Patent Grant
9270899
Users are increasingly utilizing electronic devices to obtain various types of information. For example, a user wanting to obtain information about a book can capture an image of the cover of the book and upload that image to a book identification service for analysis. In many cases, the cover image will be matched against a set of two-dimensional images including views of objects from a particular orientation. While books are typically relatively easy to match, as a book cover generally includes several features that enable the cover to be matched against a set of cover images, other objects are not as straightforward to match. For example, an object such as a men's dress shoe that is captured from the side might not have many distinctive features, and may appear primarily as a shaped black object in the image. In order to efficiently perform image matching for such an object, the object of interest is often first separated from the background portion of the image. Unfortunately, it can be difficult to separate an object that does not have many unique features that help to distinguish the object from the background. Accordingly, objects such as shoes can take longer to recognize, and the results can be less accurate on average than for objects such as books or media packaging.
Various embodiments in accordance with the present disclosure will be described with reference to the drawings, in which:
a), 3(b), 3(c), and 3(d) illustrate example images of an object that can be captured and generated during processing in accordance with various embodiments;
a), 4(b), 4(c), and 4(d) illustrate example images of an object that can be generated during processing in accordance with various embodiments;
Systems and methods in accordance with various embodiments of the present disclosure overcome one or more of the above-referenced and other deficiencies in conventional approaches to identifying objects using an electronic device. In particular, various embodiments enable a user to capture images including a view of an object of interest and receive information about one or more objects that are determined to match based at least in part on the captured images. A pair of images is captured in at least some embodiments, with a first image being captured without use of a flash and a second image being captured with a flash (or other source of illumination). The images can be compared in order to attempt to suppress a significant portion of the background in the image, as the flash will generally affect objects in the foreground much more than most objects in the background. In some embodiments, the resulting image can be a grayscale image that can have the intensities normalized to assist with processing. An algorithm such as a connected components algorithm can be applied to the normalized image to attempt to locate portions corresponding to one or more objects in the image. Based upon information such as the shape of these located objects, whether edges of the objects appear in the image, and other such information, a selection process can determine the portion that likely corresponds to the object of interest. The shape or outline of this portion or region then can be used with one of the original captured images to extract the portion of the image that corresponds to the object of interest. This portion then can be provided to an object recognition, image matching, or other such process.
Various other functions and advantages are described and suggested below as may be provided in accordance with the various embodiments.
In this example, a camera 106 on the device 104 can capture image information including the book 110 of interest, and at least a portion of the image can be displayed on a display screen 112 of the computing device. At least a portion of the image information can be analyzed and, upon a match being located, identifying information can be displayed back to the user via the display screen 112 of the computing device 104. The portion of the image to be analyzed can be indicated manually, such as by a user pointing to the book on the screen or drawing a bounding box around the book. In other embodiments, one or more image analysis algorithms can attempt to automatically locate one or more objects in an image. In some embodiments, a user can manually cause image information to be analyzed, while in other embodiments the image information can be analyzed automatically, either on the device or by transferring image data to a remote system or service as discussed later herein.
As discussed, however, other types of objects can be more difficult to recognize based on a captured image. For example,
Approaches in accordance with various embodiments can improve the accuracy and efficiency of object recognition for objects including those without several distinctive features, particularly those objects that belong to a class represented primarily by the shapes of the objects. Various approaches can utilize a pair of images of an object to assist in segmenting an object of interest from background or other objects in the images, enabling an object boundary to be determined. Such an approach effectively eliminates most of the outlier features, which can improve precision over conventional approaches for other types of objects as well.
An approach in accordance various embodiments uses a two-stage process for segmenting an object from other portions of an image, where those stages include a pre-processing stage and a processing stage. It should be understood that these stages are used for purposes of explanation only, and that approaches discussed herein can be performed as part of a single process or multiple processes.
In an example pre-processing stage, two captured images can be obtained that include similar views of an object of interest. The images can be digital still images captured at different times (within a determined allowable amount of time) or frames of video corresponding to different points in time, among other such options. For one of the images, a flash or other source of illumination can be activated such that at least some objects in the images will reflect, or at least show the effects of, the flash. The flash element can be any appropriate source of illumination, such as a digital flash, a flash gun, a flashtube, a microflash, an LED, a ring lite, and the like. An advantage to using flash-type illumination for one of the images is that objects in the foreground will generally reflect more light, and thus appear brighter, than background objects that are further away. Such an approach enables foreground objects to be identified with respect to at least some background objects or areas, although objects such as mirrors might reflect very well even when in the background of an image.
