1. Field of the Invention
This invention relates generally to natural interaction systems. More particularly this invention relates to adaptive reality augmentation and 3-dimensional input interfaces.
2. Description of the Related Art
Natural user interfaces are gaining momentum in the entertainment and computer industry. Gesture controls are supplementing or replacing more conventional and less natural interfaces such as keyboard and mouse, game controller, and remote control. The user interactions, however, continue to relate largely to the computer monitor, thus limiting applicability and ease of use of such interfaces. Some of the gesture controls rely on optical 3-dimensional mapping.
Various methods are known in the art for optical 3-D mapping, i.e., generating a 3-dimensional profile of the surface of an object by processing an optical image of the object. This sort of profile is also referred to as a depth map or depth image, and 3-D mapping is also referred to as depth mapping.
Some methods are based on projecting a laser speckle pattern onto the object, and then analyzing an image of the pattern on the object. For example, PCT International Publication WO 2007/043036, whose disclosure is incorporated herein by reference, describes a system and method for object reconstruction in which a coherent light source and a generator of a random speckle pattern project onto the object a coherent random speckle pattern. An imaging unit detects the light response of the illuminated region and generates image data. Shifts of the pattern in the image of the object relative to a reference image of the pattern are used in real time reconstruction of a 3-D map of the object. Further methods for 3-D mapping using speckle patterns are described, for example, in PCT International Publication WO 2007/105205, whose disclosure is incorporated herein by reference.
The present invention, in certain embodiments thereof seeks to provide an improved content projection device, which is aware of objects in its field of view, recognizing such objects as suitable for projection of content thereon. The projection device may adapt to the geometry and character of the objects by controlling scale, distortion, focus of the projected content, and varying the projected content itself. Additionally or alternatively, the projection device may adapt the projected content according to the relationship of the viewer to the projected content, such as its gaze vector, distance from the surface onto which content is projected, and other similar parameters. The 2D/3D input device used to analyze the geometry for projection can also be used to interact with the projected content.
According to disclosed embodiments of the invention, methods and apparatus are provided for the projection of content, such as the input device interface, using a 3-dimensional input device as means of determining the optimal objects to serve as substrate for such content projection.
There is provided according to embodiments of the invention an apparatus for processing data, including a sensing element for acquiring a scene including a 2-dimensional camera and a 3-dimensional camera, a processor linked to the 3-dimensional camera and the 2-dimensional camera and programmed to produce a depth map of the scene using an output of the 3-dimensional camera, and to coordinate the depth map with a 2-dimensional image captured by the 2-dimensional camera to identify a 3-dimensional object in the scene that meets predetermined criteria for projection of images thereon, and a content projector for establishing a projected image onto the 3-dimensional object responsively to instructions of the processor.
According to an aspect of the apparatus, coordinating the depth map includes identifying a position of the 3-dimensional object with six degrees of freedom with respect to a reference system of coordinates, wherein the content projector is operative to compensate for scale, pitch, yaw and angular rotation of the 3-dimensional object.
According to a further aspect of the apparatus, coordinating the depth map includes referencing a database of 3-dimensional object definitions and comparing the 3-dimensional object with the definitions in the database.
An aspect of the apparatus includes a wearable monitor, wherein the content projector is operative to establish the projected image as a virtual image in the wearable monitor or in a virtual space. The sensing element, the processor and the content projector may be incorporated in the wearable monitor.
According to a further aspect of the apparatus, the content projector is operative to establish the projected image onto a virtual surface for user interaction therewith.
According to yet another aspect of the apparatus, the processor is operative for controlling a computer application responsively to a gesture and wherein the projected image includes a user interface for control of the computer application.
According to aspect of the apparatus, the projected image includes written content.
In another embodiment, an apparatus for processing data includes a projector, which is configured to project content onto at least a part of a scene, and a processor, which is configured to detect a location of an eye of a person in the scene and to control the projector so as to reduce an intensity of the projected content in an area of the eye.
Other embodiments of the invention provide methods for carrying out the function of the above-described apparatus.
For a better understanding of the present invention, reference is made to the detailed description of the invention, by way of example, which is to be read in conjunction with the following drawings, wherein like elements are given like reference numerals, and wherein:
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the various principles of the present invention. It will be apparent to one skilled in the art, however, that not all these details are necessarily always needed for practicing the present invention. In this instance, well-known circuits, control logic, and the details of computer program instructions for conventional algorithms and processes have not been shown in detail in order not to obscure the general concepts unnecessarily.
