by touching an item on a virtual shelf or clothing rack displayed on the user interface of the mobile device) and upon selecting the item the presentation module  108  links the viewer to a page displaying detailed information regarding the item. 
       FIG. 2  is a flowchart illustrating operations of a device in performing a method  200  for automatic guided capturing and presentation of images, according to some example embodiments. The method  200  includes capturing of an item image  202 , removing automatically the image background  204 , performing manual mask editing  206 , generating an item listing using inferred image information  208 , and presenting an item listing in an augmented reality environment  210 . The method  200  may be embodied in computer-readable instructions for execution by one or more processors such that the steps of the method  200  may be performed in part or in whole by functional components (e.g., modules) of the GIPS  124 ; accordingly, the method  200  is described below by way of example with reference thereto. However, it shall be appreciated that the method  200  may be deployed on various other hardware configurations and is not intended to be limited to the functional components of the GIPS  124 . 
     In operation  202 , the GIPS  124  assists a user of a mobile device in capturing an image of an item for an item listing. For example, the GIPS  124  guides the user to position the mobile device in an optimal direction from the item, detects the item through a camera of the mobile device and captures one or more image frames of the item. The GIPS  124  estimates a background portion of the image and a foreground portion of the image for each captured image frame, for example, by identifying a first region and a second region and determining that at least one area within the first region comprises a grouping of pixels that are different (e.g., different in color) from a grouping of pixels in the second region, or vice versa. The GIPS  124  estimates that the first region or second region comprises the item of interest. Additionally, the GIPS  124  communicates instructions to the user of the mobile device for capturing an image of the item. As the user holds the mobile device, the GIPS  124  displays human-readable instructions (e.g., textual instructions) within the user interface instructing the user to move the mobile device such that the item will become completely included within a boundary of the viewfinder, to increase or decrease lighting or to relocate objects, for example removing objects from the background portion of the image or separating two objects within the foreground portion of the image (e.g., the item of interest being proximate to another object). When the GIPS  124  detects that the item is completely included within the boundary of the viewfinder, an image is captured. 
     At operation  204 , the GIPS  124  automatically removes the image background. In automatically removing the image background, the GIPS  124  leaves only the portion of the image comprising the item of interest. As an example of the sub-operations involved in the operation  204 ,  FIG. 3  is a flowchart illustrating operations of a device in performing a method for automatic-guided image capturing and presentation, according to some example embodiments. As shown, the operation of automatically removing the image background includes guessing of a binary mask  302 , computing a color histogram  304 , computing a probability map  306 , smoothing of the probability map  308 , and obtaining a binary mask  310 . In operation  302 , the GIPS  124  makes an initial guess of a binary mask. The GIPS  124  accesses a captured image of the item and detects a boundary of the item within the image, separating the item from the image background. In detecting the boundary of the item, the GIPS  124  makes an initial estimate of a binary mask, the binary mask comprising a first region and a second region of the image. In operation  304 , the GIPS  124  computes a color histogram for both the first region and the second region of the image. In operation  306 , the GIPS  124  computes a probability map. The GIPS  124  computes a probability per pixel using the color histogram. In computing the probability per pixel, the GIPS  124  creates a probability map. In operation  308 , the GIPS  124  smooths the probability map by using domain transform geodesic filtering. In performing domain transform geodesic filtering, the GIPS  124  assigns pixels having a value (e.g., a RGB value) above a determined threshold value to one binary value and pixels having a value below the determined threshold value to another binary value. In assigning pixels a value above or below the determined threshold value, in operation  310 , the GIPS  124  obtains a binary mask. 
     Returning to  FIG. 2 , at operation  206 , the GIPS  124  performs manual mask editing. As an example of the sub-operations involved in the operation  206 ,  FIG. 4  is a flowchart illustrating operations of a device in performing a method for automatic-guided image capturing and presentation, according to some example embodiments. 
     In operation  402 , the GIPS  124  access a probability map, for example, probability map created during automatic image background removal). In operation  404 , the GIPS  124  receives a user input on the mobile device in the form of a scribble. Using the scribble, the GIPS  124  further defines the outline of the item of interest and further separates the background portion of the image from the item of interest. In operation  406 , after receiving a user input scribble, the GIPS  124  uses domain transform geodesic filtering to smooth the scribble. In operation  408 , the GIPS  124  scales the smoothed scribble to a value above or below the threshold value. For example, the smoothed scribble may be scaled to a value above or below a determined threshold value corresponding to the threshold value used in creating the binary mask using the threshold selection method. In operation  410 , the GIPS  124  differentiates the scaled scribble from the probability map by adding or subtracting the scaled scribble value from the probability map created during automatic background removal, depending on the value of the scribble being above or below the determined threshold value. In differentiating the scaled scribble from the probability map, in operation  412 , the GIPS  124  obtains a binary mask from the probability map. The GIPS  124  may cause the scribble to shrink or grow in size covering portions of the background to be removed. Additionally, the GIPS  124  may also cause the scribble to snap to a detected boundary of the item of interest within the image. 
