Under water image color correction

Aspects of the present invention disclose a method for color reconstruction of individual detected objects of an underwater image using a library of reference images. The method includes one or more processors obtaining image data of a computing device that includes an underwater image. The method further includes determining a depth measurement corresponding to the underwater image. The method further includes identifying an object of the underwater image based at least in part on a shape of the object. The method further includes reconstructing one or more colors of the object of the underwater image based at least in part on a reference image.

BACKGROUND OF THE INVENTION

The present invention relates generally to the field of image recognition, and more particularly to underwater color correction.

Computer vision is a field of computer science that works on enabling computers to see, identify and process images in the same way that human vision does, and then provide appropriate output. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information.

Visual recognition uses deep learning algorithms to analyze images for scenes, objects, and other content. Visual recognition deals with how computers can be made to gain high-level understanding from digital images or videos. Also, visual recognition is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images.

Cognitive analytics combines the use of cognitive computing and analytics. Cognitive computing combines artificial intelligence and machine-learning algorithms, in an approach that attempts to reproduce the behavior of the human brain. Analytics is the scientific process of transforming data into insights for making better decisions. Cognitive analytics applies intelligent technologies to bring unstructured data sources within reach of analytics processes for decision making.

SUMMARY

Aspects of the present invention disclose a method, computer program product, and system for color reconstruction of individual detected objects of an underwater image using a library of reference images. The method includes one or more processors obtaining image data of a computing device that includes an underwater image. The method further includes one or more processors determining a depth measurement corresponding to the underwater image. The method further includes one or more processors identifying an object of the underwater image based at least in part on a shape of the object. The method further includes one or more processors reconstructing one or more colors of the object of the underwater image based at least in part on a reference image.

DETAILED DESCRIPTION

Embodiments of the present invention allow for color reconstruction of individual detected objects of an underwater image using a library of reference images. Embodiments of the present invention utilize image recognition techniques to identify shapes of an underwater image. Embodiments of the present invention identify a color of an identified shape of an underwater image from a library of reference images utilizing a depth measurement corresponding to the underwater image. Additional embodiments of the present invention reconstruct a color of one or more identified shapes of an image of the underwater image independent of each of the one or more identified shapes.

Some embodiments of the present invention recognize that images taken underwater without the use of expensive underwater lighting equipment have muted colors due to light absorption of the water (i.e., the color in water is absorbed based on depth and color frequency). Consequently, the colors of underwater photos are blue-green compared to photos made with full color spectrum. Furthermore, embodiments of the present invention recognize that current methodologies that utilize depth information of underwater images to boost the colors using frequency histogram are ineffective if color is absent (i.e., this approach is not able to recover the original colors). Various embodiments of the present invention resolve these problems utilizing image recognition and a high-quality reference library of well-lit underwater subjects to reconstruct colors of an underwater image that includes muted and/or absent colors due to spectrum absorption.

Embodiments of the present invention can operate to eliminate the need to stock and/or attach color filters to an underwater camera to offset the color spectrum absorption of water. Various embodiments of the present invention eliminate the need for multiple color filters by dynamically adjusting parameters utilized to reconstruct colors of a photo based on an underwater depth of a camera taking the photo. Embodiments of the present invention can operate to a white balancing steps in a photo capture workflow, which also can be ineffective when color is absent due to spectrum absorption. Additionally, embodiments of the present invention improve the photo capture workflow by allowing a user to digitally correct image during or after capturing images by automatically identifying and reconstructing colors of shapes in a photo that includes muted and/or is absent of color due to spectrum absorption.

Implementation of embodiments of the invention may take a variety of forms, and exemplary implementation details are discussed subsequently with reference to the Figures.

The present invention may contain various accessible data sources, such as database144, that may include personal data, content, or information the user wishes not to be processed. Personal data includes personally identifying information or sensitive personal information as well as user information, such as tracking or geolocation information. Processing refers to any, automated or unautomated, operation or set of operations such as collection, recording, organization, structuring, storage, adaptation, alteration, retrieval, consultation, use, disclosure by transmission, dissemination, or otherwise making available, combination, restriction, erasure, or destruction performed on personal data. Image program200enables the authorized and secure processing of personal data. Image program200provides informed consent, with notice of the collection of personal data, allowing the user to opt in or opt out of processing personal data. Consent can take several forms. Opt-in consent can impose on the user to take an affirmative action before personal data is processed. Alternatively, opt-out consent can impose on the user to take an affirmative action to prevent the processing of personal data before personal data is processed. Image program200provides information regarding personal data and the nature (e.g., type, scope, purpose, duration, etc.) of the processing. Image program200provides the user with copies of stored personal data. Image program200allows the correction or completion of incorrect or incomplete personal data. Image program200allows the immediate deletion of personal data.