As used herein, the term “content projection” may encompass establishment of an image of the content onto a wearable transparent monitor, such as see-through eyeglasses, and thus invisible to anyone other than the person wearing the glasses, or onto a physical object that is visible to anyone interacting with the object. The term is not limited to the above examples. It may encompass forming an image by many means, including retinal projection, projection onto see-through glasses, projection of the image into a virtual space, for example as a hologram, and other techniques for creating augmented reality.
System Architecture.
Turning now to the drawings, reference is initially made to
The 3-D camera 12 and the 2-D camera 14 are cooperative with a content projector 16, all under the control of a processor, such as a computer 18.
A suitable unit for use in the system 10 that bundles the 3-D camera 12 and the 2-D camera 14 is the PrimeSensor™ Reference Design, available from PrimeSense Corporation, 104 Cambay Conn., Cary N.C., 27513, U.S.A. The content projector 16 may be the PicoP® display engine, available from MicroVision, Inc., 6222 185th Ave NE Redmond Wash., 98052. In some embodiments, the 3-D camera 12 and the 2-D camera 14 may be integral with the content projector 16 as a modification of the PrimeSensor Reference Design. In one embodiment, the 3-D camera 12 is an integrated module that includes an IR projector, which projects a pattern of spots onto the object and captures an image of the projected pattern. Alternatively, the IR projector, may be embodied as a separate module (not shown). The IR projector may be realized according to the teachings of U.S. Provisional Applications 61/372,729 (filed Aug. 11, 2010) and 61/425,788 (filed Dec. 22, 2010), as well as in PCT International Publication WO 2010/020380, all of which are herein incorporated by reference. These provisional and PCT applications also teach how to reuse the scanning hardware to project both the IR required for depth mapping and the visible content.
The processor may analyze the scene using the teachings of commonly assigned copending U.S. Patent Application Publication 2011/0293137, entitled “Analysis of Three-Dimensional Scenes”, which is herein incorporated by reference.
The computer 18 may comprise a general-purpose computer processor, which is programmed in software to carry out the functions described hereinbelow. The software may be downloaded to the processor in electronic form, over a network, for example, or it may alternatively be provided on non-transitory tangible storage media, such as optical, magnetic, or electronic memory media. Alternatively or additionally, some or all of the image functions may be implemented in dedicated hardware, such as a custom or semi-custom integrated circuit or a programmable digital signal processor (DSP). Although the computer 18 is shown in
The computer 18 may execute programs such as Nite™ Middleware, available from PrimeSense, in cooperation with the PrimeSensor Reference Design. For example, the PrimeSensor Reference Design supplies an application layer in the computer 18 with control widgets, thereby providing an application programming interface (API) that translates user gestures or postures into known deterministic application inputs. The Middleware performs image processing operations on data generated by the components of the system 10, including the 3-D camera 12 with its IR projector, and the 2-D camera 14 in order to reconstruct 3-dimensional maps of a user 20 and acquired scenes. The term “3-dimensional map” refers to a set of 3-dimensional coordinates representing the surface of a given object. One form of 3-dimensional map is referred to as a depth image or depth map, in which each pixel has a value indicating the distance from the camera to the corresponding point in the scene, rather than the brightness and color of the point as in a 2-dimensional image. The computer 18 then computes the three-dimensional coordinates of points on the surface of the control entity by triangulation, based on transverse shifts of the spots in the pattern.
In typical applications, information captured by the 3-D camera 12 is processed by the computer 18, which drives the content projector 16. The computer 18 may operate according to a program that is designed to create a natural or contrived experience for the user. As shown in
Furthermore, as the interaction of the user 20 with the book 22 and the sale offer 24 evolves, for example, by the user 20 grasping the book 22, a gaze identification module executing in the computer 18 may recognize that the user 20 is looking at the book 22. By processing the acquired 2-D images, the book title may be recognized and interpreted in the system 10. Then, computing optimal projection parameters, a book review may be projected onto the book 22. The user 20 could scroll and interact with the projected book review as if he were viewing it on a display screen. In this way, the system 10, cooperatively with the user 20, converts the book 22 in an ad hoc fashion into a virtual information screen for the benefit of the user a20.
The system 10 optionally includes a display screen 28 and conventional input devices such as a keyboard 30 and mouse 32, which may present a user interface for administrative use, e.g., system configuration, and for operational control of the system 10 by the user 20.