     Turning back to  FIG. 2 , in operation  208 , the GIPS  124  generates an item listing using inferred image information. The GIPS  124  automatically fills an item listing form to create an item listing, inferring information from an image of an item (e.g., characteristics of an item including color, style, category, brand name). The GIPS  124  infers information regarding the item from the image or image frames captured during the guided image capturing described above with respect to operation  202 . In automatically completing item listing forms, the GIPS  124  also includes displaying to the user a list of items that potentially have similar title and/or price, assisting the user in constructing the title of the item listing and the price of the item. The GIPS  124  completes the item listing form, creating the item listing, including the image of the item, information inferred from the image regarding the item, and any additional input information received from the user of the mobile device. The GIPS  124  uses a completed item listing to display the item within an augmented reality environment of a seller&#39;s online store. 
     In operation  210 , the GIPS  124  presents the item listing in an augmented reality environment. The GIPS  124  showcases the item (e.g., a captured image of the item as described above) from the item listing in an augmented reality environment including a presentation layer, for example, presenting the item on a virtual shelf or clothing rack to be viewed by potential buyers visiting the user&#39;s online store. In some embodiments, based on information inferred from the image of the item, the GIPS  124  automatically chooses a presentation layer in accordance with the category and/or characteristics of item (e.g., a virtual shoe rack to display shoes, a virtual clothing rack to display clothing, or a virtual shelf). Enhancing the augmented reality experience for a viewer of an online store (e.g., a visiting buyer or the seller), the GIPS  124  allows for the addition of a virtual sale indicator, allowing a seller to draw attention from viewers of the online store toward a specific item. The GIPS  124  allows a viewer to select a displayed item (e.g., by touching an item on a virtual shelf or clothing rack displayed on the user interface of the mobile device) and upon selecting the item the GIPS  124  links the viewer to a page displaying detailed information regarding the item. 
       FIG. 5  is a diagram illustrating a method of automatic guided capturing presentation of images, according to some example embodiments. In  502 , the GIPS  124  assists a user of a mobile device in capturing an image of an item for an item listing. As shown in  FIG. 5 , the GIPS  124  guides the user in positioning the mobile device in an optimal direction from the item. The GIPS  124  estimates a background portion of the image and a foreground portion of the image for each captured image frame, for example, by identifying a first region and a second region and determining that at least one area within the first region comprises a grouping of pixels that are different (e.g., different in color) from a grouping of pixels in the second region, or vice versa. The GIPS  124  estimates that the first region or second region comprises the item of interest. As shown in  FIG. 5 , for example, the GIPS  124  indicates the estimated first and second regions of the image to the user by displaying an outline of the item of interest. Additionally, the GIPS  124  communicates instructions to the user of the mobile device for capturing an image of the item. As the user holds the mobile device, the GIPS  124  displays instructions upon the user interface instructing the user to move the mobile device such that the item will become completely included within a boundary of the viewfinder, to increase or decrease lighting or to relocate objects. When the GIPS  124  detects that the item is completely included within the boundary of the viewfinder, an image is captured. 
     In  504 , the GIPS  124  automatically removes the image background. As shown in  FIG. 5 , the GIPS  124  leaves only the portion of the image comprising the item of interest and displays a viewfinder showing the boundaries of the item of interest, which may have been used during guided image capturing. The GIPS  124  makes an initial guess of a binary mask and detects a boundary of the item within the image, separating the item from the image background. The GIPS  124  computes a color histogram a probability map, computing a probability per pixel using the color histogram. The GIPS  124  smooths the probability map by using domain transform geodesic filtering. In producing the image in  504 , the GIPS  124  may also perform manual mask editing, receiving a user input in the form of a scribble and further defining the outline of the item of interest and further separating the background portion of the image from the item of interest. 
