US20170103581A1 - Visualization of physical interactions in augmented reality - Google Patents

Visualization of physical interactions in augmented reality Download PDF

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US20170103581A1
US20170103581A1 US15/389,095 US201615389095A US2017103581A1 US 20170103581 A1 US20170103581 A1 US 20170103581A1 US 201615389095 A US201615389095 A US 201615389095A US 2017103581 A1 US2017103581 A1 US 2017103581A1
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image
area
viewing
physical
objects
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US15/389,095
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Brian Mullins
Matthew Kammerait
Christopher Broaddus
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RPX Corp
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Daqri LLC
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Priority to US14/145,567 priority Critical patent/US9626801B2/en
Priority to US14/461,257 priority patent/US9558592B2/en
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Priority to US15/389,095 priority patent/US20170103581A1/en
Assigned to DAQRI, LLC reassignment DAQRI, LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: BROADDUS, Christopher, KAMMERAIT, MATTHEW, MULLINS, BRIAN
Publication of US20170103581A1 publication Critical patent/US20170103581A1/en
Assigned to AR HOLDINGS I LLC reassignment AR HOLDINGS I LLC SECURITY INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: DAQRI, LLC
Assigned to RPX CORPORATION reassignment RPX CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: DAQRI, LLC
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T19/00Manipulating 3D models or images for computer graphics
    • G06T19/006Mixed reality
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • G06K9/4652Extraction of features or characteristics of the image related to colour
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • G06K9/4671Extracting features based on salient regional features, e.g. Scale Invariant Feature Transform [SIFT] keypoints
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • G06K9/52Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/64Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix
    • G06K9/66Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2210/00Indexing scheme for image generation or computer graphics
    • G06T2210/21Collision detection, intersection

Abstract

An imaging device of a device captures an image. The device identifies a first and second object in the image, determines physical properties of the first and second object, generates a model of a predicted interaction between the first and second object based on the physical properties of the first and second object, identifies an area in the image based on the model of the predicted interaction between the first and second object, and augments the image at the area in a display of the device.

Description

    PRIORITY APPLICATION
  • This application in a continuation of U.S. patent application Ser. No. 14/461,257, filed on Aug. 15, 2014, which is a continuation-in-part of U.S. patent application Ser. No. 14/145,567, filed on Dec. 31, 2013, the disclosures of which are incorporated herein in their entireties by reference.
  • TECHNICAL FIELD
  • The subject matter disclosed herein generally relates to the processing of data. Specifically, the present disclosure addresses systems and methods for visualization of physical interactions in augmented reality.
  • BACKGROUND
  • A device can be used to generate and display data in addition to an image captured with the device. For example, augmented reality (AR) is 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.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings.
  • FIG. 1 is a block diagram illustrating an example of a network suitable for visualizing predicted interactions between physical objects, according to some example embodiments.
  • FIG. 2 is a block diagram illustrating an example embodiment of modules (e.g., components) of a viewing device.
  • FIG. 3 is a block diagram illustrating an example embodiment of modules of a recognition module.
  • FIG. 4 is a block diagram illustrating an example embodiment of modules of a physical characteristics module.
  • FIG. 5 is a block diagram illustrating an example embodiment of modules of a server.
  • FIG. 6 is a ladder diagram illustrating an example embodiment of an operation of object interactions visualization.
  • FIG. 7 is a ladder diagram illustrating another example embodiment of an operation of object interactions visualization.
  • FIG. 8 is a flowchart illustrating an example operation of generating visualization of physical characteristics of real world objects in an augmented reality application.
  • FIG. 9 is a flowchart illustrating an example operation of generating visualization of object interactions in an augmented reality application.
  • FIG. 10 is a flowchart illustrating an example operation of visualization of static and dynamic objects interactions in an augmented reality application.
  • FIG. 11 is a flowchart illustrating an example operation of visualization of dynamic objects interactions in an augmented reality application.
  • FIG. 12 is a diagram illustrating an example operation of generating a visualization of dynamic and static object interactions in an augmented reality application.
  • FIG. 13 is a diagram illustrating another example operation of generating a visualization of dynamic object interactions in an augmented reality application.
  • FIG. 14 is a block diagram illustrating components of a machine, according to some example embodiments, able to read instructions from a machine-readable medium and perform any one or more of the methodologies discussed herein.
  • FIG. 15 is a block diagram illustrating a mobile device, according to an example embodiment.
  • DETAILED DESCRIPTION
  • Example methods and systems are directed to visualization of a physics model in augmented reality based on real world object dynamics. 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.
  • Augmented reality 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 virtual object, a two-dimensional virtual object. For example, the three-dimensional virtual object may include a three-dimensional view of a chair 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, mass, velocity, tension, stress). An image of the virtual object may be rendered at the viewing device or at a server in communication with the viewing device.
  • A system and method for visualization of physical interactions between objects are described. In one example embodiment, a sensor generates live data corresponding to physical characteristics of one or more object identified in a scene captured by a viewing device. The scene may include objects that are present within a field of view of the viewing device. Physical characteristics of the object are computed using the live data at a server. A visualization of the physical characteristics of the object is generated and communicated to the viewing device configured to capture an image of the objects in the scene. The viewing device augments the image of the object with the visualization of the physical characteristics of the object.
  • In another example embodiment, the system and method for visualization identifies objects in a scene captured with a viewing device. The system computes physical characteristics of the objects using data from sensors corresponding to the objects. A physics model of predicted interactions between the objects is generated using the physical characteristics of the objects. An interaction visualization is generated based on the physics model of the predicted interactions between objects. The image of the objects is augmented with the interaction visualization in a display of the viewing device. A characteristics visualization of the physical characteristics of the objects is generated. The image of the objects is augmented with the characteristics visualization of the objects in the display of the viewing device. The data may include a live data stream from the sensors.
