Sample based color extraction for augmented reality

A system and method for sampling-based color extraction for augmented reality are described. A viewing device includes an optical sensor to capture an image of a real-world object. A color extraction software divides the captured image into multiple regions or recognizes pre-defined regions and identifies a color value for each region. A color-based augmented reality effect module retrieves a virtual content based on the color values for the regions, and delivers the virtual content in the viewing device.

TECHNICAL FIELD

The subject matter disclosed herein generally relates to the processing and visualization of data. Specifically, the present disclosure addresses systems and methods for extracting sample color values of an image for use in augmented and mixed reality.

BACKGROUND

An augmented reality (AR) device can be used to generate and display data in addition to an image captured with the device. For example, AR provides a live, direct or indirect view of a physical, real-world environment whose elements are augmented by computer-generated sensory input such as sound, video, graphics or GPS data. With the help of advanced AR technology (e.g., adding computer vision and object recognition), the information about the surrounding real world of the user becomes interactive. Device-generated (e.g., artificial) information about the environment and its objects can be overlaid on the real world.

AR devices can also be used to capture color information of an image of a real world physical object. However, when attempting to recognize or identify color values, the device can be overwhelmed with the resulting computational load. The number of color value computations increases with the resolution of the captured image, specificity of the color space being identified, and many other factors. Thus, the AR device may not have sufficient computational resources to extract the color value of every pixel and further render three-dimensional models of virtual objects and complex animations, especially when the computation all happens on the local device.

DETAILED DESCRIPTION

Example methods and systems are directed to sample-based color extraction of an image for an augmented reality (AR) system and using the sampled color values to generate a virtual object in the AR system. Examples merely typify possible variations. Unless explicitly stated otherwise, components and functions are optional and may be combined or subdivided, and operations may vary in sequence or be combined or subdivided. In the following description, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of example embodiments. It will be evident to one skilled in the art, however, that the present subject matter may be practiced without these specific details.

AR applications allow a user to experience information, such as in the form of a virtual object such as a three-dimensional virtual object overlaid on an image of a physical object captured with a camera of a viewing device. The physical object may include a visual reference (e.g., a recognized image, pattern, or object) that the augmented reality application can identify. A visualization of the additional information, such as the three-dimensional virtual object overlaid or engaged with an image of the physical object, is generated in a display of the viewing device. The three-dimensional virtual object may be selected based on the recognized visual reference or captured image of the physical object. A rendering of the visualization of the three-dimensional virtual object may be based on a position of the display relative to the visual reference. Other augmented reality applications allow a user to experience visualization of the additional information overlaid on top of a view or an image of any object in the real physical world. The virtual object may include a three-dimensional or a two-dimensional virtual object. For example, the three-dimensional virtual object may include a three-dimensional model of a toy or an animated dinosaur. The two-dimensional virtual object may include a two-dimensional view of a dialog box, menu, or written information such as statistics information for properties or physical characteristics of the object (e.g., temperature, humidity, color). An image of the virtual object may be rendered at the viewing device or at a server in communication with the viewing device.

A user may view the virtual object visually perceived as an overlay onto the image or a view of the real-world object using a viewing device. The viewing device may include a mobile computing device such as a smartphone, a head mounted display system, computing glasses, and other types of wearable devices. The viewing device may include a system for sample-based color extraction for AR. In one example embodiment, the viewing device includes an optical sensor to capture an image of a real-world object. A sample-based color extraction module of the viewing device breaks down the captured image into multiple regions and identifies a color value for each region, which may be an average, a pass/fail check against a range, or other color related identification or comparison. A color-based AR effect module retrieves or creates virtual content based on the color values for the plurality of regions and characteristics (e.g., specific animation, behavior, color, effect) associated with the virtual content. The viewing device then generates a visualization of the virtual content in a display of the viewing device.

In one example embodiment, the viewing device divides the captured image into multiple regions. Each region includes sample pixel color values within a predefined threshold. The size of each region may be determined based on color value variations within each corresponding region. For example, the size of a region increases when the color value variations exceed an upper threshold. The size of a region decreases when the color value variations fall below a lower threshold.

For example, the viewing device may compute a median color value for each region. For example, the viewing device selects sample pixels within a region and computes the median color value based on the sample pixels within the region.

In one example embodiment, the viewing device retrieves the virtual content corresponding to the color value associated with one or more regions and renders a three-dimensional model of the virtual content in the display of the viewing device. The three-dimensional model is visually perceived in the viewing device as an overlay on top of the captured image or a view of the real world object.

The viewing device may retrieve a characteristic of the virtual content associated with the color value corresponding to one or more regions. The viewing device then renders a visualization of the characteristic of the virtual content in the display of the viewing device. The characteristic includes, for example: an animation or a color of the virtual content, a first animation of a virtual object associated with a first color value of one or more regions, a second animation of the virtual object associated with a second color value of one or more regions, a first color of the virtual object associated with the first color value of one or more regions, and a second color of the virtual object associated with the second color value of one or more regions.