     In  506 , the GIPS  124  generates an item listing using inferred image information. The GIPS  124  automatically fills an item listing form to create an item listing, inferring information from an image of an item (e.g., characteristics of an item including color, style, category, brand name). The GIPS  124  infers information regarding the item from the image or image frames captured during the guided image capturing described above with respect to operation  202 . As shown in  510  of  FIG. 5 , the GIPS  124  also includes displaying a suggested title or category to the user or a list of items that potentially have similar title and/or price, assisting the user in constructing the title of the item listing and the price of the item. The GIPS  124  completes the item listing form, creating the item listing, including the image of the item, information inferred from the image regarding the item, and any additional information received from the user of the mobile device. The GIPS  124  uses a completed item listing to display the item within an augmented reality environment of a seller&#39;s online store. In  508 , the GIPS  124  presents the item listing in an augmented reality environment. As shown in  FIG. 5 , the GIPS  124  showcases items in an augmented reality environment including a presentation layer, for example, presenting the items on a virtual shelf to be viewed by potential buyers visiting the user&#39;s online store. 
       FIG. 6  is a network diagram illustrating a network system  600  suitable for facilitating automatic guided capturing and presentation of images, according to one embodiment, having a client-server architecture configured for exchanging data over a network  602 . While the network system  600  is depicted as having a client-server architecture, the present inventive subject matter is, of course, not limited to such an architecture, and could equally well find application in an event-driven, distributed, or peer-to-peer architecture system, for example. Further, to avoid obscuring the inventive subject matter with unnecessary detail, various functional components that are not germane to conveying an understanding of the inventive subject matter have been omitted from  FIG. 6 . Moreover, it shall be appreciated that although the various functional components of the network system  600  are discussed in the singular sense, multiple instances of any one of the various functional components may be employed. 
     The network system  600  includes a network-based content publisher  604  in communication with a client device  606  and a third party server  608 . In some example embodiments, the network-based content publisher  604  may be a network-based marketplace (e.g., eBay.com). The network-based content publisher  604  communicates and exchanges data within the network system  600  that pertain to various functions and aspects associated with the network system  600  and its users. The network-based content publisher  604  may provide server-side functionality, via a network  602  (e.g., the Internet), to network devices such as the client device  606 . 
     The client device  606  may be operated by users who use the network system  600  to exchange data over the network  602 . These data exchanges include transmitting, receiving (communicating), and processing data to, from, and regarding content and users of the network system  600 . The data may include, but are not limited to, images; video or audio content; user preferences; product and service feedback, advice, and reviews; product, service, manufacturer, and vendor recommendations and identifiers; product and service listings associated with buyers and sellers; product and service advertisements; auction bids; transaction data; user profile data; and social data, among other things. 
     The client device  606  may interface with the network-based content publisher  104  via a connection with the network  602 . Depending on the form of the client device  606 , any of a variety of types of connections and networks  602  may be used. For example, the connection may be Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or another type of cellular connection. Such a connection may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, or other data transfer technology (e.g., fourth generation wireless, 4G networks). When such technology is employed, the network  602  may include a cellular network that has a plurality of cell sites of overlapping geographic coverage, interconnected by cellular telephone exchanges. These cellular telephone exchanges may be coupled to a network backbone (e.g., the public switched telephone network (PSTN), a packet-switched data network, or other types of networks). 
     In another example, the connection to the network  602  may be a Wireless Fidelity (Wi-Fi, IEEE 802.11x type) connection, a Worldwide Interoperability for Microwave Access (WiMAX) connection, or another type of wireless data connection. In such an embodiment, the network  602  may include one or more wireless access points coupled to a local area network (LAN), a wide area network (WAN), the Internet, or another packet-switched data network. In yet another example, the connection to the network  602  may be a wired connection (e.g., an Ethernet link), and the network  602  may be a LAN, a WAN, the Internet, or another packet-switched data network. Accordingly, a variety of different configurations are expressly contemplated. 
     In various embodiments, the data exchanged within the network system  600  may be dependent upon user-selected functions available through one or more client or user interfaces (UIs). The UIs may be associated with a client device, such as the client device  606  executing a web client  610  (e.g., an Internet browser), which may be in communication with the network-based content publisher  604 . The UIs may also be associated with one or more applications  612  executing on the client device  606 , such as a mobile application designed for interacting with the network-based content publisher  604  or with a social network platform hosted by the third party server  608 . 