Distributed data processing environment100includes server140and client device120, all interconnected over network102. Network110can be, for example, a telecommunications network, a local area network (LAN), a wide area network (WAN), such as the Internet, or a combination of the three, and can include wired, wireless, or fiber optic connections. Network110can include one or more wired and/or wireless networks capable of receiving and transmitting data, voice, and/or video signals, including multimedia signals that include voice, data, and video information. In general, network110can be any combination of connections and protocols that will support communications between server140, client device120, and other computing devices (not shown) within distributed data processing environment100.

Client device120can be one or more of a laptop computer, a tablet computer, a smart phone, smart watch, a smart speaker, digital camera, or any programmable electronic device capable of communicating with various components and devices within distributed data processing environment100, via network110. In general, client device120represents one or more programmable electronic devices or combination of programmable electronic devices capable of executing machine readable program instructions and communicating with other computing devices (not shown) within distributed data processing environment100via a network, such as network110. Client device120may include components as depicted and described in further detail with respect toFIG. 4, in accordance with embodiments of the present invention.

Client device120may include one or more a processor, user interface122, application124, and sensor126. User interface122is a program that provides an interface between a user of client device120and a plurality of applications that reside on the client device. A user interface, such as user interface122, refers to the information (such as graphic, text, and sound) that a program presents to a user, and the control sequences the user employs to control the program. A variety of types of user interfaces exist. In one embodiment, user interface122is a graphical user interface. A graphical user interface (GUI) is a type of user interface that allows users to interact with electronic devices, such as a computer keyboard and mouse, through graphical icons and visual indicators, such as secondary notation, as opposed to text-based interfaces, typed command labels, or text navigation. In computing, GUIs were introduced in reaction to the perceived steep learning curve of command-line interfaces which require commands to be typed on the keyboard. The actions in GUIs are often performed through direct manipulation of the graphical elements. In another embodiment, user interface122is a script or application programming interface (API).

Application124is a computer program designed to run on client device120. An application frequently serves to provide a user with similar services accessed on personal computers (e.g., web browser, playing music, or other media, etc.). In one embodiment, application124is a web user interface (WUI) and can display text, documents, web browser windows, user options, application interfaces, and instructions for operation, and include the information (such as graphic, text, and sound) that a program presents to a user and the control sequences the user employs to control the program.

Sensor126is a device, module, machine, or subsystem that is utilized to detect events or changes in an environment and send the information to other electronics. In various embodiments of the present invention sensor126may represent one or more sensors of client device120. In one embodiment, sensor126is an image sensor that detects and conveys information used to make an image. In another embodiment, sensor126is depth/pressure sensor that detects and conveys information used to measure water depth.

For example, a user is using a waterproof digital camera (e.g., client device120) to capture pictures of fish that live in a coral reef. In this example, the waterproof camera includes a barometer (e.g., sensor126) to provide a user with an altitude or depth reading when capturing images. Additionally, the waterproof digital camera includes a client-side application (e.g., application124) of image program200that can be utilized to perform functions/tasks of image program200.

In various embodiments of the present invention, server140may be a desktop computer, a computer server, or any other computer systems, known in the art. In general, server140is representative of any electronic device or combination of electronic devices capable of executing computer readable program instructions. Server140may include components as depicted and described in further detail with respect toFIG. 4, in accordance with embodiments of the present invention.

Server140can be a standalone computing device, a management server, a web server, a mobile computing device, or any other electronic device or computing system capable of receiving, sending, and processing data. In one embodiment, server140can represent a server computing system utilizing multiple computers as a server system, such as in a cloud computing environment. In another embodiment, server140can be a laptop computer, a tablet computer, a netbook computer, a personal computer (PC), a desktop computer, a personal digital assistant (PDA), a smart phone, or any programmable electronic device capable of communicating with client device120and other computing devices (not shown) within distributed data processing environment100via network110. In another embodiment, server140represents a computing system utilizing clustered computers and components (e.g., database server computers, application server computers, etc.) that act as a single pool of seamless resources when accessed within distributed data processing environment100.