  • In another example embodiment, an area in the scene affected by the predicted interactions between the objects is identified. A virtual object corresponding to the area affected by the predicted interactions is rendered. An image of the scene is augmented with the virtual object at the identified area in the viewing device.
  • In another example embodiment, an area in the scene unaffected by the predicted interactions between the one or more objects is identified. A virtual object corresponding to the area unaffected by the predicted interactions in the scene is rendered. An image of the scene is augmented with the virtual object at the identified area.
  • In another example embodiment, a physics model for each identified object is generated using the physical characteristics of the corresponding identified object. The physics model of predicted interactions is generated using the physics model of each identified object.
  • In another example embodiment, a predicted trajectory of each identified object within the scene is generated based on the physics model for each identified object. A predicted collision and a predicted collision location of the one or more identified objects are identified based on the predicted trajectories within the scene. An intensity of the predicted collision is identified.
  • In another example embodiment, a physics model of a dynamic object from the objects is generated. A physics model of a static object from the objects is generated. A physics model of the physical interaction between the dynamic object and the static object is generated. Areas in the scene affected by the physical interaction between the dynamic object and the static object are identified. An image of the identified areas in the scene is augmented.
  • In another example embodiment, a physics model of a first and a second dynamic object from the objects is generated. A physics model of the physical interaction between the first and second dynamic objects is generated. Areas in the scene affected by the physical interaction between the first and second dynamic objects are identified. An image of the identified areas in the scene is augmented.
  • In another example embodiment, a plurality of three-dimensional simulated trajectory paths within the scene based on a same source related to a first object in the scene are generated. Areas within the scene that are not affected by the plurality of three-dimensional simulated trajectory paths are identified. A second object that can withstand an impact from the first object is identified using the physical characteristics of the first object and the second objects. An image of the second object is augmented in the display of the viewing device.
  • 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. 1 is a network diagram illustrating a network environment 100 suitable for operating an augmented reality application of a viewing device, according to some example embodiments. The network environment 100 includes a viewing device 101 and a server 110, communicatively coupled to each other via a network 108. The viewing device 101 and the server 110 may each be implemented in a computer system, in whole or in part, as described below with respect to FIGS. 2 and 5.
  • The server 110 may be part of a network-based system. For example, the network-based system may be or include a cloud-based server system that provides additional information, such as three-dimensional models or other virtual objects, to the viewing device 101.
  • A user 102 may utilize the viewing device 101 to capture a view of a scene having several physical objects (e.g., object A 116, object B 118, object C 120, and object D 122) in a real world physical environment 114 viewed by the user 102. The user 102 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 device 101), or any suitable combination thereof (e.g., a human assisted by a machine or a machine supervised by a human). The user 102 is not part of the network environment 100, but is associated with the viewing device 101 and may be a user 102 of the viewing device 101. For example, the viewing device 101 may 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). The computing device may be hand held or may be removably mounted to the head of the user 102. In one example, the display may be a screen that displays what is captured with a camera of the viewing device 101. In another example, the display of the viewing device 101 may be transparent or semi-transparent such as in lenses of wearable computing glasses or the visor of a helmet.
  • The user 102 may be a user of an augmented reality application in the viewing device 101 and at the server 110. The augmented reality application may provide the user 102 with an augmented experience triggered by identified objects in the physical environment 114. The physical environment 114 may include identifiable objects such as a two-dimensional physical object (e.g., a picture), a three-dimensional physical object (e.g., a factory machine), a location (e.g., at the bottom floor of a factory), or any references (e.g., perceived corners of walls or furniture) in the real world physical environment 114. For example, the user 102 may point a camera of the viewing device 101 to capture an image of the objects 116, 118, 120, and 122.
  • In one embodiment, the objects in the image are tracked and recognized locally in the viewing device 101 using a local context recognition dataset or any other previously stored dataset of the augmented reality application of the viewing device 101. 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 device 101 identifies feature points in an image of the objects 116, 118, 120, 122 to determine different planes (e.g., edges, corners, surface). The viewing device 101 also identifies tracking data related to the objects 116, 118, 120, 122 (e.g., GPS location of a bridge, facing west, e.g., viewing device 101 standing x meters away from the bridge, etc.). If the captured image is not recognized locally at the viewing device 101, the viewing device 101 downloads additional information (e.g., the three-dimensional model) corresponding to the captured image, from a database of the server 110 over the network 108.
  • In another embodiment, the objects 116, 118, 120, 122 in the image are tracked and recognized remotely at the server 110 using a remote context recognition dataset or any other previously stored dataset of an augmented reality application in the server 110. The remote context recognition dataset module may include a library of virtual objects associated with real-world physical objects or references.
  • Sensors 112 may be associated with, coupled to, related to the objects 116, 118, 120, and 122 in the physical environment to measure physical properties of the objects 116, 118, 120, and 122. Examples of measured physical properties may include and but are not limited to weight, pressure, temperature, velocity, direction, position, intrinsic and extrinsic properties, acceleration, and dimensions.
  • For example, sensors 112 may be disposed throughout a span of a bridge to measure movement, pressure, orientation, temperature, etc. The server 110 can compute physical characteristics of the bridge using the live data generated by the sensors 112. The server 110 can compute the amount of stress at specific locations throughout the bridge as cars drive over the bridge. The server 110 can generate virtual indicators such as vectors or colors based on the computed physical characteristics of the bridge. The virtual indicators are then overlaid on top of a live image of the bridge to show the amount of real-time stress on the bridge. For example, the virtual indicators may include arrows with shapes and colors that change based on real-time data. The visualization may be provided to the viewing device 101 so that the viewing device 101 can render the virtual indicators in a display of the viewing device 101. In another embodiment, the virtual indicators are rendered at the server 110 and streamed to the viewing device 101. The viewing device 101 displays the virtual indicators or visualization corresponding to a display of the physical environment 114. The virtual arrows are positioned at locations corresponding to the amount of stress measured on the bridge.