     Turning specifically to the network-based content publisher  604 , an API server  614  and a web server  616  are coupled to, and provide programmatic and web interfaces respectively to, an application server  618 . As illustrated in  FIG. 6 , the application server  618  may be coupled via the API server  614  and the web server  616  to the network  602 , for example, via wired or wireless interfaces. The application server  618  is, in turn, shown to be coupled to a database server  620  that facilitates access to a database  622 . In some examples, the application server  618  can access the database  622  directly without the need for the database server  620 . The database  622  may include multiple databases that may be internal or external to the network-based content publisher  604 . 
     The application server  618  may, for example, host one or more applications, which provide a number of content publishing and viewing functions and services to users who access the network-based content publisher  604 . For example, the network-based content publisher  604  may host a marketplace application that provides a number of marketplace functions and services to users, such as publishing, listing, and price-setting mechanisms whereby a seller may list (or publish information concerning) goods or services (also collectively referred to as “products”) for sale, a buyer can express interest in or indicate a desire to purchase such goods or services, and a price can be set for a transaction pertaining to the goods or services. The client device  606  (e.g., mobile device) hosts the GIPS  124  that performs automatic guided capturing and presentation of images. 
     The messages generated and published by the GIPS  124  may also include a reference to content (e.g., a link). As used herein, the terms “content” or “content item” refer to electronic data that is consumed by viewers (e.g., users) on displays, client devices, or page/display-based media (e.g., World-Wide Web (WWW) media embodied in browsers and accessible over the Internet). As such, the terms “content” and “content item” may refer to data associated with readable text, data associated with images, data associate with graphics or video, programmatic content, scripts, or data corresponding to various combinations of these. Further, while the GIPS  124  is shown in  FIG. 6  to form part of the client device  606 , it will be appreciated that, in alternative embodiments, the GIPS  124  may form part of a service that is separate and distinct from the client device  606 . For example, in some embodiments, the GIPS  124  may be hosted in whole or in part by the application server  618 , 
     The database  622  stores data pertaining to various functions and aspects associated with the network system  600  and its users. For example, the database  622  stores and maintains user profiles for users of the network-based content publisher  604 . Each user profile comprises user profile data that describes aspects of a particular user. The user profile data may, for example, include demographic data, user preferences, social data, and financial information. The demographic data may, for example, include information describing one or more characteristics of a user such as gender, age, location information (e.g., hometown or current location), employment history, education history, contact information, familial relations, or user interests. The financial information may, for example, include private financial information of the user such as account number, credential, password, device identifier, user name, phone number, credit card information, bank information, transaction history, or other financial information which may be used to facilitate online transactions by the user. 
     The database  622  also stores a record of user activity. Accordingly, the network-based content publisher  604  may monitor, track, and record activities of users utilizing one or more devices (e.g., the client device  606 ) to interact with the various components of the network system  600  during a user session. Each user session may be maintained in a repository stored in the database  622 . Accordingly, the user activity data may include past keyword searches that users have performed, web pages viewed by each user, products added to a user wish list or watch list, products added to an electronic shopping cart, and products that the users own. Consistent with some embodiments, the record of user activity is linked to user profile data so as to maintain an association of a user profile with the activities that the corresponding user has performed. 
     In instances in which the network-based content publisher  104  is a network-based marketplace, the database  122  stores product information. Such product information may, for example, include a product identifier (e.g., a title or a model number), a price, a make, a manufacturer, a model, a brand name, a textual description, a size, a style, product dimensions, compatibility information, or any other information that may be used to describe a product. In these instances, the database  622  may also store a transaction history of users of the network-based content publisher  604  that includes information related to transactions for products that may be offered for sale by merchants who utilize marketplace services provided by the network-based content publisher  604 . The transaction history information may, for example, include a description of a product offered for sale, sold, or purchased by users, an identifier of the product, a category to which the product belongs, a purchase price, a purchase date, a purchased quantity, a number of bids for the product, or various combinations thereof. 
       FIG. 6  also illustrates a third party application  626  executing on the third party server  608  that may offer information or services to the application server  618  or to users of the client device  606 . The third party application  626  has programmatic access to the network-based content publisher  604  via a programmatic interface provided by the API server  614 . The third party application  626  is associated with any organization that may conduct transactions with or provide services to the application server  618  or to users of the client device  606 . For example, the third party application  626  may be associated with a network based social network platform (e.g., Facebook®, Twitter®, Google+®, Pinterest®, LinkedIn®, or the like) that may provide a platform for members to build and maintain social networks and relations among members. 