In one example embodiment, the viewing device includes a head-mounted device comprising a transparent display that displays the visualization of the virtual content visually perceived as an overlay to a real-world object.

In another example embodiment, a non-transitory machine-readable storage device may store a set of instructions that, when executed by at least one processor, causes the at least one processor to perform the method operations discussed within the present disclosure.

FIG. 1is a block diagram illustrating an example of a network environment100suitable for implementing an AR system, according to some example embodiments. The network environment100includes a viewing device101and a server110, communicatively coupled to each other via a network108. The viewing device101and the server110may each be implemented in a computer system, in whole or in part, as described below with respect toFIGS. 2 and 5.

The server110may be part of a network-based system. For example, the network-based system includes a cloud-based server system that provides additional information, such as three-dimensional models or other virtual objects and corresponding characteristics, to the viewing device101based on a color value of a region in a captured image.

A user102may utilize the viewing device101to capture a view of a real world physical environment114(e.g., a room, a desk, a hallway) having one or more physical objects (e.g., object A116—such as a piece of paper, a magazine, a child's toy, markings on a floor in a factory) viewed by the user102. The user102may be a human user (e.g., a human being), a machine user (e.g., a computer configured by a software program to interact with the viewing device101), or any suitable combination thereof (e.g., a human assisted by a machine or a machine supervised by a human). The user102is not part of the network environment100, but is associated with the viewing device101and may be a user102of the viewing device101. For example, the viewing device101may be a computing device with a display such as a smartphone, a tablet computer, a wearable computing device (e.g., watch or glasses), or a head-mounted computing device (e.g., helmet). A tablet computer may be held up to view the object A116through a display of the table computer. The computing device may be hand held or may be removably mounted to the head of the user102. In one example, the display may be a screen that displays what is captured with a camera of the viewing device101. In another example, the display of the viewing device101may be transparent or semi-transparent such as in lenses of wearable computing glasses or the visor of a helmet.

The user102may be a user of an AR application in the viewing device101and at the server110. The AR application in the viewing device101may optionally communicate with AR application in the server110to access AR content. The AR application may provide the user102with an augmented experience triggered by identified objects and/or colors of the identified objects in the physical environment114. The augmented experience may be in the form of a virtual object based on color values within the captured image of the real world object A116. In one example embodiment, the nature and behavior of the virtual object may be based on the captured color of the object A116. The nature of the virtual object may refer to the type of virtual object being displayed in the viewing device101. For example, a red color value may be associated with a virtual fire log. A blue color value may be associated with virtual ocean waves. The virtual object may already have predefined behaviors such as flaming or crackling fire from the virtual fire log, or splashing mist from crashing waves. The behavior of the virtual object may refer to how the virtual object is animated or behaves in response a color value. For example, a bright red color value may cause the virtual fire log to burst in flames. A light blue color value may cause the virtual waves to move gently.

In another example, the viewing device101retrieves and displays a virtual object (e.g., a virtual dog) that is associated with a color (e.g., brown) of the object A116(e.g., brown coloring in an outline of a drawing of a dog on a piece of paper). In another example, the virtual object may act or behave in a specific manner based on the color in specific portions of the object A116. For example, a red color on the collar of the dog in the drawing causes the viewing device101to animate the virtual brown dog to jump and bark. A yellow color on the collar of the dog may cause the virtual brown dog to sit and wag his tail.

The physical environment114may include identifiable objects such as a two-dimensional physical object (e.g., a picture of a dog), a three-dimensional physical object (e.g., a toy or an action figure), a location (e.g., at the bottom floor of a house), or any references (e.g., perceived corners of walls or furniture) in the real world physical environment114. For example, the user102may point a camera of the viewing device101to capture an image of real world object (e.g., object A116).

In one example embodiment, the objects in the image are tracked and recognized locally in the viewing device101using a local context recognition dataset or any other previously stored dataset of the augmented reality application of the viewing device101. The objects in the image may be recognized patterns on a drawing (e.g., dogs, characters, scenery). The local context recognition dataset module may include a library of virtual objects associated with real-world physical objects or references. In one example, the viewing device101identifies feature points in an image of the object A116to determine different planes (e.g., edges, corners, surface). The viewing device101also identifies tracking data related to the object A116(e.g., GPS location, orientation and position of the object A116relative to the viewing device101, etc.). In another example embodiment, if the captured image is not recognized locally at the viewing device101, the viewing device101downloads additional information (e.g., the three-dimensional model) corresponding to the captured image from a database of the server110over the network108.

In another example embodiment, the object A116in the captured image is tracked and recognized remotely at the server110using a remote context recognition dataset or any other previously stored dataset of an AR application at the server110. The remote context recognition dataset module may include, for example, a library of virtual objects and characteristics associated with detected colors of the object A116. For example, the viewing device101may have a limited library of a context recognition dataset. If the viewing device101does not recognize a pattern or a drawing, the viewing device101sends an image of the drawing to the server110to determine a new virtual object associated with a portion of the image of the drawing. The viewing device101then downloads the new virtual object from the server110. In another example, the viewing device101recognizes the object A116and queries the server110for updates to virtual objects associated with the object A116. For example, the viewing device101recognizes a colored drawing of a cartoon character and queries the server110for additional effects related to the cartoon character. The viewing device101determines that a new power (e.g., firing a laser gun) is available for the cartoon character and downloads the updated virtual object (e.g., 3D model of a virtual character firing a laser gun) or new feature for the virtual object. Other features may include additional accessories for the cartoon character. For example, a new dress or a tiara is available for the virtual character associated with the colored cartoon character.