  • In another example, the sensors 112 may include a blood pressure and heart rate monitor coupled to a subject. For example, the server 110 can generate a picture or a virtual three-dimensional model of a heart with a beating animation with a pace corresponding to the measured heart rate from the sensors 112. The server 110 may change a color in corresponding portions of the heart based on the measured blood pressure. The live animation is provided to the viewing device 101 such that the picture of the heart is displayed on top of a chest area of the subject in the display of the viewing device 101. The position of the picture of the heart may be determined based on the orientation and position of the viewing device 101 relative to the subject using sensors (e.g., gyroscope) internal to the viewing device 101.
  • In another example, sensors (e.g., optical) may be used to capture the direction of a car on a road or the direction of a bullet fired from a point of origin. The server 110 can generate physical properties of the car or the bullet such as direction and speed. The server 110 can determine a path of the car or bullet based on the physical properties of the car or bullet. The server 110 may generate a visualization based on the physical properties of the car or the bullet. For example, the server 110 may generate a color filter to augment the image of the car in a different color based on the speed of the car. Furthermore, the server 110 can generate a physics model of object interactions based on the physical properties of the car or the bullet. For example, if a ladder is misplaced on the road, the server 110 may identify the ladder, a position of the ladder on the road, a type of ladder, the speed of the car, distance of the car from the ladder, and the weight of the car, among other physical properties, to generate a physics model of a predicted interactions (e.g. predicted collision path, predicted areas of impact, intensity of impact, etc.) between the car and the ladder. For example, the physics model may predict that the ladder may bounce off the car during the impact with a predicted trajectory. Areas that are susceptible to the predicted trajectory of the ladder resulting from the impact may be visualized by coloring those areas in the image of the scene. As such, areas of the road may appear red based on a high probability of being run over by the car or being injured from debris from the ladder. In another example, green areas in the image may indicate safe areas that may not be affected by the car and the ladder.
  • In another example, the server 110 may determine whether objects identified within the scene can withstand and protect the user 102 from being hit by a bullet. For example, the server 110 may identify a wooden chair in the scene and a car in the scene. The server 110 may generate a physics model of the bullet by determining predicted trajectories. Based on the predicted trajectories and the location and properties of the identified objects (e.g., wooden chair and car), the server 110 may generate an augmented visualization in the image by coloring safe areas in green and dangerous areas in red in the scene viewed by the user 102 through the viewing device 101. Safe areas may include areas adjacent to the car because the server 110 may determine that the car is strong enough to withstand bullets in contrast to a wooden chair.
  • The sensors 112 may include other sensors used to track the location, movement, and orientation of the viewing device 101 externally without having to rely on the sensors internal to the viewing device 101. The sensors 112 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 user 102 having the viewing device 101, distance of the user 102 to the tracking sensors 112 in the physical environment 114 (e.g., sensors placed in corners of a venue or a room), the orientation of the viewing device 101 to track what the user 102 is looking at (e.g., direction at which the viewing device 101 is pointed, viewing device 101 pointed towards a player on a tennis court, viewing device 101 pointed at a person in a room).
  • In another embodiment, data from the sensors 112 and internal sensors in the viewing device 101 may be used for analytics data processing at the server 110 (or another server) for analysis on usage and how the user 102 is interacting with the physical environment 114. 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 user 102 has looked, how long the user 102 has looked at each location on the physical or virtual object, how the user 102 held the viewing device 101 when looking at the physical or virtual object, which features of the virtual object the user 102 interacted with (e.g., such as whether a user 102 tapped on a link in the virtual object), and any suitable combination thereof. The viewing device 101 receives a visualization content dataset related to the analytics data. The viewing device 101 then generates a virtual object with additional or visualization features, or a new experience, based on the visualization content dataset.
  • Any of the machines, databases, or devices shown in FIG. 1 may be implemented in a general-purpose computer modified (e.g., configured or programmed) by software to be a special-purpose computer to perform one or more of the functions described herein for that machine, database, or device. For example, a computer system able to implement any one or more of the methodologies described herein is discussed below with respect to FIGS. 12, 13. As used herein, a “database” is a data storage resource and may store data structured as a text file, a table, a spreadsheet, a relational database (e.g., an object-relational database), a triple store, a hierarchical data store, or any suitable combination thereof. Moreover, any two or more of the machines, databases, or devices illustrated in FIG. 1 may be combined into a single machine, and the functions described herein for any single machine, database, or device may be subdivided among multiple machines, databases, or devices.
  • The network 108 may be any network that enables communication between or among machines (e.g., server 110), databases, and devices (e.g., device 101). Accordingly, the network 108 may be a wired network, a wireless network (e.g., a mobile or cellular network), or any suitable combination thereof. The network 108 may include one or more portions that constitute a private network, a public network (e.g., the Internet), or any suitable combination thereof.
  • FIG. 2 is a block diagram illustrating modules (e.g., components) of the viewing device 101, according to some example embodiments. The viewing device 101 may include sensors 202, a display 204, a processor 206, and a storage device 208. For example, the viewing device 101 may be a wearing computing device (e.g., glasses or 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 device 101), or any suitable combination thereof (e.g., a human assisted by a machine or a machine supervised by a human).
  • The sensors 202 may include, for example, a proximity or location sensor (e.g., Near Field Communication, GPS, Bluetooth, Wi-Fi), an optical sensor (e.g., camera), an orientation sensor (e.g., gyroscope), an audio sensor (e.g., a microphone), or any suitable combination thereof. For example, the sensors 202 may include a rear facing camera and a front facing camera in the viewing device 101. It is noted that the sensors 202 described herein are for illustration purposes; the sensors 202 are thus not limited to the ones described. The sensors 202 may be used to generate internal tracking data of the viewing device 101 to determine what the viewing device 101 is capturing or looking at in the real physical world.