       FIG. 7  is a block diagram illustrating components of a machine  700 , according to some example embodiments, able to read instructions  724  from a machine-readable medium  722  (e.g., a non-transitory machine-readable medium, a machine-readable storage medium, a computer-readable storage medium, or any suitable combination thereof) and perform any one or more of the methodologies discussed herein, in whole or in part. Specifically,  FIG. 7  shows the machine  700  in the example form of a computer system (e.g., a computer) within which the instructions  724  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  700  to perform any one or more of the methodologies discussed herein may be executed, in whole or in part. 
     In alternative embodiments, the machine  700  operates as a standalone device or may be communicatively coupled (e.g., networked) to other machines. In a networked deployment, the machine  700  may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a distributed (e.g., peer-to-peer) network environment. The machine  700  may be a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a cellular telephone, a smartphone, a set-top box (STB), a personal digital assistant (PDA), a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions  724 , sequentially or otherwise, that specify actions to be taken by that machine. Further, while only a single machine  700  is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute the instructions  724  to perform all or part of any one or more of the methodologies discussed herein. 
     The machine  700  includes a processor  702  (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a radio-frequency integrated circuit (RFIC), or any suitable combination thereof), a main memory  704 , and a static memory  706 , which are configured to communicate with each other via a bus  708 . The processor  702  contains solid-state digital microcircuits (e.g., electronic, optical, or both) that are configurable, temporarily or permanently, by some or all of the instructions  724  such that the processor  702  is configurable to perform any one or more of the methodologies described herein, in whole or in part. For example, a set of one or more microcircuits of the processor  702  may be configurable to execute one or more modules (e.g., software modules) described herein. In some example embodiments, the processor  702  is a multicore CPU (e.g., a dual-core CPU, a quad-core CPU, or a 128-core CPU) within which each of multiple cores behaves as a separate processor that is able to perform any one or more of the methodologies discussed herein, in whole or in part. Although the beneficial effects described herein may be provided by the machine  700  with at least the processor  702 , these same beneficial effects may be provided by a different kind of machine that contains no processors (e.g., a purely mechanical system, a purely hydraulic system, or a hybrid mechanical-hydraulic system), if such a processor-less machine is configured to perform one or more of the methodologies described herein. 
     The machine  700  may further include a graphics display  710  (e.g., a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, a cathode ray tube (CRT), or any other display capable of displaying graphics or video). The machine  700  may also include an alphanumeric input device  712  (e.g., a keyboard or keypad), a cursor input device  714  (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, an eye tracking device, or other pointing instrument), a data storage  716 , an audio generation device  718  (e.g., a sound card, an amplifier, a speaker, a headphone jack, or any suitable combination thereof), and a network interface device  720 . 
     The data storage  716  (e.g., a data storage device) includes the machine-readable medium  722  (e.g., a tangible and non-transitory machine-readable storage medium) on which are stored the instructions  724  embodying any one or more of the methodologies or functions described herein. The instructions  724  may also reside, completely or at least partially, within the main memory  704 , within the processor  702  (e.g., within the processor&#39;s cache memory), or both, before or during execution thereof by the machine  700 . Accordingly, the main memory  704  and the processor  702  may be considered machine-readable media (e.g., tangible and non-transitory machine-readable media). The instructions  724  may be transmitted or received over the network  190  via the network interface device  720 . For example, the network interface device  720  may communicate the instructions  724  using any one or more transfer protocols (e.g., hypertext transfer protocol (HTTP)). 
     In some example embodiments, the machine  700  may be a portable computing device (e.g., a smart phone, tablet computer, or a wearable device), and have one or more additional input components  730  (e.g., sensors or gauges). Examples of such input components  730  include an image input component (e.g., one or more cameras), an audio input component (e.g., one or more microphones), a direction input component (e.g., a compass), a location input component (e.g., a global positioning system (GPS) receiver), an orientation component (e.g., a gyroscope), a motion detection component (e.g., one or more accelerometers), an altitude detection component (e.g., an altimeter), a biometric input component (e.g., a heartrate detector or a blood pressure detector), and a gas detection component (e.g., a gas sensor). Input data gathered by any one or more of these input components  730  may be accessible and available for use by any of the modules described herein. 