As such, the nature of a virtual object (e.g., a three-dimensional model of a truck) may be determined based on a combination of the recognized object A116(e.g., a drawing of a truck) and the color of the object A116(e.g., the truck is red). Furthermore, the behavior of the virtual object (e.g., music, sound, and animation of a steam train engine) may be determined based on a combination of the recognized object A116(e.g., a drawing of a steam train) and the color of the object A116(e.g., the train is blue).

In another example embodiment, the viewing device101includes sensors to measure physical properties of the object A116. Examples of measured physical properties may include and but are not limited to color, shades, weight, pressure, temperature, velocity, direction, position, intrinsic and extrinsic properties, acceleration, and dimensions. The sensors may also be used to track the location, movement, and orientation of the viewing device101. The sensors may include optical sensors (e.g., depth-enabled 3D camera), wireless sensors (Bluetooth, Wi-Fi), GPS sensor, and audio sensor to determine the location of the viewing device101, the orientation of the viewing device101to track what the user102is looking at (e.g., a direction at which the viewing device101is pointed, e.g., the viewing device101is pointed towards a drawing on a wall or on a table, markings on a floor). The sensors may be embedded in a head-mounted device.

In another example embodiment, data from the internal sensors in the viewing device101may be used for analytics data processing at the server110(or another server) for analysis on usage and how the user102is interacting with the physical environment114. Live data from other servers may also be used in the analytics data processing. For example, the analytics data may track at what locations (e.g., points or features) on the physical or virtual object the user102has looked, how long the user102has looked at each location on the physical or virtual object, how the user102held the viewing device101when looking at the physical or virtual object, with which features of the virtual object the user102interacted (e.g., such as whether a user102tapped on a part of the virtual object—a user pets a virtual dog on the head), and any suitable combination thereof. The tracking may be performed by tracking the position of the viewing device101relative to the object A116, or by using front cameras in the viewing device101to track an eye gaze of the user102.

The viewing device101may be determine and quantify resources available to capture color values from different regions in an image. The viewing device101may offload computation to the server110based on the available resources at the viewing device101. Specific computations may be allocated between the viewing device101and the server110in real time based on available resources at each device and changing network conditions (e.g., limited bandwidth).

The network108may be any network that enables communication between or among machines (e.g., server110), databases, and devices (e.g., device101). Accordingly, the network108may be a wired network, a wireless network (e.g., a mobile or cellular network), or any suitable combination thereof. The network108may include one or more portions that constitute a private network, a public network (e.g., the Internet), or any suitable combination thereof.

FIG. 2is a block diagram illustrating modules (e.g., components) of the viewing device101, according to some example embodiments. The viewing device101may include sensors202, a display204, a processor206, and a storage device208. For example, the viewing device101may be a wearable computing device (e.g., glasses or a helmet), a desktop computer, a vehicle computer, a tablet computer, a navigational device, a portable media device, or a smart phone of a user. The user may be a human user (e.g., a human being), a machine user (e.g., a computer configured by a software program to interact with the viewing device101), or any suitable combination thereof (e.g., a human assisted by a machine or a machine supervised by a human).

The sensors202may include, for example, a proximity or location sensor (e.g., Near Field Communication, GPS, Bluetooth, Wi-Fi), an optical sensor (e.g., a camera), an orientation sensor (e.g., a gyroscope), an audio sensor (e.g., a microphone), or any suitable combination thereof. For example, the sensors202may include a rear-facing camera and a front-facing camera in the viewing device101. It is noted that the sensors202described herein are for illustration purposes; the sensors202are thus not limited to the ones described. The sensors202may be used to generate internal tracking data of the viewing device101to determine what the viewing device101is capturing or looking at in the real physical world.

The display204may include, for example, a touchscreen display configured to receive a user input via a contact on the touchscreen display. In one example, the display204may include a screen or monitor configured to display images generated by the processor206. In another example, the display204may be transparent or semi-opaque so that the user102can see through the display204(e.g., a Head-Up Display).

The processor206may include an AR application216for capturing an image of a real world physical object (e.g., object A116) and for generating a display of a virtual object in the display204of the viewing device101corresponding to a color of the captured image of the object A116. In one example embodiment, the AR application216may include a recognition module214, a sample-based color extraction module218, and a color-based AR effect module220.