  • The display 204 may include, for example, a touchscreen display configured to receive a user input via a contact on the touchscreen display. In one example, the display 204 may include a screen or monitor configured to display images generated by the processor 206. In another example, the display 204 may be transparent or semi-opaque so that the user 102 can see through the display 204 (e.g., Head-Up Display).
  • The processor 206 may include an augmented reality application 216 for creating a virtual display of real-time physical characteristics and predicted interactions related to an object when the viewing device 101 captures an image of an object or a subject. In one example embodiment, the augmented reality application 216 may include a recognition module 214 and a physical characteristics and interaction module 218.
  • The recognition module 214 identifies the object that the viewing device 101 is pointed to. The recognition module 214 may detect, generate, and identify identifiers such as feature points of the physical object being viewed or pointed at the viewing device 101 using an optical device of the viewing device 101 to capture the image of the physical object. As such, the recognition module 214 may be configured to identify one or more physical objects. In one example embodiment, the recognition module 214 may include a feature points module 302 and a contextual local image module 304 as illustrated in FIG. 3. The identification of the object may be performed in many different ways. For example, the feature points module 302 may determine feature points of the object based on several image frames of the object. The feature points module 302 also 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 module 214 can 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 module 214 includes a facial recognition algorithm to determine an identity of a subject or object. The contextual local image module 304 may 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 device 101. In one embodiment, the contextual local image module 304 retrieves a primary content dataset from the server 110, and generates and updates a contextual content dataset based an image captured with the viewing device 101.
  • The physical characteristics and interaction module 218 determines information or status including physical characteristics related to the object and generates predicted interactions between identified objects. In one example embodiment, the physical characteristics and interaction module 218 may include a live data module 402, a physics modeling module 404, and a visualization module 406 as illustrated in FIG. 4.
  • The live data module 402 communicates with sensors associated with the objects to receive live data related to physical characteristics of the objects. In another embodiment, the live data module 402 communicates with the server 110 to receive a live data stream related to physical characteristics of the objects. In another embodiment, the object may broadcast live data from sensors internal to the object. For example, the object may include sensors embedded in it. The sensors can broadcast real time data to the live data module 402 of the viewing device 101 and the server 110. The live data module 402 may receive the live data after authentication with the object. In another example, the live data module 402 can receive computed physical characteristics from the server 110, or live raw data from sensors 112.
  • The physics modeling module 404 may compute a physics model based on the live data. For example, the physics model may include a predicted three dimensional trajectory using the velocity of an object based on observational data or measured from sensors embedded in the object, and the position and direction of the object. In one embodiment, the physics modeling module 404 generates a physics model for each identified object captured by the viewing device 101. The physics modeling module 404 further generates a physics model of interactions between the objects. Interactions may include for example collisions or the effects of a first object on a second object. For example, the physics modeling module 404 may generate a physics model of the projected trajectories of collided objects or the intensity of impacts between objects. In another example, the physics model may identify areas on the water surface affected by a traveling boat. In another example, the physics model may identify areas on a wall affected by the temperature of an object connected to the wall. For example, the physics model may identify areas that may be harmful to certain thermal sensitive objects because of the temperature of a furnace. Other examples include determining locations that are safe or dangerous based on the physical characteristics of a first object and a second object.
  • The visualization module 406 may generate a visualization of the physical characteristics and interactions related to the objects. The visualization may include rendering a three-dimensional object (e.g., model of a beating heart), a two-dimensional object (e.g., arrow or symbols), displaying parts of the scene or objects in different colors. In one example embodiment, the visualization module 406 receives data from the server 110 to render the visualization. In another example embodiment, the visualization module 406 receives the rendered object. The visualization module 406 further determines the position and size of the rendered object to be displayed in relation to an image of the object. For example, the visualization module 406 places the animated heart with the size and position based on the image of the subject such that the animated heart is displayed on the chest of the subject with the appropriate size. The visualization module 406 may track the image of the subject and render the virtual object based on the position of the image of the subject in a display of the viewing device 101. In another example, the visualization module 406 may render portions of the objects or the scene in different colors based on the physical characteristics and the predicted interactions between the objects from the physics model.
  • In one example embodiment, the viewing device 101 accesses 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 device 101 receives a visualization model corresponding to the image of the object from the server 110. The viewing device 101 then renders the visualization model to be displayed in relation to an image of the object being displayed in the viewing device 101 or in relation to a position and orientation of the viewing device 101 relative to the object. The augmented reality application 216 may adjust a position of the rendered visualization model in the display 204 to correspond with the last tracked position of the object (as last detected either from the sensors 202 of the viewing device 101 or from the tracking sensors 112 of the server 110).
  • The visualization module 406 may 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 device 101 in the display 204 of the viewing device 101. 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 device 101. Similarly, the visualization of the three-dimensional virtual object may be manipulated by adjusting a position of the camera of the viewing device 101 relative to the physical object.
  • In one example embodiment, the visualization module 406 may retrieve three-dimensional models of virtual objects associated with a captured 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, 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 visualization module 406 may include a manipulation module that identifies the physical object (e.g., a physical telephone), access 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 generate a virtual function corresponding to a physical manipulation of the physical object.
  • The storage device 208 may be configured to store a database of identifiers of physical objects, tracking data, and corresponding virtual user interfaces. 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). For example, the visual reference may include a machine-readable code or a previously identified image (e.g., a picture of a shoe). The previously identified image of the shoe may correspond to a three-dimensional virtual model of the shoe that can be viewed from different angles by manipulating the position of the viewing device 101 relative to the picture of the shoe. Features of the three-dimensional virtual shoe may include selectable icons on the three-dimensional virtual model of the shoe. An icon may be selected or activated by tapping on or moving the viewing device 101.