Server140includes storage device142, database144, image recognition engine220, and image program200. Storage device142can be implemented with any type of storage device, for example, persistent storage405, which is capable of storing data that may be accessed and utilized by client device120and server140, such as a database server, a hard disk drive, or a flash memory. In one embodiment storage device142can represent multiple storage devices within server140. In various embodiments of the present invention, storage device142stores a plurality of information, such as database144. Database144may represent one or more organized collections of data stored and accessed from server140. For example, database144includes reference images, depth measurements, color absorption information of water, and color temperatures. In one embodiment, data processing environment100can include additional servers (not shown) that host additional information that accessible via network110.

Generally, image program200utilizes image recognition techniques to identify shapes of an underwater image and reconstruct colors of the identified shapes of the underwater image. In one embodiment, image program200retrieves image data from client device120. For example, image program200retrieves an image from client device120and metadata (e.g., depth measurement) of the image from sensor126(e.g., pressure/depth sensor). In this example, image program200utilizes object detection techniques (e.g., machine learning approaches, deep learning approaches, etc.) to detect one or more shapes (e.g., objects) of the image.

In another embodiment, image program200utilizes image recognition engine220and database144to identify detected shapes of an image of client device120. Generally, image recognition engine220is a machine learning algorithm that image program200utilizes to identify features, animals, objects or other targeted subjects. For example, image program200utilizes image recognition engine220to identify a shape of an image of database144(e.g., library of reference images) that matches a detected shape of an image of client device120. Additionally, image program200can utilize features of the detected shape to identify the shape of the image of database144that matches the detected shape of the image of client device120.

In yet another embodiment, image program200utilizes image recognition engine220and database144to determine a color of a detected shape of an image of client device120. For example, image program200utilizes a color of a shape image recognition engine identifies in database144(e.g., library of reference images) to reconstruct a color of a detected shape of an image of client device120. In this example, image program200utilizes a depth measurement, color absorption information, and color temperature corresponding to the detected shape of the image of client device120to generate a color of the detected shape.

FIG. 2is a flowchart depicting operational steps of image program200, a program for color reconstruction of individual detected objects of an underwater image using a library of reference images, in accordance with embodiments of the present invention. In one embodiment, image program200initiates in response to client device120capturing an image. For example, image program200initiates when a digital camera (e.g., client device120) captures a photo. In another embodiment, image program200is continuously monitoring client device120. For example, image program200monitors a digital camera (e.g., client device120) of a user after the user registers the digital camera with image program200.

In step202, image program200captures an image. In one embodiment, image program200retrieves an image of client device120. For example, image program200retrieves an image stored in memory (e.g., computer readable storage, persistent storage, etc.) of a digital camera (e.g., client device120) and stores the image in a memory (e.g., storage device142of server140). In another example, image program200monitors a digital camera (e.g. client device120) to retrieve image data, which may include one or more images, in the field of view of the digital camera (i.e., capturing real-time or near real-time image sensor data of the digital camera).

In step204, image program200records a depth measurement of the image. In one embodiment, image program200retrieves data of sensor126of client device120. For example, image program200retrieves measurement data from a pressure sensor (e.g., sensor126) that indicates a depth in meters that a digital camera (e.g., client device120) is below a surface of a body of water. In another embodiment, image program200extracts depth information from an image of client device120. For example, image program200retrieves an image file of a digital camera (e.g., client device120) and utilizes natural language processing (NLP) techniques (e.g., parsing, word segmentation, lexical semantics, metadata analysis, etc.) to identify a recording corresponding to a depth measurement of the digital camera at the creation of the image file (i.e., at the time an image was taken).

Embodiments of the present invention recognize that generally, underwater photography presents unique challenges due to the properties of water and its effect on light. Water absorbs light in ways that air does not, due to this color absorption underwater, underwater photography requires a means of compensation to restore the colors and contrast loss from absorption. Water absorbs different wavelengths of light to different degrees. The longest wavelengths with the lowest energy are absorbed first. For example, red is the first to be absorbed, followed by orange and yellow (i.e., the colors disappear underwater in the same order as they appear in the color spectrum), and at depths greater than 36 meters (120 ft) the only visible color is hues of blue. In various embodiments of the present invention, image program200utilizes a depth measurement as an input value to reconstruct colors of an object.