Reference is now made to
The position and attitude of the user may be taken into consideration when computing projection parameters. For example, as noted above, the gaze vector toward the projected content may vary as the user moves about in the scene. The projection parameters may be accordingly adjusted to compensate for such variations, e.g., by adjusting for scale, parallax, and similar distortions, so as to simulate a realistic experience for the user. One example of such adjustment is a correction for the fact that 3-dimensional objects appear differently when viewed from different directions, i.e., different sides of the object or different 2-D projections of the object become apparent to the observer. The projection content can be adjusted as a function of the gaze vector and user position relative to virtual object, thus creating a realistic experience of the object actually being in the presence of the observer. Gaze direction can be determined by methods known in art. For example, in the case of a device embedded in see-through glasses, head position orientation is obtainable by rigid registration of the world relative to the device. Gaze direction can also be measured, for example, using eye-tracking products available from Tobii Technology, Inc., 510 N, Washington Street, Suite 200, Falls Church, Va. 22046. Gaze may then be translated into object coordinates using 3D information obtained by the sensor.
Object Awareness.
Techniques for identifying and tracking body parts are known from commonly assigned U.S. Patent Application Publication No. 2011/0052006, entitled “Extraction of Skeletons from 3-D Maps”, which is herein incorporated by reference. Essentially this is accomplished by receiving a temporal sequence of depth maps of a scene containing a humanoid form. A digital processor processes at least one of the depth maps so as to find a location of a designated body part, such as the head or hand estimates dimensions of the humanoid form based on the location. The processor tracks movements of the humanoid form over the sequence using the estimated dimensions. These teachings are employed in the above-mentioned Nite Middleware, and may be enhanced by linking other known recognition routines by those skilled in the art.
For example, in the case of identifying the head of the body, the processor may segment and analyzes a 3-dimensional form to identify right and left arms, and then search the space between the arms in order to find the head. Additionally or alternatively recognition techniques may be used. The depth maps may be registered with 2-dimensional images of the head or other object. The processor may apply a pattern or face recognition technique to identify the face of a humanoid form in a 2-dimensional image. The face location in the 2-dimensional image is then correlated with the location of the head of the 3-dimensional form. Using the same techniques, an entire scene may be analyzed, segmented, and known categories of objects identified as candidates for projection of images thereon.
In one embodiment, which is shown in
Object Processor.
Reference is now made to
A depth processor 64 processes the information captured by the 3-D camera 12 (
Depth processor 64 receives input IR data from 3-D camera 12 (
In parallel with the depth input and processing operations, a color processing block 76 receives input color video data from the 2-D camera 14 (
The unit 74 acts as a buffer level between the various data suppliers and a USB controller 82. The unit 74 packs and formats the various data types according to different classes (such as a USB video class and a USB audio class), and also serves to prevent data loss due to USB bandwidth glitches. It arranges the data into USB packets according to the USB protocol and format prior to transferring them to the USB controller.
A high-bandwidth bus, such as an Advanced High-performance Bus (AHB) matrix 84, is used to carry data between the components of the processing device 50, and specifically for conveying data from the unit 74 to the USB controller 82 for transfer to the host computer 54. (AHB is a bus protocol promulgated by ARM Ltd., of Cambridge, England.) When there are packets ready in the unit 74 and space available in the internal memory of USB controller 82, the USB controller 82 uses direct memory access (DMA) to read data from memory 72, memory 80, and an audio FIFO memory 86 via an AHB slave module 88 and the matrix 84. The USB controller 82 multiplexes the color, depth and audio data into a single data stream for output via the USB port 52 to the host computer 54.
For the purpose of USB communications, they processing device 50 comprises a USB physical layer interface, PHY 90, which may be operated by the USB controller 82 to communicate via a suitable USB cable with a USB port of the host computer 54. The timing of the USB PHY is controlled by a crystal oscillator 92 and a phase-locked loop 94 (PLL), as is known in the art.
Alternatively, USB controller 86 may optionally communicate with the host computer via a USB 2.0 Transceiver Macrocell Interface (UTMI) and an external PHY 96.
Various external devices may connect with the processing device 50 cooperatively with the host computer 54, including a projector control module 98, which accepts instructions from the processing device 50 and the host computer 54 to effect a desired image projection onto specified coordinates in space.
The controller 68 is responsible for managing the functions of the processing device 50, including boot-up, self-test, configuration, power and interface management, and parameter adjustment.