  • In one embodiment, the storage device 208 includes 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 server 110. The core set of images may include a limited number of images identified by the server 110. For example, the core set of images may include the images depicting covers of the ten most popular magazines and their corresponding experiences (e.g., virtual objects that represent the ten most popular magazines). In another example, the server 110 may generate the first set of images based on the most popular or often scanned images received at the server 110. Thus, the primary content dataset does not depend on objects or images scanned by the recognition module 214 of the viewing device 101.
  • 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 server 110. For example, images captured with the viewing device 101 that are not recognized (e.g., by the server 110) in the primary content dataset are submitted to the server 110 for recognition. If the captured image is recognized by the server 110, a corresponding experience may be downloaded at the viewing device 101 and stored in the contextual content dataset. Thus, the contextual content dataset relies on the context in which the viewing device 101 has been used. As such, the contextual content dataset depends on objects or images scanned by the recognition module 214 of the viewing device 101.
  • In one embodiment, the viewing device 101 may communicate over the network 108 with the server 110 to retrieve a portion of a database of visual references, corresponding three-dimensional virtual objects, and corresponding interactive features of the three-dimensional virtual objects. The network 108 may be any network that enables communication between or among machines, databases, and devices (e.g., the viewing device 101). Accordingly, the network 108 may be a wired network, a wireless network (e.g., a mobile or cellular network), or any suitable combination thereof. The network 108 may include one or more portions that constitute a private network, a public network (e.g., the Internet), or any suitable combination thereof.
  • 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. 5 is a block diagram illustrating modules (e.g., components) of the server 110. The server 110 includes a sensor interface 501, a processor 502, and a database 510. The sensor interface 501 may communicate with the sensors 112 (FIG. 1) to receive real time data.
  • The processor 502 may include an object identifier 504, a physical characteristics identifier 506, a physical characteristics visualization module 508, a physics modeling module 518, an objects interactions module 520, and an objects interactions visualization module 522. The object identifier 504 may operate similarly to the recognition module 214 of the viewing device 101. For example, the object identifier 504 may identify objects 116, 118, 120, and 122 based on a picture being received from the viewing device 101. In another example, the viewing device 101 already has identified objects 116, 118, 120, and 122 and has provided the identification information to the object identifier 504.
  • The physical characteristics identifier 506 may determine the physical characteristics associated with the object identified. For example, if the object is bridge “abc” at location x, the physical characteristics may include the number of cars driven on the bridge during rush hours, the amount of stress on a particular span of the bridge, the temperature of the pavement in the middle of the bridge. If the object is a bullet, the physical characteristics may include speed, point of origin, and direction. If the object is a car, the physical characteristics may include a make and model of the car, specifications associated with the make and model of the car. If the object is a bridge, the physical characteristics may include load capacity and span length.
  • The physical characteristics visualization module 508 generates a graphical visualization based on the real time data of the physical characteristics of identified objects 116, 118, 120, and 122. For example, the physical characteristics visualization module 508 may generate arrows or vectors corresponding to sections of a bridge. In another embodiment, the physical characteristics visualization module 508 may generate a visual model based on the real time data of the physical characteristics. For example, the visual model may include a three dimensional model of the bridge.
  • The physics modeling module 518 may include similar functionalities from physics modeling module 404 of the viewing device 101. For example, the physics modeling module 518 may compute a physics model based on the live data related to the identified objects 116, 118, 120, and 122. For example, the physics model may include a predicted three dimensional trajectory using the velocity of an object based on observational data or measured from sensors embedded in the object, and the position and direction of the object. In one embodiment, the physics modeling module 518 generates a physics model for each identified object.
  • The objects interactions module 520 generates a physics model of interactions between the objects. Interactions may include for example physical interactions such as predicted collisions or the effects of a first object on a second object. For example, the physics modeling module 518 may generate a physics model of the projected trajectories of collided objects or the intensity of impacts between objects. In another example, the physics model may identify areas on the water surface affected by a traveling boat. In another example, the physics model may identify areas on a wall affected by the temperature of an object connected to the wall. For example, the physics model may identify areas that may be harmful to certain thermal sensitive objects because of the temperature of a furnace. Other examples include determining locations that are safe or dangerous based on the physical characteristics of a first object and a second object.
  • The objects interactions visualization module 522 may render portions of the objects or a scene captured with the viewing device 101 in different colors based on the physical characteristics and the predicted interactions between the objects from the physics model. For example, areas of potential dangers from the predicted collisions may be displayed in a semi-opaque red color. Other types of interactions visualizations may include augmented information related to the predicted interaction based on the physical characteristics of the identified objects. In another example, the viewing device 101 may be looking at a machine and a tool. The objects interactions visualization module 522 may model the interaction between the tool and the machine. The model generated indicates that another valve may need to be adjusted based on the interaction between the tool and the machine. The objects interactions visualization module 522 may then generate a visualization comprising augmented information about the affected other valve or augment the image of the other valve in a display of the viewing device 101 by changing its color or generating an animation on the image of the other valve through the viewing device 101.
  • The database 510 may store a content dataset 512, a virtual content dataset 514, and physical characteristics dataset 516. The content dataset 512 may store a primary content dataset and a contextual content dataset. The primary content dataset comprises a first set of images and corresponding virtual object models. The object identifier 504 determines that a captured image received from the viewing device 101 is not recognized in the content dataset 512, and generates the contextual content dataset for the viewing device 101. The contextual content dataset may include a second set of images and corresponding virtual object models. The virtual content dataset 514 includes models of virtual objects to be generated upon receiving a notification associated with an image of a corresponding physical object. The physical characteristics dataset 516 includes a table of identified objects with corresponding physical characteristics (e.g., shapes, color, intrinsic properties, extrinsic properties, temperature, weight, speed, etc).