As an example,
Idiff=I2−I1,
where
I1=E1*R
and
I2=E2*R*cos(α)/Z4,
with I1 being the image without flash and only ambient light E1 and matte reflectance map R, and I2 being the image with flash, which depends upon the angle α between the light source direction and the surface normal. Also, the normalized image can be determined from:
Inorm=Idiff/I1˜cos(α)/Z4,
where the normalized image does not depend on object color variation, but retains the dependence on distance and surface curvature. While in this example the differential image includes substantially only the portions corresponding to the shoe 302 and the table 304, it should be understood that due to noise, auto-exposure adjustments, and other such reasons that there can be at least some other features represented in a differential image as well in at least some examples. Further, curves, angles, and other aspects of the object can cause at least certain portions of the object to reflect differently, which can also impact the accuracy of the comparison process. As can be seen, however, the portions of the original image that are primarily represented in the differential image correspond to the foreground objects. Unfortunately, factors such as object color variation can still complicate segmentation of this differential image.
In order to minimize this problem, a normalization process can be applied whereby the colors (or intensities for a grayscale image) of the differential image can be normalized using the colors (or intensities) from original non flash image, or the average of flash and no-flash images in some embodiments. Such an approach can help to reduce the effective differences due to color.
During a processing stage, one or more segmentation techniques can be applied to the normalized image. Any of a number of conventional and/or modified segmentation techniques can be used for such purposes. For example, a computer vision algorithm or other segmentation algorithm (e.g., GrabCut, WaterShed, or QuadTree) can be used if a sufficient initialization process is used that determines the approximate region of the object within an allowable amount of variation. In the present example, however, a connected component algorithm can be used with a Canny edge filter (which locates edges based on changes in color or intensity) to select raw foreground information and use this information to generate a raw outline of one or more objects in the normalized image, as objects near the foreground might provide similar intensity or color values, and thus each be picked up by a connected component algorithm. Such an algorithm can look at the intensity value of a point and compare that value to the intensity value of nearby points to attempt to determine points that likely correspond to a common object, based on factors such as the amount of variation in intensity over a given distance. The process can continue expanding out from the point until reaching the “edge” of a region where the points no longer appear to belong to the same object. The shape of this region should roughly approximate the shape of an item in the image. As an example, a first result of such an algorithm can be an object 402 that essentially corresponds to the table, as illustrated in the image 400 of
Once a portion is selected that corresponds to a foreground object and likely corresponds to the object of interest, an outline, edge, or shape of that object can be determined. For example, if the shoe portion 422 of
Certain approaches use infrared (IR) illumination instead of a flash, since IR is faster and the processing can be done in near real time. The use of flash, however, is more powerful than IR, even though flash may require an offline process. An offline process can be acceptable for processes such as object recognition, however, where the user might be willing to wait up to a couple of seconds to receive the results.
A potential downside to using flash, however, is that there will necessarily be some delay between capturing an image of an object with flash and another image of the object without flash. Such a delay can allow for movement of the camera, which can impact the image subtraction or differential process as the portions to be compared will no longer align. In some embodiments a sensor such as an electronic gyroscope or accelerometer can be used to detect motion, which then can be used to attempt to align the object in the images. Various other approaches exist for aligning images as well. In some embodiments, the sensor data can detect when more than an allowable amount of movement has occurred between image captures, and might simply indicate to the user that too much movement occurred and the user should attempt to capture the images again. Such an approach also has the benefit that it can help to minimize blur in the images, which can also improve segmentation, matching, and other such processes.