The controller 68 may comprise a digital signal processor (DSP) core 100 and an AHB master 102 for controlling data movement on the matrix 84. Typically, controller 68 boots from a boot read-only memory 104, and then loads program code from a flash memory (not shown) via a flash memory interface 106 into instruction random-access memory 60 and data memory 62. The controller 68 may, in addition, have a test interface 108, such as a Joint Test Action Group (JTAG) interface, for purposes of debugging by an external computer 110.
The controller 68 distributes configuration data and parameters to other components of the processing device 50 via a register configuration interface 112, such as an Advanced Peripheral Bus (APB), to which the controller is connected through the matrix 84 and an APB bridge 114.
Further details of the processing device 50 are disclosed in the above-noted PCT International Publication WO 2010/004542.
Object Analysis.
Continuing to refer to
The algorithm executed by the object analyzer 56 may be dictated by an application program in the host computer 54. For example, the object analyzer 56 may be instructed to search for and report one or more known objects in the scene that are specified in the database 58. The host computer 54 may thereupon instruct the content projector 16 (
The data communicated by the object analyzer 56 with respect to an identified object typically includes the size and location of the object, as well as its orientation, preferably with six degrees of freedom, including scale, pitch, yaw and angular rotation with respect to a reference system of coordinates. This information allows the projector to compensate for distortions by suitably scaling and contorting a projected image so as to be project it onto the selected object such that the viewer sees an image that is substantially distortion-free. Configuration of a projected image is known, e.g., from U.S. Patent Application Publication No. 20110081072, entitled “Image Processing Device, Image Processing Method, and Program”. The image may be configured in software in order to avoid the expense of complex optical arrangements and to more easily achieve freedom from such effects as off-axis image distortion Alternatively, As noted above, commercially available projects may provide their own compensation for distortion control.
Reference is now made to
Assume that the viewer is located in a bookshop. At initial step 116 an application program executing in the host computer 54 would like to identify an open book displaying textual information. This is a 3-dimensional object having a known definition in the database 58 that includes at least one generally light-colored planar surface. The 3-D camera 12 is enabled and a 3-dimensional scene captured in the processing device 50. The object analyzer 56 evaluates the scene, locates and identifies objects in 3-dimensional space.
At decision step 118 it is determined whether a planar surface has been located in the scene.
Control now proceeds to decision step 120, where it is determined if the planar surface meets criteria for a book. The criteria may involve, inter alia, size, proximity to certain other objects, and geometric details corresponding to a closed or open book.
If the determination at decision step 120 is affirmative, then control proceeds to final step 122. The coordinates and orientation of the book are reported by the object analyzer 56 to the controller 68, which instructs the projector control module 98 cooperatively with the host computer 54 to display an application-determined image (MENU-1) on the identified book. The image may contain, for example, options to purchase the item, or obtain additional details, for example book reviews, and popularity ratings. Indeed, if the 3-D camera 12 was successful in capturing the title of the book, the additional details may be included in the projected image. It is assumed that the host computer 54 has access to a local or distributed database or can make automatic inquiries via the Internet.
The coordinates and other characteristics of the book (or of any other object onto which an image is to be projected) can also be used in controlling projection parameters such as the intensity of light projected in the image. Thus, for example, the projector may increase the intensity of the projected light when the object is relatively far from the projector and decrease it for nearby objects. Additionally or alternatively, the reflectivity of the object may be assessed (using image data from camera 36, for example), and the intensity of the projected light may be increased when projected onto less reflective objects and decreased for more reflective objects.
If the determination at decision step 120 is negative, then control proceeds to decision step 124. A determination is made if more objects are present in the scene for processing.
If the determination at decision step 124 is affirmative, then control returns to decision step 118.
If the determination at decision step 124 is negative, then a second state of the method commences. It is assumed that the application program falls through to a secondary option, in which an image is projected on the user's hand, if visible to the 3-D camera 12.
Control now proceeds to decision step 126, where it is determined if a body part is present in the scene. This may be accomplished using the teachings of the above-noted U.S. Patent Application Publication No. 2011/0052006.
If the determination at decision step 126 is affirmative, then control proceeds to decision step 128, where it is determined if the body part is a hand.
If the determination at decision step 128 is affirmative, then control proceeds to final step 130, which is similar to final step 122. However, a different menu (MENU-2) is now projected on the hand, which may include, for example, control options for the governing computer application. In both final step 122 and final step 130 the image is configured so as to create a natural feeling on the part of the user when interacting with the content.