  • FIG. 6 is a ladder diagram illustrating an example embodiment of a system for visualizing physical characteristics in an augmented reality application of the viewing device 101 and server 110. At operation 602, the viewing device 101 identifies objects 116, 118, 120, and 122 in a scene and tracks data related to the objects being captured by the viewing device 101. At operation 604, the viewing device 101 communicates the identity of the objects being tracked to the server 110. At operation 606, the server 110 retrieves live data related to the objects and computes physical characteristics of the objects. At operation 608, the server 110 generates a physics model for the one or more objects. At operation 610, the server 110 generates a physics model of objects interactions. At operation 612, the server 110 communicates the live characteristics data and the objects interactions model data to the viewing device 101.
  • At operation 614, the viewing device 101 generates a visualization based on the live data of physical characteristics of the objects. For example, a virtual heart may be displayed on an image of a chest of the identified subject. In another example, the visualization may be projected onto another object. For example, the viewing device 101 points to a physical plastic heart model in the physician's office. The viewing device 101 then starts to animate a virtual heart over the image of the plastic heart model based on the live heartbeat of a patient/subject. As such, the viewing device 101 may recognize the object as a plastic heart model and request live data from any sensor connected to any patient. Furthermore, the viewing device 101 may be moved around the physical plastic heart. The visualization is accordingly modified based on the position of the viewing device 101 relative to the plastic heart so as to show from a different angle. Thus, the visualization of live data from one object may be projected on an image of another object.
  • At operation 616, the viewing device 101 generates a visualization based physics model of objects interactions using the live data of physical characteristics of the objects and physics model of one or more objects. For example, the visualization may include identifying areas of danger or safety for the user of the viewing device 101 based on the physics model of the predicted interactions of the objects (e.g., the car to the right of the user may be augmented with green color or information to identify that hiding behind it would be safe from bullets). In another example, the viewing device 101 captures a scene having a first container with chemical X, a second container with chemical Y, and an empty container. The viewing device 101 may identify the chemicals based on identifiers on the respective containers. The viewing device 101 may generate a visualization of a simulated or predicted interaction of the chemical X and Y in the empty container. For example, the image of the empty container may be augmented with a virtual object showing the resulting reaction between chemical X and Y using a three dimensional virtual object (e.g., colored bubbling smoke rising from the empty container, or a simulated explosion from the empty container).
  • FIG. 7 is a ladder diagram illustrating an example embodiment of training an augmented reality application at a viewing device. At operation 701, the viewing device 101 may generate data containing an image stream to the server 110. The image stream may include a live video feed from the viewing device 101. At operation 702, objects from the image stream are identified at the server 110. At operation 704, the server 110 accesses live data from sensors (e.g., optical, thermal, etc.) related or associated with the corresponding objects. At operation 706, the server 110 computes a physics model of one or more objects from the image stream. At operation 708, the server 110 computes a physics model of interactions between two or more objects from the image stream. At operation 710, the server 110 generates a visualization of physical characteristics of one or more identified objects from the image stream. At operation 712, the server 110 generates a visualization of physical characteristics of one or more identified object interactions from the image stream. At operation 714, the server 110 communicates the visualization data to the viewing device 101. At operation 716, the viewing device 101 displays the augmented visualization in relation to the image of the identified objects.
  • FIG. 8 is a flowchart illustrating an example operation of comparing visualization physical characteristics of real world objects in an augmented reality application. At operation 802, the viewing device identifies two or more objects. At operation 804, the viewing device retrieves live physical characteristics data related to the identified objects from a server (e.g., temperature, weight, direction, velocity, acceleration of an object). In one example embodiment, the viewing device may access historical data of the same object being viewed. For example, the historical data may include the height of the same plant (identified object) from previous days. At operation 806, the viewing device generates a visualization based on the live physical characteristics data related to the identified objects. The visualization may include a result from a computation performed on the live physical characteristics. At operation 808, the viewing device modifies the visualization based on changes in the live physical characteristics data of the identified objects.
  • FIG. 9 is a flowchart illustrating an example operation of a visualization of physical characteristics in augmented reality at a viewing device. At operation 902, the viewing device identifies at least two objects. At operation 904, the viewing device retrieves live physical characteristics data related to the identified objects from a server. At operation 906, the viewing device generates a physics model for each identified object. At operation 908, the viewing device generates a physics model of object interactions between the identified objects. At operation 910, the viewing device generates a visualization of the predicted object interactions or an analysis based on the predicted object interactions.
  • FIG. 10 is a flowchart illustrating an example operation of visualization of physical characteristics and interactions in augmented reality at a server. At operation 1002, the server identifies a dynamic object (e.g., an object that is physically moving or having a physical characteristic that is changing, such as a change in temperature) in a scene or field of view captured by the viewing device. At operation 1004, the server identifies sensors related to the identified object and generates a physics model of the dynamic object based on the live data from the corresponding sensors. The physics model allows for a predicted path or trajectory, behavior, or reaction of the identified object. The physics model may also include a virtual three dimensional model of the identified object. At operation 1006, the server identifies a static object within the same scene captured by the viewing device. At operation 1008, the server generates a model of the physical interactions between the dynamic object and the static object. At operation 1010, the server generates an augmented visualization based on the model of physical interactions.
  • FIG. 11 is a diagram illustrating an example operation of a visualization of physical characteristics and interactions in augmented reality applications. At operation 1102, the server identifies a first and a second dynamic object (e.g., an object that is physically moving or having a physical characteristic that is changing, such as a change in temperature) in a scene or field of view captured by the viewing device. At operation 1104, the server identifies sensors related to the identified objects and generates a physics model of the dynamic objects based on the live data from the corresponding sensors. The physics model allows for a predicted path or trajectory, behavior, or reaction of the identified objects. The physics model may also include a virtual three dimensional model of the identified object. At operation 1108, the server generates a model of the physical interactions between the dynamic objects. At operation 1110, the server generates an augmented visualization based on the model of physical interactions.