The recognition module214identifies the object that the viewing device101is pointed to. The recognition module214may detect, generate, and identify identifiers such as feature points of the physical object being viewed or pointed at by the viewing device101using an optical device of the viewing device101to capture the image of the physical object. As such, the recognition module214may be configured to identify one or more physical objects. The identification of the object may be performed in many different ways. For example, the recognition module214may determine feature points of the object based on several image frames of the object. The recognition module214also determines the identity of the object using any visual recognition algorithm. In another example, a unique identifier may be associated with the object. The unique identifier may be a unique wireless signal or a unique visual pattern such that the recognition module214can look up the identity of the object based on the unique identifier from a local or remote content database. In another example embodiment, the recognition module214includes a facial recognition algorithm to determine an identity of a subject or an object.

Furthermore, the recognition module214may be configured to determine whether the captured image matches an image locally stored in a local database of images and corresponding additional information (e.g., three-dimensional model and interactive features) on the viewing device101. In one embodiment, the recognition module214retrieves a primary content dataset from the server110, and generates and updates a contextual content dataset based on an image captured with the viewing device101.

The sample-based color extraction module218captures an image of a real world object with the optical sensor, and divides or breaks down the captured image into multiple regions. The sample-based color extraction module218then identifies a color value for each region. As such, instead of determining the color value of every pixel in a captured image, the sample-based color extraction module218extracts color values of sample pixels from a region to assign a color value to the corresponding region. In one example embodiment, the sample-based color extraction module218includes a region module302and a color sampling module304as illustrated inFIG. 3.

The region module302divides the captured image into multiple regions where each region includes sample pixel color values within a predefined threshold. For example, a predefined threshold may include a range of color values within which sample pixel color values fall in the region. As such, the higher the range of color values, the higher the diversity of colors (e.g., light blue to dark blue). Furthermore, the region module302may adjust the shape and size of a region based on color value variations within the corresponding region. For example, a user uses crayons to color inside an outline of a dog. The region may be defined as different shades of brown color within the outline of the dog. In another example, the size of the region may be adjusted so that the color variation of color values within the corresponding region falls within the predefined thresholds. For example, the viewing device101captures a drawing of a colored brown dog sitting on green grass. The region module302divides regions in the drawing according to the green and brown color. Because the color variation between the green color and brown color exceeds the predefined threshold, the region module302does not create a region that includes both green and brown color.

In another example, the region module302adjusts the shape and size of a region based on color value variations in the surrounding regions. For example, a red round button may stand out against a grey background of a machine. An LED readout of gauge may include different colors different from the surrounding regions (e.g., black frame). As such, the region module302adjusts the shape of the region to the size of the red button or the LED readout gauge. The region module302may also adjust the shape and size of a region based on the behavior of colors within a region. For example, the size of the region may be adjusted based on the color, the lack of color, the brightness of the color, and the state of the color (e.g., blinking vs non-blinking).

In another example embodiment, the region module302increases the size of the region in response to the color value variations exceeding an upper threshold value. Similarly, the region module302decreases the size of the region in response to the color value variations falling below a lower threshold value. For example, the region module302increases the size of the region around the head of the brown dog to include the entire body of the dog if the dog is entirely brown.

In one example embodiment, the color sampling module304computes a median color value for each region. For example, the color sampling module304determines a number of color value pixels for a sampled region as defined by the region module302. A larger region may include a larger number of sampled color value pixels than a smaller region. The number of samples corresponding to a size of a region may be predefined. For example, the number of samples may be a function of the size and shape of a region. The region module302may include a table of the number of pixels and corresponding region sizes. The region module302then determines the median color value of the sampled pixels in a corresponding region. Other statistical methods may be used to sample and determine a color value of the sampled pixels. The region module302then assigns the median color value to the corresponding region.

In another example embodiment, the color sampling module304randomly selects a predefined number of sample pixels within a region. The color sampling module304computes the median color value based on the sample pixels within the region. The region module302then assigns the computed median color value to the corresponding region. As such, the color sampling module304reduces the processing requirement on the processor206of the viewing device101by avoiding computation to extract color values from every pixel in the captured image of the object A116.

Referring back toFIG. 2, the color-based AR effect module220retrieves a virtual content based on the computed sample color values of one or more of regions in the capture image and generates an effect or a visualization based on the computed sample color values. For example, the region module302determines that the region includes an outline of a drawing of a car on a piece of paper. The color sampling module304determines a number of sample pixels to sample within the drawing of the car, extracts the color value of the number of sample pixels in the drawing of the car, and computes a median color value for the drawing of the car. The color-based AR effect module220retrieves a virtual three-dimensional model of a car with colors based on the computed sample color values in the drawing of the car. In one example, the color-based AR effect module220retrieves a virtual three-dimensional model of a sports car based on red colors in the drawing of the car. In another example, the color-based AR effect module220retrieves a virtual three-dimensional model of a police car based on white and black colors in the drawing of the car. As such, the color-based AR effect module220retrieves virtual content based on a combination of the recognized object and the color of the recognized object. In one example embodiment, the color-based AR effect module220includes an AR content module402and an AR visualization module404as illustrated inFIG. 4.