Alternatively or additionally, the object analyzer may determine whether the body part in question is a head and if so, may instruct the projector to reduce or turn off the projected intensity in the area of the head. This option is described in greater detail hereinbelow with reference to
If the determination at decision step 128 is negative, then control proceeds to decision step 132. A determination is made if more objects are present in the scene for processing.
If the determination at decision step 132 is affirmative, then control returns to decision step 126. Otherwise, control passes to final step 134, in which a conventional menu display is presented on a display screen. Final step 134 represents a failure to identify a suitable external object for projection of an image thereon. It will be appreciated that the method shown in
This embodiment is similar to the first embodiment, except a convenient virtual surface is provided for projection of images and for access by the user. Reference is now made to
In one mode of operation, the projector 140 may create an enlarged version of information displayed on the screen 136.
In another mode of operation the sensing device 142 captures an external scene. The mobile information device 138 is configured to perform the method of scene analysis described above with reference to
In the first embodiment, images have been described as projections onto a physical object, e.g., a book or a hand. In this embodiment, the projector may be embodied as a device that projects content onto a wearable monitor, such as eye-glasses. In this embodiment final step 122 and final step 130 are modified in the method of
Reference is now made to
In the example of
While the image 156 is actually established within the wearable monitor 154, in some embodiments it may be perceived by the user 152 as being superimposed in an external region of space as shown in
As shown in
A scanning mirror 176 (or a pair of scanning mirrors—not shown) scans the beams from sources 170 and 172, typically in a raster pattern, over the field of view of camera 178. While the beams are scanned, projector control 44 in processor 40 (
The projector shown in
This principles of this embodiment may be applied using other types of imaging and projection devices and are not limited to the particular sort of scanning projector and mapping device that are described above. For example, other types of mapping and imaging devices, as well as other image analysis techniques, which may operate on either a 2-D image captured by a suitable capture device or a 3-D map, may be applied in identifying the area of the eyes for this purpose. Similarly, substantially any suitable type of electronically-driven projector (including standard video projectors) can be controlled in this manner to reduce intensity in the area of the eyes, as long as an image or map of the area onto which the projector casts its beam is registered in the frame of reference of the projector. Thus, when the location of the head and/or eyes that is found in the image or map, the corresponding part of the projected beam can be dimmed accordingly.
It will be appreciated by persons skilled in the art that the present invention is not limited to what has been particularly shown and described hereinabove. Rather, the scope of the present invention includes both combinations and sub-combinations of the various features described hereinabove, as well as variations and modifications thereof that are not in the prior art, which would occur to persons skilled in the art upon reading the foregoing description.
This application is a continuation-in-part of PCT Patent Application PCT/IB2011/053192, filed Jul. 18, 2011, which claims the benefit of U.S. Provisional Application No. 61/365,788, filed Jul. 20, 2010. This application is related to another U.S. patent application, filed on even date, entitled “Adaptive Projector”. All of these related applications are incorporated herein by reference.