     As used herein, the term “memory” refers to a machine-readable medium able to store data temporarily or permanently and may be taken to include, but not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, and cache memory. While the machine-readable medium  722  is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions  724 . The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing the instructions  724  for execution by the machine  700 , such that the instructions  724 , when executed by one or more processors of the machine  700  (e.g., processor  702 ), cause the machine  700  to perform any one or more of the methodologies described herein, in whole or in part. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as cloud-based storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, one or more tangible and non-transitory data repositories (e.g., data volumes) in the example form of a solid-state memory chip, an optical disc, a magnetic disc, or any suitable combination thereof. A “non-transitory” machine-readable medium, as used herein, specifically does not include propagating signals per se. In some example embodiments, the instructions  724  for execution by the machine  700  may be communicated by a carrier medium. Examples of such a carrier medium include a storage medium (e.g., a non-transitory machine-readable storage medium, such as a solid-state memory, being physically moved from one place to another place) and a transient medium (e.g., a propagating signal that communicates the instructions  724 ). 
     Certain example embodiments are described herein as including modules. Modules may constitute software modules (e.g., code stored or otherwise embodied in a machine-readable medium or in a transmission medium), hardware modules, or any suitable combination thereof. A “hardware module” is a tangible (e.g., non-transitory) physical component (e.g., a set of one or more processors) capable of performing certain operations and may be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems or one or more hardware modules thereof may be configured by software (e.g., an application or portion thereof) as a hardware module that operates to perform operations described herein for that module. 
     In some example embodiments, a hardware module may be implemented mechanically, electronically, hydraulically, or any suitable combination thereof. For example, a hardware module may include dedicated circuitry or logic that is permanently configured to perform certain operations. A hardware module may be or include a special-purpose processor, such as a field programmable gate array (FPGA) or an ASIC. A hardware module may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. As an example, a hardware module may include software encompassed within a CPU or other programmable processor. It will be appreciated that the decision to implement a hardware module mechanically, hydraulically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. 
     Accordingly, the phrase “hardware module” should be understood to encompass a tangible entity that may be physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Furthermore, as used herein, the phrase “hardware-implemented module” refers to a hardware module. Considering example embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where a hardware module includes a CPU configured by software to become a special-purpose processor, the CPU may be configured as respectively different special-purpose processors (e.g., each included in a different hardware module) at different times. Software (e.g., a software module) may accordingly configure one or more processors, for example, to become or otherwise constitute a particular hardware module at one instance of time and to become or otherwise constitute a different hardware module at a different instance of time. 
     Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over circuits and buses) between or among two or more of the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory (e.g., a memory device) to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information from a computing resource). 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented module” refers to a hardware module in which the hardware includes one or more processors. Accordingly, the operations described herein may be at least partially processor-implemented, hardware-implemented, or both, since a processor is an example of hardware, and at least some operations within any one or more of the methods discussed herein may be performed by one or more processor-implemented modules, hardware-implemented modules, or any suitable combination thereof. 
     Moreover, such one or more processors may perform operations in a “cloud computing” environment or as a service (e.g., within a “software as a service” (SaaS) implementation). For example, at least some operations within any one or more of the methods discussed herein may be performed by a group of computers (e.g., as examples of machines that include processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an application program interface (API)). The performance of certain operations may be distributed among the one or more processors, whether residing only within a single machine or deployed across a number of machines. In some example embodiments, the one or more processors or hardware modules (e.g., processor-implemented modules) may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the one or more processors or hardware modules may be distributed across a number of geographic locations. 
     Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and their functionality presented as separate components and functions in example configurations may be implemented as a combined structure or component with combined functions. Similarly, structures and functionality presented as a single component may be implemented as separate components and functions. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein. 
     Some portions of the subject matter discussed herein may be presented in terms of algorithms or symbolic representations of operations on data stored as bits or binary digital signals within a memory (e.g., a computer memory or other machine memory). Such algorithms or symbolic representations are examples of techniques used by those of ordinary skill in the data processing arts to convey the substance of their work to others skilled in the art. As used herein, an “algorithm” is a self-consistent sequence of operations or similar processing leading to a desired result. In this context, algorithms and operations involve physical manipulation of physical quantities. Typically, but not necessarily, such quantities may take the form of electrical, magnetic, or optical signals capable of being stored, accessed, transferred, combined, compared, or otherwise manipulated by a machine. It is convenient at times, principally for reasons of common usage, to refer to such signals using words such as “data,” “content,” “bits,” “values,” “elements,” “symbols,” “characters,” “terms,” “numbers,” “numerals,” or the like. These words, however, are merely convenient labels and are to be associated with appropriate physical quantities. 