  • FIG. 12 is a diagram illustrating an example operation of generating a visualization of interactions between a dynamic and a static object in an augmented reality application. The viewing device 101 may include a handheld mobile device having a rear view camera 1202 and a touch sensitive display 1204. The viewing device 101 may be pointed at a scene comprising a dynamic object (“object A”) 1206 and a static object (“object B”) 1208. The rear view camera 1202 captures an image of both objects 1206, 1208 and displays pictures 1210, 1212 of the objects 1206, 1208 in the display 1204. Identifiers and tracking data related to objects 1206, 1208 may be determined by the viewing device 101 based on the pictures 1210, 1212 so as to identify the objects 1206, 1208. The viewing device 101 communicates an identification of the identified objects 1206, 1208 to the server 110. The server 110 accesses live data from physical characteristics sensors 112 coupled to or associated with the objects 1206, 1208. The server 110 computes physical characteristics based on the live data and generates a physics model of predicted interactions between the objects 1206, 1208 based on the live physical characteristics. The server 110 then generates a visualization of the computed physical characteristics and predicted interactions. For example, the visualization may include a virtual arrow 1214 representing the trajectory of object A 1206 on the display 1204. The virtual arrow 1214 may have a different color or size based on the speed of object A 1206. The visualization may also include a virtual image 1216 representing the potential area of damage caused by the predicted interaction. The virtual image 1216 may be for example a colored layer on the picture 1212 of the object B 1208.
  • The server 110 may send the visualization of the computed physical interactions to the viewing device 101. In another example, the viewing device 101 generates the visualization. The viewing device 101 displays the visualization on another layer on top of the pictures 1210, 1212.
  • FIG. 13 is a diagram illustrating another example operation of generating a visualization of object interactions between dynamic objects in an augmented reality application. The viewing device 101 may be pointed at a scene comprising a first dynamic object (“object C”) 1306 and a second dynamic object (“object D”) 1308. The rear view camera 1202 captures an image of both objects 1306, 1308 and displays pictures 1310, 1312 of the objects 1306, 1308 in the display 1204. Identifiers and tracking data related to objects 1306, 1308 may be determined by the viewing device 101 based on the pictures 1310, 1312 so as to identify the objects 1306, 1308. The viewing device 101 communicates an identification of the identified objects 1306, 1308 to the server 110. The server 110 accesses live data from physical characteristics sensors 112 coupled to or associated with the objects 1306, 1308. The server 110 computes physical characteristics based on the live data and generates a physics model of predicted interactions between the objects 1306, 1308 based on the live physical characteristics. The server 110 then generates a visualization of the computed physical characteristics and predicted interactions. For example, the visualization may include a virtual arrow 1302 representing the trajectory of object C 1306 and another virtual arrow 1304 representing the trajectory of object D 1308 on the display 1204. The virtual arrows 1302 and 1304 may have a different color or size based on the speed of respective dynamic objects C 1306 and D 1308. The visualization may also include a virtual object 1305 representing the potential area of damage, explosion, or collision caused by the predicted interaction. The virtual object 1305 may be for example an animated or static virtual two or three dimensional object. The server 110 may send the visualization of the computed physical interactions to the viewing device 101. In another example, the viewing device 101 generates the visualization. The viewing device 101 displays the visualization on another layer on top of the pictures 1310, 1312.
  • Modules, Components and Logic
  • Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware modules. A hardware module is a tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client, or server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein.
  • In various embodiments, a hardware module may be implemented mechanically or electronically. For example, a hardware module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.
  • Accordingly, the term “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired) or temporarily configured (e.g., programmed) to operate in a certain manner and/or to perform certain operations described herein. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where the hardware modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware modules at different times. Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time.
  • Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple of such hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices and can operate on a resource (e.g., a collection of information).
  • The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules.
  • Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations.
  • The one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), these operations being accessible via a network and via one or more appropriate interfaces (e.g., APIs).
  • Electronic Apparatus and System
  • Example embodiments may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. Example embodiments may be implemented using a computer program product, e.g., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable medium for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers.
  • A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.
  • In example embodiments, operations may be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Method operations can also be performed by, and apparatus of example embodiments may be implemented as, special purpose logic circuitry (e.g., a FPGA or an ASIC).
  • A computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In embodiments deploying a programmable computing system, it will be appreciated that both hardware and software architectures merit consideration. Specifically, it will be appreciated that the choice of whether to implement certain functionality in permanently configured hardware (e.g., an ASIC), in temporarily configured hardware (e.g., a combination of software and a programmable processor), or a combination of permanently and temporarily configured hardware may be a design choice. Below are set out hardware (e.g., machine) and software architectures that may be deployed, in various example embodiments.
  • Example Machine Architecture and Machine-Readable Medium
  • FIG. 14 is a block diagram of a machine in the example form of a computer system 1400 within which instructions 1424 for causing the machine to perform any one or more of the methodologies discussed herein may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.
  • The example computer system 1400 includes a processor 1402 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 1404 and a static memory 1406, which communicate with each other via a bus 1408. The computer system 1400 may further include a video display unit 1410 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 1400 also includes an alphanumeric input device 1412 (e.g., a keyboard), a user interface (UI) navigation (or cursor control) device 1414 (e.g., a mouse), a disk drive unit 1416, a signal generation device 1418 (e.g., a speaker) and a network interface device 1420.
  • Machine-Readable Medium
  • The disk drive unit 1416 includes a machine-readable medium 1422 on which is stored one or more sets of data structures and instructions 1424 (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions 1424 may also reside, completely or at least partially, within the main memory 1404 and/or within the processor 1402 during execution thereof by the computer system 1400, the main memory 1404 and the processor 1402 also constituting machine-readable media. The instructions 1424 may also reside, completely or at least partially, within the static memory 1406.