As discussed, information such as that illustrated in
In this example, the request is received to a network interface layer 508 of the content provider 506. The network interface layer can include any appropriate components known or used to receive requests from across a network, such as may include one or more application programming interfaces (APIs) or other such interfaces for receiving such requests. The network interface layer 508 might be owned and operated by the provider, or leveraged by the provider as part of a shared resource or “cloud” offering. The network interface layer can receive and analyze the request, and cause at least a portion of the information in the request to be directed to an appropriate system or service, such as a matching service 510 as illustrated in
The matching service 510 in this example can cause information to be sent to at least one identification service 514, device, system, or module that is operable to analyze the image data and attempt to locate one or more matches for objects reflected in the image data. In at least some embodiments, an identification service 514 will process the received data, such as to extract points of interest or unique features in a captured image, for example, then compare the processed data against data stored in a matching data store 520 or other such location. In other embodiments, the unique feature points, image histograms, or other such information about an image can be generated on the device and uploaded to the matching service, such that the identification service can use the processed image information to perform the match without a separate image analysis and feature extraction process. Certain embodiments can support both options, among others. The data in an image matching data store 520 might be indexed and/or processed to facilitate with matching, as is known for such purposes. For example, the data store might include a set of histograms or feature vectors instead of a copy of the images to be used for matching, which can increase the speed and lower the processing requirements of the matching. Approaches for generating image information to use for image matching are well known in the art and as such will not be discussed herein in detail.
The matching service 510 can receive information from each contacted identification service 514 as to whether one or more matches could be found with at least a threshold level of confidence, for example, and can receive any appropriate information for a located potential match. The information from each identification service can be analyzed and/or processed by one or more applications of the matching service, such as to determine data useful in obtaining information for each of the potential matches to provide to the user. For example, a matching service might receive bar codes, product identifiers, or any other types of data from the identification service(s), and might process that data to be provided to a service such as an information aggregator service 516 that is capable of locating descriptions or other content related to the located potential matches.
In at least some embodiments, an information aggregator might be associated with an entity that provides an electronic marketplace, or otherwise provides items or content for consumption (e.g., purchase, rent, lease, or download) by various customers. Although products and electronic commerce are presented in this and other examples presented, it should be understood that these are merely examples and that approaches presented in the present disclosure can relate to any appropriate types of objects or information as discussed and suggested elsewhere herein. In such an instance, the information aggregator service 516 can utilize the aggregated data from the matching service 510 to attempt to locate products, in a product data store 524 or other such location, which are offered through the marketplace and that match, or are otherwise related to, the potential match information. For example, if the identification service identifies a book in the captured image or video data, the information aggregator can attempt to determine whether there are any versions of that book (physical or electronic) offered through the marketplace, or at least for which information is available through the marketplace. In at least some embodiments, the information aggregator can utilize one or more suggestion algorithms or other such approaches to attempt to determine related elements that might be of interest based on the determined matches, such as a movie or audio tape version of a book. In some embodiments, the information aggregator can return various types of data (or metadata) to the environmental information service, as may include title information, availability, reviews, and the like. For facial recognition applications, a data aggregator might instead be used that provides data from one or more social networking sites, professional data services, or other such entities. In other embodiments, the information aggregator might instead return information such as a product identifier, uniform resource locator (URL), or other such digital entity enabling a browser or other interface on the client device 502 to obtain information for one or more products, etc. The information aggregator can also utilize the aggregated data to obtain various other types of data as well. Information for located matches also can be stored in a user data store 522 of other such location, which can be used to assist in determining future potential matches or suggestions that might be of interest to the user. Various other types of information can be returned as well within the scope of the various embodiments.
The matching service 510 can bundle at least a portion of the information for the potential matches to send to the client as part of one or more messages or responses to the original request. In some embodiments, the information from the identification services might arrive at different times, as different types of information might take longer to analyze, etc. In these cases, the matching service might send multiple messages to the client device as the information becomes available. The potential matches located by the various identification services can be written to a log data store 512 or other such location in order to assist with future matches or suggestions, as well as to help rate a performance of a given identification service. As should be understood, each service can include one or more computing components, such as at least one server, as well as other components known for providing services, as may include one or more APIs, data storage, and other appropriate hardware and software components.
It should be understood that, although the identification services are shown to be part of the provider environment 506 in
The example computing device 700 also includes at least one microphone 706 or other audio capture device capable of capturing audio data, such as words or commands spoken by a user of the device. In this example, a microphone 706 is placed on the same side of the device as the display screen 702, such that the microphone will typically be better able to capture words spoken by a user of the device. In at least some embodiments, a microphone can be a directional microphone that captures sound information from substantially directly in front of the microphone, and picks up only a limited amount of sound from other directions. It should be understood that a microphone might be located on any appropriate surface of any region, face, or edge of the device in different embodiments, and that multiple microphones can be used for audio recording and filtering purposes, etc.