FIG. 3Adepicts environment300, which is an example of components of distributed data environment100that include library reference images and a captured underwater image. Environment300includes database144, reference image145, reference image147, reference image149, client device120, and underwater image129. Reference image145is a depiction of a coral reef and a clown goby. Reference image147is a depiction of a coral reef and a yellowtail clownfish. Reference image149is a depiction of a coral reef and a clownfish. Underwater image129is a depiction of an underwater photo that is absent of color due to underwater color absorption. In this example embodiment, reference image145, reference image147, and reference image149are a part of a collection of images of database144. Additionally, underwater image129is an image captured by client device120.

In step206, image program200identifies a shape of the image. In various embodiments of the present invention, image program200utilizes various computer vision techniques to identify objects (e.g., shapes) of an image of client device120. In one embodiment, image program200identifies an object of an image of client device120. For example, image program200uses object detection techniques (e.g., Viola-Jones, scale-invariant feature transform, histogram of oriented gradients, etc.) to detect one or more shapes (e.g., objects) of an image a digital camera (e.g., client device120) captures. In this example, image program200utilizes a trained machine learning model, which may be trained using labeled images of a database (e.g., database144) and a support vector machine (SVM), to detect the one or more shapes of the image of the digital camera. In another example, image program200may utilize a deep learning approach (e.g., regional convolutional neural network, single shot multibox detector, single-shot refinement neural network for object detection, etc.) to identify one or more shapes of an image of a digital camera.

FIG. 3Bdepicts an example of images of components ofFIG. 3Aafter image program200has identified an object the library reference images and the captured underwater image. Environment300includes shape320, shape330, shape340, and shape310, hereinafter shape(s). Shape(s) is a depiction of an object image program200has identified respectively in reference image145, reference image147, reference image149, and underwater image129. In an example embodiment, image program200utilizes an object detection technique (e.g., Viola-Jones object detection framework based on Haar Features) to detect shape310in image129. In this example, image program200identifies shape320, shape330, and shape340based using shape310as an input of image recognition engine220.

In decision step208, image program200determines whether the shape is present in a library. In one embodiment, image program200utilizes image recognition engine220to identify one or more images of database144that include an object that matches an object of an image of client device120. For example, image program200uses a library of reference images of a database (e.g., database144) to create a set of training data to detect a shape (e.g., an object) of an image of a digital camera (e.g., client device120) in the library of the database. Also, the library of reference images includes photos taken by experienced underwater photographers using high quality underwater lighting. Additionally, the training data includes one or more labels to each image of the library in order for image recognition engine220to identify labeled objects that match the shape of the image of the digital camera. In this example, image recognition engine220analyzes the image of the digital camera and provides a probability score on a scale of zero (0) to one (1), where one (1) indicates a high probability that the shape of the image of the digital camera matches a labeled object of one or more images of the library.

If image program200determines that an object of an image of client device120does not match one or more images of database144(decision step208, “NO” branch), then image program200provides feedback to image recognition engine220to improve an image recognition algorithm of image recognition engine220. For example, image program200determines that image recognition engine220does not provide an image of a library of a database with a score greater than a defined threshold value of (0.8) (i.e., no images of the library match a shape of an image of a digital camera), then image program200extracts the image and depth measurements to retrain image recognition engine220(as in step214).

If image program200determines that an object of an image of client device120matches one or more images of database144(decision step208, “YES” branch), then image program200modifies a color of the identified one or more images of database144that match the object of the image of client device120. In various embodiments of the present invention image program200can modify an entire library image or a matching object of the library image. For example, image program200determines that image recognition engine220provided an image of a library of a database with a score greater than a defined threshold value of (0.8), then image program200uses a depth measurement of a digital camera when capturing an image of the digital camera and corresponding water color absorption information to modify the color of the image of the library.