     Unless specifically stated otherwise, discussions herein using words such as “accessing,” “processing,” “detecting,” “computing,” “calculating,” “determining,” “generating,” “presenting,” “displaying,” or the like refer to actions or processes performable by a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or any suitable combination thereof), registers, or other machine components that receive, store, transmit, or display information. Furthermore, unless specifically stated otherwise, the terms “a” or “an” are herein used, as is common in patent documents, to include one or more than one instance. Finally, as used herein, the conjunction “or” refers to a non-exclusive “or,” unless specifically stated otherwise. 
     The following describes various examples of methods, machine-readable media, and systems (e.g., machines, devices, or other apparatus) discussed herein. 
     EXAMPLES 
     A first example provides a method comprising capturing an image of an item, including guiding the user to position a mobile device in a direction from the item such that the item is included within the boundary of an area of a user interface of the mobile device, detecting the item through a camera of the mobile device and capturing at least one image frame of the item, estimating a boundary of a first region and a boundary of a second region of the at least one image frame, determining that the first region comprises a larger portion of the item than the second region, displaying instructions on the user interface of the mobile device instructing the user to take an action such that the item will become completely included and visible within the boundary of the first region, and detecting that the item is completely included and visible within the boundary of the first region. 
     A second example provides a method according to the first example further comprising automatically removing a background of the image frame, including accessing the image frame of the item, detecting a boundary of the item within the first region, the boundary of the item following an outline of the item on the user interface, an inner portion of the boundary comprising only the item and an outer portion of the boundary comprising the background of the image frame, differentiating the inner portion of the boundary from the outer portion of the boundary, estimating a first binary mask, the first binary mask comprising the inner portion of the boundary and the outer portion of the boundary of the image frame, computing a color histogram of the image frame, including the inner portion of the boundary and the outer portion of the boundary, computing a probability map of the image frame, including the inner portion of the boundary and the outer portion of the boundary, and smoothing the probability map, including the inner portion of the boundary and the outer portion of the boundary. 
     A third example provides a method according to the second example further comprising obtaining a second binary mask, assigning a first binary value to each pixel of a first plurality of pixels within the image frame having an RGB value above a determined threshold value and assigning a second binary value to each pixel of a second plurality of pixels within the image frame having an RGB value below the determined threshold value, and obtaining a first background-free item image, applying the second binary mask to the image frame. 
     A fourth example provides a method according to the first example further comprising performing manual mask editing, including accessing the probability map, receiving a user input from the mobile device, the user input including a scribble, smoothing the scribble, and scaling the scribble, including assigning the first binary value to each pixel of a third plurality of pixels comprising the scribble having an RGB value above the determined threshold value and assigning the second binary value to each pixel having an RGB value below the determined threshold value. 
     A fifth example provides a method according to the first example further comprising obtaining a third binary mask, including differentiating the scaled scribble from the probability map, adding or subtracting the binary values of the pixels comprising the scaled scribble from the probability map, and obtaining a second background-free item image, applying the third binary mask to the first background-free item image. 
     A sixth example provides a method according to the first example further comprising generating an item listing, inferring item information from the image frame and automatically applying the inferred item information to an item listing form. 
     A seventh example provides a method according to the first example further comprising presenting an item listing in an augmented reality environment, including displaying the item from the image frame in a presentation layer on a user interface of a mobile device. 
     An eighth example provides a system comprising one or more processors and executable instructions accessible on a computer-readable medium that, when executed, cause the one or more processors to perform operations comprising capturing an image of an item, including guiding the user to position a mobile device in a direction from the item such that the item is included within the boundary of an area of a user interface of the mobile device, detecting the item through a camera of the mobile device and capturing at least one image frame of the item, estimating a boundary of a first region and a boundary of a second region of the at least one image frame, determining that the first region comprises a larger portion of the item than the second region, displaying instructions on the user interface of the mobile device instructing the user to take an action such that the item will become completely included and visible within the boundary of the first region, and detecting that the item is completely included and visible within the boundary of the first region. 
     A ninth example provides a system according to the eighth example, wherein the operations further comprise automatically removing a background of the image frame, including accessing the image frame of the item detecting a boundary of the item within the first region, the boundary of the item following an outline of the item on the user interface, an inner portion of the boundary comprising only the item and an outer portion of the boundary comprising the background of the image frame, differentiating the inner portion of the boundary from the outer portion of the boundary, estimating a first binary mask, the first binary mask comprising the inner portion of the boundary and the outer portion of the boundary of the image frame, computing a color histogram of the image frame, including the inner portion of the boundary and the outer portion of the boundary, computing a probability map of the image frame, including the inner portion of the boundary and the outer portion of the boundary, and smoothing the probability map, including the inner portion of the boundary and the outer portion of the boundary. 