The example computing device 700 also includes at least one orientation sensor 708, such as a position and/or movement-determining element. Such a sensor can include, for example, an accelerometer or gyroscope operable to detect an orientation and/or change in orientation of the computing device, as well as small movements of the device. An orientation sensor also can include an electronic or digital compass, which can indicate a direction (e.g., north or south) in which the device is determined to be pointing (e.g., with respect to a primary axis or other such aspect). An orientation sensor also can include or comprise a global positioning system (GPS) or similar positioning element operable to determine relative coordinates for a position of the computing device, as well as information about relatively large movements of the device. Various embodiments can include one or more such elements in any appropriate combination. As should be understood, the algorithms or mechanisms used for determining relative position, orientation, and/or movement can depend at least in part upon the selection of elements available to the device.
In some embodiments, the computing device 800 of
The device 800 also can include at least one orientation or motion sensor 810. As discussed, such a sensor can include an accelerometer or gyroscope operable to detect an orientation and/or change in orientation, or an electronic or digital compass, which can indicate a direction in which the device is determined to be facing. The mechanism(s) also (or alternatively) can include or comprise a global positioning system (GPS) or similar positioning element operable to determine relative coordinates for a position of the computing device, as well as information about relatively large movements of the device. The device can include other elements as well, such as may enable location determinations through triangulation or another such approach. These mechanisms can communicate with the processor 802, whereby the device can perform any of a number of actions described or suggested herein.
As an example, a computing device such as that described with respect to
As discussed, different approaches can be implemented in various environments in accordance with the described embodiments. For example,
The illustrative environment includes at least one application server 908 and a data store 910. It should be understood that there can be several application servers, layers or other elements, processes or components, which may be chained or otherwise configured, which can interact to perform tasks such as obtaining data from an appropriate data store. As used herein the term “data store” refers to any device or combination of devices capable of storing, accessing and retrieving data, which may include any combination and number of data servers, databases, data storage devices and data storage media, in any standard, distributed or clustered environment. The application server can include any appropriate hardware and software for integrating with the data store as needed to execute aspects of one or more applications for the client device and handling a majority of the data access and business logic for an application. The application server provides access control services in cooperation with the data store and is able to generate content such as text, graphics, audio and/or video to be transferred to the user, which may be served to the user by the Web server in the form of HTML, XML or another appropriate structured language in this example. The handling of all requests and responses, as well as the delivery of content between the client device 902 and the application server 908, can be handled by the Web server 906. It should be understood that the Web and application servers are not required and are merely example components, as structured code discussed herein can be executed on any appropriate device or host machine as discussed elsewhere herein.
The data store 910 can include several separate data tables, databases or other data storage mechanisms and media for storing data relating to a particular aspect. For example, the data store illustrated includes mechanisms for storing production data 912 and user information 916, which can be used to serve content for the production side. The data store also is shown to include a mechanism for storing log or session data 914. It should be understood that there can be many other aspects that may need to be stored in the data store, such as page image information and access rights information, which can be stored in any of the above listed mechanisms as appropriate or in additional mechanisms in the data store 910. The data store 910 is operable, through logic associated therewith, to receive instructions from the application server 908 and obtain, update or otherwise process data in response thereto. In one example, a user might submit a search request for a certain type of element. In this case, the data store might access the user information to verify the identity of the user and can access the catalog detail information to obtain information about elements of that type. The information can then be returned to the user, such as in a results listing on a Web page that the user is able to view via a browser on the user device 902. Information for a particular element of interest can be viewed in a dedicated page or window of the browser.
Each server typically will include an operating system that provides executable program instructions for the general administration and operation of that server and typically will include computer-readable medium storing instructions that, when executed by a processor of the server, allow the server to perform its intended functions. Suitable implementations for the operating system and general functionality of the servers are known or commercially available and are readily implemented by persons having ordinary skill in the art, particularly in light of the disclosure herein.