The AR content module402retrieves a characteristic of the virtual content associated with the color value corresponding to one or more regions. For example, the characteristic includes an effect, an animation, a behavior, or a color of the virtual content. For example, a three-dimensional model of a raining virtual cloud is associated with a grey color of a region defined with an outline of a cloud in a drawing. In another example, a three-dimensional model of a puffy white virtual cloud is associated with a white color of a region defined with an outline of the cloud in the drawing. The AR content module402may associate other colors with other characteristics. For example, a red color of a recognized drawing of a cartoon character may be associated with a three-dimensional model of the same cartoon character jumping around or dancing.

In another example, the AR content module402identifies green markings on a floor of a factory. The AR content module402determines that the green markings correspond to pedestrian traffic in the factory and retrieves a three-dimensional model of arrows visually perceived as floating above the ground to guide the user to walk through the factory along the markings on the floor. The AR content module402determines that the orange markings on the floor correspond to machine-operated traffic in the factory and retrieves a three-dimensional model of a warning sign or caution sign visually perceived as floating above the green markings on the ground to guide the user to prevent walking in those areas. As such, different colors may be associated with different behaviors and animations of the corresponding virtual object.

The AR visualization module404generates or modifies a visualization of the virtual content in the captured image of the real-world object. For example, the AR visualization module404renders a three-dimensional model of the virtual content in the display204of the viewing device101. The user102of the viewing device101visually perceives the three-dimensional model as an overlay on top of the captured image. For example, the user102may visually perceive a virtual dog sitting on top of a dog house. The viewing device101may display the three-dimensional model via a transparent or non-transparent display.

In another example embodiment, the AR visualization module404renders a visualization of the characteristic of the virtual content in the display204of the viewing device101. For example, the AR visualization module404displays a three-dimensional model of a virtual dog associated with a drawing of a dog. The AR visualization module404further animates the three-dimensional model of the dog based on the color of the dog in the drawing. In another example, the AR visualization module404animates the three-dimensional model of the dog based on the color of a specific area in the drawing of the dog. For example, red paws cause the virtual three-dimensional dog to jump around. In another example, a predefined area in the drawing may be dedicated to the characteristic of the three-dimensional virtual model. The drawing may include a predefined box in a lower corner for the user to color. The color in the predefined box defines the characteristic of the three-dimensional virtual model. In other embodiments, the content and characteristic of the three-dimensional virtual model may be a function of data from sensors202of the viewing device101. For example, if one of the sensors202indicates a temperature of 40 degrees Fahrenheit at a specific location in a factory, the AR visualization module404generates a visualization of fast moving exit arrows correlated to the green pedestrian markings on the floor of the factory. As such, the nature and characteristics of the virtual content generated or accessed may be a function of a combination of a recognized object, a color of the recognized object, and data from sensors202of the viewing device101.

In one example embodiment, the AR visualization module404receives data from the server110to render the visualization. In another example embodiment, the AR visualization module404receives the rendered object. The AR visualization module404further determines the position and size of the rendered object to be displayed in relation to an image of the object. For example, the AR visualization module404places a virtual three-dimensional model of an animated heart with the size and position based on the image of the subject such that the animated heart is displayed on the chest area of the subject with the appropriate size. If the subject is wearing a red T shirt, the virtual three-dimensional model of an animated heart may be moving at a faster pace than a subject wearing a darker T shirt. The AR visualization module404may track the image of the subject and render the virtual object based on the position of the image of the subject in a display204of the viewing device101.

The viewing device101may access from a local memory a visualization model (e.g., vector shapes) corresponding to the image of the object (e.g., bridge). In another example, the viewing device101receives a visualization model corresponding to the image of the object from the server110. The viewing device101then renders the visualization model to be displayed in relation to an image of the object being displayed in the viewing device101or in relation to a position and orientation of the viewing device101relative to the object. The AR application216may adjust a position of the rendered visualization model in the display204to correspond with the last tracked position of the object.

The AR visualization module404may include a local rendering engine that generates a visualization of a three-dimensional virtual object overlaid (e.g., superimposed upon, or otherwise displayed in tandem with) on an image of a physical object captured by a camera of the viewing device101in the display204of the viewing device101. A visualization of the three-dimensional virtual object may be manipulated by adjusting a position of the physical object (e.g., its physical location, orientation, or both) relative to the camera of the viewing device101. Similarly, the visualization of the three-dimensional virtual object may be manipulated by adjusting a position of the camera of the viewing device101relative to the physical object.

In one example embodiment, the AR visualization module404may retrieve three-dimensional models of virtual objects associated with a captured image of a real-world object. For example, the captured image may include a visual reference (also referred to as a marker) that consists of an identifiable image, symbol, letter, number, machine-readable code. For example, the visual reference may include a bar code, a quick response (QR) code, a pattern, or an image that has been previously associated with a three-dimensional virtual object (e.g., an image that has been previously determined to correspond to the three-dimensional virtual object).

In one example embodiment, the AR visualization module404identifies the physical object (e.g., a physical telephone), accesses virtual functions (e.g., increase or lower the volume of a nearby television) associated with physical manipulations (e.g., lifting a physical telephone handset) of the physical object, and generates a virtual function corresponding to a physical manipulation of the physical object.