     A tenth example provides a system according to the ninth example, wherein the operations further comprise obtaining a second binary mask, assigning a first binary value to each pixel of a first plurality of pixels within the image frame having an RGB value above a determined threshold value and assigning a second binary value to each pixel of a second plurality of pixels within the image frame having an RGB value below the determined threshold value, and obtaining a first background-free item image, applying the second binary mask to the image frame. 
     An eleventh example provides a system according to the eighth example, wherein the operations further comprise performing manual mask editing, including accessing the probability map, receiving a user input from the mobile device, the user input including a scribble, smoothing the scribble, and scaling the scribble, including assigning the first binary value to each pixel of a third plurality of pixels comprising the scribble having an RGB value above the determined threshold value and assigning the second binary value to each pixel having an RGB value below the determined threshold value. 
     A twelfth example provides a system according to the eleventh example, wherein the operations further comprise obtaining a third binary mask, including differentiating the scaled scribble from the probability map, adding or subtracting the binary values of the pixels comprising the scaled scribble from the probability map, and obtaining a second background-free item image, applying the third binary mask to the first background-free item image. 
     A thirteenth example provides a system according to the eighth example, wherein the operations further comprise generating an item listing, inferring item information from the image frame and automatically applying the inferred item information to an item listing form. 
     A fourteenth example provides a system according to the eighth example, wherein the operations further comprise presenting an item listing in an augmented reality environment, including displaying the item from the image frame in a presentation layer on a user interface of a mobile device. 
     A fifteenth example provides a computer-readable hardware storage device having stored therein a set of program instructions which, when executed by the computer causes the computer to perform operations comprising capturing an image of an item, including guiding the user to position a mobile device in a direction from the item such that the item is included within the boundary of an area of a user interface of the mobile device, detecting the item through a camera of the mobile device and capturing at least one image frame of the item, estimating a boundary of a first region and a boundary of a second region of the at least one image frame, determining that the first region comprises a larger portion of the item than the second region, displaying instructions on the user interface of the mobile device instructing the user to take an action such that the item will become completely included and visible within the boundary of the first region, and detecting that the item is completely included and visible within the boundary of the first region. 
     A sixteenth example provides a computer-readable hardware storage device according to the fifteenth example, further comprising automatically removing a background of the image frame, including accessing the image frame of the item, detecting a boundary of the item within the first region, the boundary of the item following an outline of the item on the user interface, an inner portion of the boundary comprising only the item and an outer portion of the boundary comprising the background of the image frame, differentiating the inner portion of the boundary from the outer portion of the boundary, estimating a first binary mask, the first binary mask comprising the inner portion of the boundary and the outer portion of the boundary of the image frame, computing a color histogram of the image frame, including the inner portion of the boundary and the outer portion of the boundary, computing a probability map of the image frame, including the inner portion of the boundary and the outer portion of the boundary, and smoothing the probability map, including the inner portion of the boundary and the outer portion of the boundary. 
     A seventeenth example provides a computer-readable hardware storage device according to the sixteenth example, further comprising obtaining a second binary mask, assigning a first binary value to each pixel of a first plurality of pixels within the image frame having an RGB value above a determined threshold value and assigning a second binary value to each pixel of a second plurality of pixels within the image frame having an RGB value below the determined threshold value, and obtaining a first background-free item image, applying the second binary mask to the image frame. 
     An eighteenth example provides a computer-readable hardware storage device according to the fifteenth example, further comprising performing manual mask editing, including accessing the probability map, receiving a user input from the mobile device, the user input including a scribble, smoothing the scribble, and scaling the scribble, including assigning the first binary value to each pixel of a third plurality of pixels comprising the scribble having an RGB value above the determined threshold value and assigning the second binary value to each pixel having an RGB value below the determined threshold value. 
     A nineteenth example provides a computer-readable hardware storage device according to the eighteenth example, further comprising obtaining a third binary mask, including differentiating the scaled scribble from the probability map, adding or subtracting the binary values of the pixels comprising the scaled scribble from the probability map, and obtaining a second background-free item image, applying the third binary mask to the first background-free item image. 
     A twentieth example provides a computer-readable hardware storage device according to the fifteenth example, further comprising generating an item listing, inferring item information from the image frame and automatically applying the inferred item information to an item listing form.