The environment in one embodiment is a distributed computing environment utilizing several computer systems and components that are interconnected via communication links, using one or more computer networks or direct connections. However, it will be appreciated by those of ordinary skill in the art that such a system could operate equally well in a system having fewer or a greater number of components than are illustrated in
As discussed above, the various embodiments can be implemented in a wide variety of operating environments, which in some cases can include one or more user computers, computing devices, or processing devices which can be used to operate any of a number of applications. User or client devices can include any of a number of general purpose personal computers, such as desktop or laptop computers running a standard operating system, as well as cellular, wireless, and handheld devices running mobile software and capable of supporting a number of networking and messaging protocols. Such a system also can include a number of workstations running any of a variety of commercially-available operating systems and other known applications for purposes such as development and database management. These devices also can include other electronic devices, such as dummy terminals, thin-clients, gaming systems, and other devices capable of communicating via a network.
Various aspects also can be implemented as part of at least one service or Web service, such as may be part of a service-oriented architecture. Services such as Web services can communicate using any appropriate type of messaging, such as by using messages in extensible markup language (XML) format and exchanged using an appropriate protocol such as SOAP (derived from the “Simple Object Access Protocol”). Processes provided or executed by such services can be written in any appropriate language, such as the Web Services Description Language (WSDL). Using a language such as WSDL allows for functionality such as the automated generation of client-side code in various SOAP frameworks.
Most embodiments utilize at least one network that would be familiar to those skilled in the art for supporting communications using any of a variety of commercially-available protocols, such as TCP/IP, OSI, FTP, UPnP, NFS, CIFS, and AppleTalk. The network can be, for example, a local area network, a wide-area network, a virtual private network, the Internet, an intranet, an extranet, a public switched telephone network, an infrared network, a wireless network, and any combination thereof.
In embodiments utilizing a Web server, the Web server can run any of a variety of server or mid-tier applications, including HTTP servers, FTP servers, CGI servers, data servers, Java servers, and business application servers. The server(s) also may be capable of executing programs or scripts in response requests from user devices, such as by executing one or more Web applications that may be implemented as one or more scripts or programs written in any programming language, such as Java®, C, C# or C++, or any scripting language, such as Perl, Python, or TCL, as well as combinations thereof. The server(s) may also include database servers, including without limitation those commercially available from Oracle®, Microsoft®, Sybase®, and IBM®.
The environment can include a variety of data stores and other memory and storage media as discussed above. These can reside in a variety of locations, such as on a storage medium local to (and/or resident in) one or more of the computers or remote from any or all of the computers across the network. In a particular set of embodiments, the information may reside in a storage-area network (“SAN”) familiar to those skilled in the art. Similarly, any necessary files for performing the functions attributed to the computers, servers, or other network devices may be stored locally and/or remotely, as appropriate. Where a system includes computerized devices, each such device can include hardware elements that may be electrically coupled via a bus, the elements including, for example, at least one central processing unit (CPU), at least one input device (e.g., a mouse, keyboard, controller, touch screen, or keypad), and at least one output device (e.g., a display device, printer, or speaker). Such a system may also include one or more storage devices, such as disk drives, optical storage devices, and solid-state storage devices such as random access memory (“RAM”) or read-only memory (“ROM”), as well as removable media devices, memory cards, flash cards, etc.
Such devices also can include a computer-readable storage media reader, a communications device (e.g., a modem, a network card (wireless or wired), an infrared communication device, etc.), and working memory as described above. The computer-readable storage media reader can be connected with, or configured to receive, a computer-readable storage medium, representing remote, local, fixed, and/or removable storage devices as well as storage media for temporarily and/or more permanently containing, storing, transmitting, and retrieving computer-readable information. The system and various devices also typically will include a number of software applications, modules, services, or other elements located within at least one working memory device, including an operating system and application programs, such as a client application or Web browser. It should be appreciated that alternate embodiments may have numerous variations from that described above. For example, customized hardware might also be used and/or particular elements might be implemented in hardware, software (including portable software, such as applets), or both. Further, connection to other computing devices such as network input/output devices may be employed.
Storage media and computer readable media for containing code, or portions of code, can include any appropriate media known or used in the art, including storage media and communication media, such as but not limited to volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage and/or transmission of information such as computer readable instructions, data structures, program modules, or other data, including RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the a system device. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will appreciate other ways and/or methods to implement the various embodiments.
The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. It will, however, be evident that various modifications and changes may be made thereunto without departing from the broader spirit and scope of the invention as set forth in the claims.
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