The storage device208may be configured to store a database of identifiers of physical objects, tracking data, and corresponding virtual objects having colors and characteristics a function of a color of a recognized physical object. In another embodiment, the database may also include visual references (e.g., images) and corresponding experiences (e.g., three-dimensional virtual objects, interactive features of the three-dimensional virtual objects, animations of the three-dimensional virtual objects, characteristics of the three-dimensional virtual objects). For example, the visual reference may include a machine-readable code or a previously identified image (e.g., a picture of a superhero character). The previously identified image of the superhero character may correspond to a three-dimensional virtual model of the superhero character that can be viewed from different angles by manipulating the position of the viewing device101relative to the picture of the shoe. Features or powers of the three-dimensional virtual superhero character may be displayed based on the detected sample color values of a real-world object.

In one embodiment, the storage device208includes a primary content dataset, a contextual content dataset, and a visualization content dataset. The primary content dataset includes, for example, a first set of images and corresponding experiences (e.g., interaction with three-dimensional virtual object models). For example, an image may be associated with one or more virtual object models. The primary content dataset may include a core set of images or the most popular images determined by the server110. The core set of images may include a limited number of images identified by the server110. For example, the core set of images may include the images depicting covers of the ten most popular drawings or cartoons and their corresponding experiences (e.g., virtual objects that represent the ten most drawings or cartoons). In another example, the server110may generate the first set of images based on the most popular or often scanned images received at the server110. Thus, the primary content dataset does not depend on objects or images scanned by the recognition module214of the viewing device101.

The contextual content dataset includes, for example, a second set of images and corresponding experiences (e.g., three-dimensional virtual object models) retrieved from the server110. For example, images captured with the viewing device101that are not recognized (e.g., by the server110) in the primary content dataset are submitted to the server110for recognition. If the captured image is recognized by the server110, a corresponding experience may be downloaded at the viewing device101and stored in the contextual content dataset. Thus, the contextual content dataset relies on the context in which the viewing device101has been used. As such, the contextual content dataset depends on objects or images scanned by the recognition module214of the viewing device101.

In one embodiment, the viewing device101may communicate over the network108with the server110to retrieve a portion of a database of visual references, corresponding three-dimensional virtual objects, and corresponding features of the three-dimensional virtual objects. The network108may be any network that enables communication between or among machines, databases, and devices (e.g., the viewing device101).

Any one or more of the modules described herein may be implemented using hardware (e.g., a processor of a machine) or a combination of hardware and software. For example, any module described herein may configure a processor to perform the operations described herein for that module. Moreover, any two or more of these modules may be combined into a single module, and the functions described herein for a single module may be subdivided among multiple modules. Furthermore, according to various example embodiments, modules described herein as being implemented within a single machine, database, or device may be distributed across multiple machines, databases, or devices.

FIG. 5is a block diagram illustrating modules (e.g., components) of the server110. The server110includes a processor502and a database510. The processor502includes a server recognition module504, a server sample-based color extraction module506, and a server color-based AR effect module508. The server recognition module504operates in a similar way to the recognition module214of the viewing device101. For example, the server recognition module504identifies the object A116based on a captured image received from the viewing device101. In another example, the viewing device101already has identified the object A116and provides the identification information to the server recognition module504.

The server sample-based color extraction module506also operates in a similar way as the sample-based color extraction module218of the viewing device101. For example, the server sample-based color extraction module506divides or breaks down the received image from the viewing device101into multiple regions. The sample-based color extraction module218then identifies a color value for each region. As such, instead of determining the color value of every pixel in a captured image, the sample-based color extraction module218extracts color values of sample pixels from a region to assign a color value to the corresponding region.

The server color-based AR effect module508also operates in a similar way as the color-based AR effect module220. For example, the server color-based AR effect module508retrieves a virtual content based on the computed sample color values of one or more of regions in the received image from the viewing device101and generates an AR effect based on the computed sample color values.

The database510may store a color and object dataset512, a virtual content dataset514, and characteristics of virtual content dataset516. The color and object dataset512may store a primary content dataset and a contextual content dataset. The primary content dataset comprises a first set of images, colors, and corresponding virtual object models. The server recognition module504determines that a color and/or a captured image received from the viewing device101is not recognized in the color and object dataset512, and generates the contextual content dataset for the viewing device101. The contextual content dataset may include a second set of colors and images and corresponding virtual object models. The virtual content dataset514includes models of virtual objects (e.g., a three-dimensional model of an object) to be generated upon receiving a notification associated with an image of a corresponding physical object. The characteristics of virtual content dataset516include a table of identified objects and/or colors with characteristics or behaviors (e.g., animation, effects, sound, music, etc.) that correspond to the sample color values from the captured image.

FIG. 6is an interaction diagram illustrating an example embodiment of a system for sample-based color extraction in an AR application of the viewing device101and the server110. At operation602, the viewing device101captures a picture of the object A116in a physical environment114. The viewing device101optionally tracks data related to the objects being captured by the viewing device101. For example, sensors202may be used in tracking a temperature or location of the object A116. The viewing device101breaks up the picture of the object A116into regions and identifies sample color values from the regions. In one example embodiment, operation602may be implemented using the recognition module214, the sample-based color extraction module218, and the color-based AR effect module220.