  • While the machine-readable medium 1422 is shown in an example embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more instructions 1424 or data structures. The term “machine-readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present embodiments, or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including by way of example semiconductor memory devices (e.g., Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), and flash memory devices); magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and compact disc-read-only memory (CD-ROM) and digital versatile disc (or digital video disc) read-only memory (DVD-ROM) disks.
  • Transmission Medium
  • The instructions 1424 may further be transmitted or received over a communications network 1426 using a transmission medium. The instructions 1424 may be transmitted using the network interface device 1420 and any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a LAN, a WAN, the Internet, mobile telephone networks, POTS networks, and wireless data networks (e.g., WiFi and WiMax networks). The term “transmission medium” shall be taken to include any intangible medium capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such software.
  • Example Mobile Device
  • FIG. 15 is a block diagram illustrating a mobile device 1500, according to an example embodiment. The mobile device 1500 may include a processor 1502. The processor 1502 may be any of a variety of different types of commercially available processors 1502 suitable for mobile devices 1500 (for example, an XScale architecture microprocessor, a microprocessor without interlocked pipeline stages (MIPS) architecture processor, or another type of processor 1502). A memory 1504, such as a random access memory (RAM), a flash memory, or other type of memory, is typically accessible to the processor 1502. The memory 1504 may be adapted to store an operating system (OS) 1506, as well as application programs 1508, such as a mobile location enabled application that may provide LBSs to a user. The processor 1502 may be coupled, either directly or via appropriate intermediary hardware, to a display 1510 and to one or more input/output (I/O) devices 1512, such as a keypad, a touch panel sensor, a microphone, and the like. Similarly, in some embodiments, the processor 1502 may be coupled to a transceiver 1514 that interfaces with an antenna 1516. The transceiver 1514 may be configured to both transmit and receive cellular network signals, wireless data signals, or other types of signals via the antenna 1516, depending on the nature of the mobile device 1500. Further, in some configurations, a GPS receiver 1518 may also make use of the antenna 1516 to receive GPS signals.
  • Although an embodiment has been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the present disclosure. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof, show by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.
  • Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description.
  • The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment.

Claims (20)

What is claimed is:
1. A device comprising:
an imaging device to capture an image; and
an augmented reality application implemented by a hardware processor of the device and configured to perform operations comprising:
identifying a first and second object in the image;
determining physical properties of the first and second object;
generating a model of a predicted interaction between the first and second object based on the physical properties of the first and second object;
identifying an area in the image based on the model of the predicted interaction between the first and second object; and
augmenting the image at the area in a display of the device.
2. The device of claim 1, wherein the area in the image includes a location of the predicted interaction between the first and second object.
3. The device of claim 1, wherein the area in the image identifies a predicted collision between the first and second object.
4. The device of claim 1, wherein the physical properties comprises a velocity and a direction of each of the first and second object.
5. The device of claim 1, wherein the physical properties comprise one or more three-dimensional simulated trajectory paths of the first and second object.
6. The device of claim 1, wherein the operations further comprise:
identifying a third object in the image;
determining that the third object can withstand an impact based on the model of the predicted interaction between the first and second object; and
augmenting the image at another area based on the location of the third object.
7. The device of claim 1, wherein the operations further comprise:
identifying a third object in the image;
determining that the third object can withstand an impact from the first or second object; and
augmenting the image at another area based on the location of the third object.
8. The device of claim 1, wherein the area of the augmented image includes at least one of a colored area, a shaded area, a patterned area, and a marked area.
9. The device of claim 1, wherein the operations further comprise:
augmenting a first area of the image with a first color, the first area identifying an area affected by the model of the predicted interaction between the first and second object; and
augmenting a second area of the image with a second color distinct from the first color, the second area identifying an area not affected by the model of the predicted interaction between the first and second object.
10. The device of claim 9, wherein the size of the first and second area corresponds to an impact intensity between the first and second object.
11. A computer-implemented method comprising:
capturing an image with an imaging device;
identifying a first and second object in the image;
determining physical properties of the first and second object;
generating a model of a predicted interaction between the first and second object based on the physical properties of the first and second object;
identifying an area in the image based on the model of the predicted interaction between the first and second object; and
augmenting the image at the area in a display of the device.
12. The method of claim 11, wherein the area in the image includes a location of the predicted interaction between the first and second object.
13. The method of claim 11, wherein the area in the image identifies a predicted collision between the first and second object.
14. The method of claim 11, wherein the physical properties comprise a velocity and a direction of each of the first and second object.
15. The method of claim 11, wherein the physical properties comprise one or more three-dimensional simulated trajectory paths of the first and second object.
16. The method of claim 11, further comprising:
identifying a third object in the image;
determining that the third object can withstand an impact based on the model of the predicted interaction between the first and second object; and
augmenting the image at another area based on the location of the third object.
17. The method of claim 11, further comprising:
identifying a third object in the image;
determining that the third object can withstand an impact from the first or second object; and
augmenting the image at another area based on the location of the third object.
18. The method of claim 11, wherein the area of the augmented image includes at least one of a colored area, a shaded area, a patterned area, and a marked area.
19. The method of claim 11, wherein the operations further comprise:
augmenting a first area of the image with a first color, the first area identifying an area affected by the model of the predicted interaction between the first and second object; and
augmenting a second area of the image with a second color distinct from the first color, the second area identifying an area not affected by the model of the predicted interaction between the first and second object.
20. A non-transitory machine-readable medium comprising instructions that when executed by one or more processors of a machine cause the machine to perform operations comprising:
capturing an image with an imaging device;
identifying a first and second object in the image;
determining physical properties of the first and second object;
generating a model of a predicted interaction between the first and second object based on the physical properties of the first and second object;
identifying an area in the image based on the model of the predicted interaction between the first and second object; and
augmenting the image at the area in a display of the device.
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