In an example embodiment, image program200utilizes probability scores of image recognition engine220to determine whether shape310matches an object of an image of database144. In this example, image recognition engine220analyzes features of shape310and returns a set of classifiers and scores based on one or more labeled objects of reference image145, reference image147, and reference image149. Image recognition engine220returns a classification of fish and a name of clown for shape310and identifies reference image145, reference image147, and reference image149in database144due to matching labels. Additionally, image recognition engine220compares shape320, shape330, and shape340to shape310to determine a probability (e.g., score) of a match. In this example, image recognition engine220provided a score of (0.5) for shape320due to shape320not having stripes and shape310having a different structure. In addition, image recognition engine220provided a score of (0.85) for shape330due to shape310having a similar structure to shape310and shape320having stripes. As well, image recognition engine220provided a score of (0.9) for shape340due to shape310having a similar structure to shape310and shape340having similarly shaped stripes. As a result, image program200utilizes a defined threshold score (e.g., 0.8) to determine that shape310matches shape330of reference image147and shape340of reference image149based on the score of shape330and shape340being greater than the defined threshold score.

In step210, image program200modifies a color of an object of a library image. In one embodiment, image program200modifies a color of an object of an image of database144that matches an object of an image of client device120. For example, image program200uses measured depth information and an underwater color absorption spectrum histogram to reduce colors of an object of library image. In this example, image program200identifies a depth in meters (e.g., 25 meters) that a shape (e.g., an object) of an image of a digital camera (e.g., client device120) is captured and reduces colors of the object of library image to correspond to color and contrast based on the underwater color absorption spectrum histogram. In an alternative example, image program200can automatically reduce colors of a library image based on an underwater color absorption spectrum histogram and cache the library image to improve performance and reduce the utilization of processing resources.

In an example embodiment, image program200utilizes depth measurement data of client device120when capturing underwater image129and color absorption spectrum histogram to modify colors of reference image145, reference image147, and reference image149to reflect colors similar to underwater image129at an equivalent depth underwater. In this example, image program200identifies that underwater image129was captured at 25 meters below the surface of a body of water. Image program200can adjust the color temperatures of reference image145, reference image147, and reference image149to correspond to a visible spectrum of colors at 25 meters depth based on the color absorption spectrum histogram.

In decision step212, image program200determines whether the shape and color of the object of the library image is a match with the shape of the image. In one embodiment, image program200utilizes image recognition engine220to determine whether a modified object of an image of database144matches an object of an image of client device120. For example, image program200uses image recognition engine220to compare local features of a region of an object of a library image of a database (e.g., database144) and local features of a region of a shape (e.g., an object) of an image of a digital camera (e.g., client device120) to determine whether the modified colors and shape of the object of the library image match the colors and the shape of the image of the digital camera. In an alternative example, image program200provides a match to a user that reviews (e.g., accepts, rejects, etc.) the match of shape and colors of an object of a library image and an image of a digital camera (e.g., client device120).

If image program200determines that a modified object of an image of database144does not match an object of an image of client device120(decision step212, “NO” branch), then image program200provides feedback to image recognition engine220to improve an image recognition algorithm of image recognition engine220. For example, image program200determines that image recognition engine220provided an image of a library of a database (e.g., database144) with a score greater than a defined threshold value of (0.8), but the reduced colors do not match colors of a shape of an image of a digital camera (e.g., client device120), then image program200uses the image of the library used as machine learning training data (e.g., object, depth measurement, color information, etc.) to improve an image recognition algorithm (i.e., image recognition engine220) and searches the library that includes recolored images with an object that matches the shape of the digital camera.

In an example embodiment, image program200utilizes probability scores of image recognition engine220to identify a closest proximate shape and color match with shape310of underwater image129. In this example embodiment, of image recognition engine220compares color information of local features of shape330and shape340to shape310to determine a probability (e.g., score) of a match. In this example, image recognition engine220provided a score of (0.8) for shape330due to shape310having a similar structure to shape310and shape320having a variation in color degradation and stripe shape from shape310. In addition, image recognition engine220provided a score of (0.9) for shape340due to shape310having a similar structure to shape310and shape340having similar shaped stripes and color degradation as shape310.

As a result, image program200compares scores provided by image recognition engine to determine that shape340is a better proximate match to shape310than shape330due to the score (e.g.,0.9) of shape340being greater in value than the score (0.8) of shape330. In an alternative example embodiment, image program200provides shape340(i.e., the better proximate match to shape310) to a user that reviews (e.g., accepts, rejects, etc.) the match of shape and colors of shape340and shape310.