At operation604, the viewing device101communicates the sample color value for one or more regions to the server110. At operation606, the server110retrieves a model of a virtual object associated with the sample color values received from the viewing device101. In one example embodiment, operation606may be implemented using the server color-based AR effect module508ofFIG. 5.

At operation608, the server110communicates the virtual object model data back to the viewing device101. At operation610, the viewing device101generates a visualization of the virtual object overlaid on corresponding sample color regions. In one example embodiment, operation610may be implemented using the AR visualization module406ofFIG. 4.

FIG. 7is an interaction diagram illustrating another example embodiment of a system for sample-based color extraction in an AR application of the viewing device101and the server110. At operation702, the viewing device101captures a live image of the object A116in a physical environment114. At operation703, the viewing device101sends the live image of the object A116to the server110for processing. At operation704, the server110breaks up the live picture of the object A116into regions and identifies sample color values from the regions using, for example, the server sample-based color extraction module506ofFIG. 5.

At operation706, the server110retrieves a model of a virtual object associated with the sample color values previously determined at operation704. In one example embodiment, operation706may be implemented using the server color-based AR effect module508ofFIG. 5.

At operation708, the server110communicates the virtual object model data for one or more regions back to the viewing device101. At operation710, the viewing device101generates a visualization of the virtual object overlaid on corresponding sample color regions. In one example embodiment, operation710may be implemented using the AR visualization module406ofFIG. 4.

FIG. 8is a flowchart of a method illustrating an example operation of sample-based color extraction for an AR system. At operation802, the viewing device101captures an image of an object.

At operation804, the viewing device101divides the image into one or more regions based on the color variations for each region. In one example embodiment, operation804may be implemented using the sample-based color extraction module218ofFIG. 2.

The viewing device101may determine a sample color value from each region (see operation806). In one example embodiment, operation806may be implemented using the sample-based color extraction module218ofFIG. 2.

Thereafter, as shown at operation808, the viewing device101generates a model of an AR object overlaid on an image of the object based on a sample color value from each region. In one example embodiment, operation808may be implemented using the color-based AR effect module220ofFIG. 2.

FIG. 9is a flowchart of a method illustrating an example operation of sample-based color extraction for an AR system. At operation902, the viewing device101identifies a dominant color for each region of a captured image. A dominant color may be determined based on the number of pixels of the same color exceeding the number of pixels of different colors. In one example embodiment, operation902may be implemented using the region module302ofFIG. 3.

At operation904, the viewing device101identifies regions with similar dominant colors. In one example embodiment, operation902may be implemented using the region module302ofFIG. 3.

As shown at operation906, the viewing device101determines a model of an AR object for the regions with similar dominant colors. In one example embodiment, operation906may be implemented using the AR content module402ofFIG. 4.

At operation908, the viewing device101generates an AR object overlaid on regions with similar dominant colors in the captured image. In one example embodiment, operation908may be implemented using the AR visualization module404ofFIG. 2.

FIG. 10is a flowchart of a method illustrating an example operation of sample-based color extraction for an AR system. At operation1002, the viewing device101divides an image into regions. At operation1004, the viewing device101identifies color value variations within each region. At operation1006, the viewing device101adjusts the size of a region if the color value variation exceeds a threshold value. In one example embodiment, operation1006may be implemented using the region module302ofFIG. 3.

At operation1008, the viewing device101generates an AR object overlaid on regions with the corresponding regions in the captured image. In one example embodiment, operation1008may be implemented using the AR visualization module404ofFIG. 2.

FIG. 11is a flowchart of a method illustrating an example operation of sample-based color extraction for an AR system. At operation1102, the viewing device101identifies a sample color value for a region. At operation1104, the viewing device101adjusts a characteristic of an AR object based on the sample color value in the region. In one example embodiment, operation1104may be implemented using the region module302ofFIG. 3.

FIG. 12is a diagram illustrating an example operation of sample-based color extraction for an AR system. The viewing device101includes a handheld mobile device having a rear view camera1202and a touch sensitive display1204. The viewing device101may be pointed at a real-world scene comprising a colored drawing on a paper1208. The rear view camera1202captures an image of the paper1208and displays a picture1206of the paper1208in the display1204. Optionally, identifiers (e.g., QR code, or specific patterns) and tracking data related to the paper1208may be recognized by the viewing device101based on the picture1206so as to identify the drawing on the paper1208. For example, markings on the paper1208are associated with a three-dimensional model of a virtual car1205.

In one example embodiment, the viewing device101divides the picture1206of the paper1208into regions1210,1212,1214,1216,1218,1220,1222,1224, and1226. The viewing device101identifies a sample color value for each region1210-1226and determines the dominant color from all regions1210-1226using the sample-based color extraction module218. The viewing device101then adjusts a color or another characteristic of the virtual car1205based on the dominant color from the regions1210-1226on the paper1208.

In another example embodiment, the viewing device101communicates an identification of the identified paper1208to the server110. The server110retrieves or generates a three-dimensional model of a virtual object associated with the paper1208. The server110divides the image of the paper1208into regions1210-1226and extracts sample color values from each region1210-1226. The server110then adjusts a color or a behavior of the three-dimensional model based on the extracted color values from the corresponding regions1210-1226. The server110then communicates the colored three-dimensional model back to the viewing device101. The viewing device101generates a visualization of the colored three-dimensional model of a virtual object. For example, the visualization may include the virtual car1205with the color extracted from a region of the paper1208.

FIG. 13Ais a diagram illustrating an example operation of sample-based color extraction for an AR system. The viewing device101may be pointed at a colored drawing on a paper1308. The rear view camera1202captures an image of the paper1308and displays an image1302of the paper1308in the display1204.

In one example embodiment, the viewing device101identifies a sample color value for each region using the sample-based color extraction module218. For example, the viewing device101determines the sample color value for region1310and applies the sample color value to the virtual car1304. Similarly, the viewing device101determines the sample color value for region1312and applies the sample color value to the virtual car1306.

The viewing device101generates a visualization of the colored three-dimensional model of the virtual car1304at a location in the image1302of the paper1308corresponding to the region1310. Similarly, the viewing device101generates a visualization of the colored three-dimensional model of the virtual car1306at a location in the image1302of the paper1308corresponding to the region1312.

FIG. 13Bis a diagram illustrating an example operation of sample-based color extraction for an AR system. The viewing device101determines the sample color value for a larger region1311(i.e., larger than region1310) and applies the sample color value to the virtual car1304. Similarly, the viewing device101determines the sample color value for region1312and applies the sample color value to the virtual car1306.

However, the region1311is larger than region1310with the same color value, the viewing device101generates a visualization of the colored virtual car1304to be larger to correspond with the larger region1311. The viewing device101generates a visualization of the colored three-dimensional model of the virtual car1304at a location in the image1302of the paper1308corresponding to the region1311. The viewing device101generates a visualization of the colored three-dimensional model of the virtual car1306with a relative size corresponding to the size of the region1312.

FIG. 13Cis a diagram illustrating an example operation of sample-based color extraction for an AR system. The viewing device101determines the sample color value for a region1314adjacent to the larger region1311and region1312. The viewing device101determines that the sample color value corresponds to a special effect (e.g., an explosion). As such, the viewing device101generates an animation of the virtual car1304colliding with the virtual car1306with an explosion animation1307at the location corresponding to the region1314in the image1302.

FIG. 14is a diagram illustrating an example operation of sample-based color extraction for an AR system. The viewing device101determines that the sample color value is the same for region1410from paper1408. The viewing device101determines that the sample color value in region1410corresponds to a moving train, and the sample color value in region1412corresponds to a train stop. As such, the viewing device101generates an animation of a virtual train1404moving along a track1406defined by the path and size of region1410. The virtual train1404stops at a location1407corresponding to the relative location of the region1412on the paper1408.

Modules, Components and Logic

Electronic Apparatus and System

Example Machine Architecture and Machine-Readable Medium

The example computer system1500includes a processor1502(e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory1504and a static memory1506, which communicate with each other via a bus1508. The computer system1500may further include a video display unit1510(e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system1500also includes an alphanumeric input device1512(e.g., a keyboard), a user interface (UI) navigation (or cursor control) device1514(e.g., a mouse), a disk drive unit1516, a signal generation device1518(e.g., a speaker) and a network interface device1520.

The disk drive unit1516includes a computer-readable medium1522on which is stored one or more sets of data structures and instructions1524(e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions1524may also reside, completely or at least partially, within the main memory1504and/or within the processor1502during execution thereof by the computer system1500, the main memory1504and the processor1502also constituting machine-readable media. The instructions1524may also reside, completely or at least partially, within the static memory1506.

Transmission Medium

Example Mobile Device

FIG. 16is a block diagram illustrating a mobile device1600, according to an example embodiment. The mobile device1600may include a processor1602. The processor1602may be any of a variety of different types of commercially available processors1602suitable for mobile devices1600(for example, an XScale architecture microprocessor, a microprocessor without interlocked pipeline stages (MIPS) architecture processor, or another type of processor1602). A memory1604, such as a random access memory (RAM), a flash memory, or other type of memory, is typically accessible to the processor1602. The memory1604may be adapted to store an operating system (OS)1606, as well as application programs1608, such as the AR application216. The processor1602may be coupled, either directly or via appropriate intermediary hardware, to a display1610and to one or more input/output (I/O) devices1612, such as a keypad, a touch panel sensor, a microphone, and the like. Similarly, in some embodiments, the processor1602may be coupled to a transceiver1614that interfaces with an antenna1616. The transceiver1614may be configured to both transmit and receive cellular network signals, wireless data signals, or other types of signals via the antenna1616, depending on the nature of the mobile device1600. Further, in some configurations, a GPS receiver1618may also make use of the antenna1616to receive GPS signals.