If image program200determines that a modified object of an image of database144matches an object of an image of client device120(decision step212, “YES” branch), then image program200utilizes original data (e.g., lighting data, color data, etc.) of the modified object of the image of the database to reconstruct colors of the object of the image of client device120. For example, image program200determines that image recognition engine220provided an image of a library of a database (e.g., database144) with a score greater than a defined threshold value of (0.8) and the reduced colors match colors of a shape of an image of a digital camera (e.g., client device120), then image program200uses the color information of the original image of the library to generate colors for corresponding features of the shape of the image of the digital camera.

In step214, image program200retrains an image recognition algorithm. In one embodiment, image program200utilizes feedback data to retrain image recognition engine220to identify one or more images of database144that include an object and colors that match an object of an image of client device120. For example, image program200uses depth measurements and color information of rejected matches to create a set of training data to detect a shape (e.g., an object) of an image of a digital camera (e.g., client device120) in the library of the database. Additionally, the training data includes one or more labels to each image of the library in order for image recognition engine220to identify labeled objects and recolored library images that match the shape of the image of the digital camera.

In step216, image program200reconstructs the color of the shape. In one embodiment, image program200generates colors for one or more shapes of an image of client device120. For example, image program200uses a color corresponding to matching local features of a region of an object of a library image of a database (e.g., database144) and local features of a region of a shape (e.g., an object) of an image of a digital camera (e.g., client device120) to reconstruct the colors of the shape of the image of the digital camera that are absent due to color absorption. In this example, image program200generates matches and colors for one or more shapes of the image of the digital camera independently by repeating the steps of image program200until the color of the one or more shapes of the image of the digital camera are reconstructed.

In another embodiment, image program200utilizes one or more images of database144to modify an image of client device120. For example, image program200uses one or more images of a library of a database (e.g., database144) to reconstruct an image of a digital camera (e.g., client device120), which has reduced or absent color information. In this example, image program200utilizes color information (e.g., original color information) of an object of an image of the library of the database determined to match a shape of the image of the digital camera to generate colors for the shape of the image of the digital camera. Additionally, image program200performs one or more iterations of steps of image program200reconstructing the colors of each identified shape of the image of the digital camera. As a result, image program200eliminates the effect of color absorption due to the depth at which the image of the digital camera was captured.

FIG. 4includes processor(s)401, cache403, memory402, persistent storage405, communications unit407, input/output (I/O) interface(s)406, and communications fabric404. Communications fabric404provides communications between cache403, memory402, persistent storage405, communications unit407, and input/output (I/O) interface(s)406. Communications fabric404can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric404can be implemented with one or more buses or a crossbar switch.

Memory402and persistent storage405are computer readable storage media. In this embodiment, memory402includes random access memory (RAM). In general, memory402can include any suitable volatile or non-volatile computer readable storage media. Cache403is a fast memory that enhances the performance of processor(s)401by holding recently accessed data, and data near recently accessed data, from memory402.

Program instructions and data (e.g., software and data410) used to practice embodiments of the present invention may be stored in persistent storage405and in memory402for execution by one or more of the respective processor(s)401via cache403. In an embodiment, persistent storage405includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage405can include a solid state hard drive, a semiconductor storage device, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information.

The media used by persistent storage405may also be removable. For example, a removable hard drive may be used for persistent storage405. Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage405. Software and data410can be stored in persistent storage405for access and/or execution by one or more of the respective processor(s)401via cache403. With respect to client device120, software and data410includes data of user interface122, application124, and sensor126. With respect to server140, software and data410includes data of storage device142, database144, image program200, and image recognition engine220.

Communications unit407, in these examples, provides for communications with other data processing systems or devices. In these examples, communications unit407includes one or more network interface cards. Communications unit407may provide communications through the use of either or both physical and wireless communications links. Program instructions and data (e.g., software and data410) used to practice embodiments of the present invention may be downloaded to persistent storage405through communications unit407.

I/O interface(s)406allows for input and output of data with other devices that may be connected to each computer system. For example, I/O interface(s)406may provide a connection to external device(s)408, such as a keyboard, a keypad, a touch screen, and/or some other suitable input device. External device(s)408can also include portable computer readable storage media, such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Program instructions and data (e.g., software and data410) used to practice embodiments of the present invention can be stored on such portable computer readable storage media and can be loaded onto persistent storage405via I/O interface(s)406. I/O interface(s)406also connect to display409.