Number | Name | Date | Kind |
---|---|---|---|
4550250 | Mueller et al. | Oct 1985 | A |
4789921 | Aho | Dec 1988 | A |
4988981 | Zimmerman et al. | Jan 1991 | A |
5495576 | Ritchey | Feb 1996 | A |
5588139 | Lanier et al. | Dec 1996 | A |
5594469 | Freeman et al. | Jan 1997 | A |
5846134 | Latypov | Dec 1998 | A |
5852672 | Lu | Dec 1998 | A |
5862256 | Zetts et al. | Jan 1999 | A |
5864635 | Zetts et al. | Jan 1999 | A |
5870196 | Lulli et al. | Feb 1999 | A |
5917937 | Szeliski et al. | Jun 1999 | A |
5973700 | Taylor et al. | Oct 1999 | A |
6002808 | Freeman | Dec 1999 | A |
6084979 | Kanade et al. | Jul 2000 | A |
6243054 | DeLuca | Jun 2001 | B1 |
6252988 | Ho | Jun 2001 | B1 |
6256033 | Nguyen | Jul 2001 | B1 |
6262740 | Lauer et al. | Jul 2001 | B1 |
6345111 | Yamaguchi et al. | Feb 2002 | B1 |
6345893 | Fateh et al. | Feb 2002 | B2 |
6452584 | Walker et al. | Sep 2002 | B1 |
6456262 | Bell | Sep 2002 | B1 |
6507353 | Huard et al. | Jan 2003 | B1 |
6512838 | Rafii et al. | Jan 2003 | B1 |
6519363 | Su et al. | Feb 2003 | B1 |
6559813 | DeLuca et al. | May 2003 | B1 |
6681031 | Cohen et al. | Jan 2004 | B2 |
6686921 | Rushmeier | Feb 2004 | B1 |
6690370 | Ellenby et al. | Feb 2004 | B2 |
6741251 | Malzbender | May 2004 | B2 |
6791540 | Baumberg | Sep 2004 | B1 |
6803928 | Bimber et al. | Oct 2004 | B2 |
6853935 | Satoh et al. | Feb 2005 | B2 |
6857746 | Dyner | Feb 2005 | B2 |
6977654 | Malik et al. | Dec 2005 | B2 |
7003134 | Covell et al. | Feb 2006 | B1 |
7013046 | Kawamura et al. | Mar 2006 | B2 |
7023436 | Segawa et al. | Apr 2006 | B2 |
7042440 | Pryor et al. | May 2006 | B2 |
7042442 | Kanevsky et al. | May 2006 | B1 |
7151530 | Roeber et al. | Dec 2006 | B2 |
7170492 | Bell | Jan 2007 | B2 |
7215815 | Honda | May 2007 | B2 |
7227526 | Hildreth et al. | Jun 2007 | B2 |
7259747 | Bell | Aug 2007 | B2 |
7264554 | Bentley | Sep 2007 | B2 |
7289227 | Smetak et al. | Oct 2007 | B2 |
7301648 | Foxlin | Nov 2007 | B2 |
7302099 | Zhang et al. | Nov 2007 | B2 |
7333113 | Gordon | Feb 2008 | B2 |
7340077 | Gokturk | Mar 2008 | B2 |
7348963 | Bell | Mar 2008 | B2 |
7358972 | Gordon et al. | Apr 2008 | B2 |
7370883 | Basir et al. | May 2008 | B2 |
7427996 | Yonezawa et al. | Sep 2008 | B2 |
7428542 | Fink et al. | Sep 2008 | B1 |
7474256 | Ohta et al. | Jan 2009 | B2 |
7526120 | Gokturk et al. | Apr 2009 | B2 |
7536032 | Bell | May 2009 | B2 |
7573480 | Gordon | Aug 2009 | B2 |
7576727 | Bell | Aug 2009 | B2 |
7580572 | Bang et al. | Aug 2009 | B2 |
7590941 | Wee et al. | Sep 2009 | B2 |
7688998 | Tuma et al. | Mar 2010 | B2 |
7696876 | Dimmer et al. | Apr 2010 | B2 |
7724250 | Ishii et al. | May 2010 | B2 |
7762665 | Vertegaal et al. | Jul 2010 | B2 |
7774155 | Sato et al. | Aug 2010 | B2 |
7812842 | Gordon | Oct 2010 | B2 |
7821541 | Delean | Oct 2010 | B2 |
7840031 | Albertson et al. | Nov 2010 | B2 |
7844914 | Andre et al. | Nov 2010 | B2 |
7925549 | Looney et al. | Apr 2011 | B2 |
7971156 | Albertson et al. | Jun 2011 | B2 |
8150142 | Freedman et al. | Apr 2012 | B2 |
8166421 | Magal et al. | Apr 2012 | B2 |
8249334 | Berliner et al. | Aug 2012 | B2 |
8290208 | Kurtz et al. | Oct 2012 | B2 |
8368647 | Lin | Feb 2013 | B2 |
8390821 | Shpunt et al. | Mar 2013 | B2 |
8400494 | Zalevsky et al. | Mar 2013 | B2 |
8493496 | Freedman et al. | Jul 2013 | B2 |
8514221 | King et al. | Aug 2013 | B2 |
20020057383 | Iwamura | May 2002 | A1 |
20020071607 | Kawamura et al. | Jun 2002 | A1 |
20020158873 | Williamson | Oct 2002 | A1 |
20030057972 | Pfaff et al. | Mar 2003 | A1 |
20030063775 | Rafii et al. | Apr 2003 | A1 |
20030088463 | Kanevsky | May 2003 | A1 |
20030156756 | Gokturk et al. | Aug 2003 | A1 |
20030185444 | Honda | Oct 2003 | A1 |
20030235341 | Gokturk et al. | Dec 2003 | A1 |
20040046744 | Rafii et al. | Mar 2004 | A1 |
20040104935 | Williamson et al. | Jun 2004 | A1 |
20040135744 | Bimber et al. | Jul 2004 | A1 |
20040174770 | Rees | Sep 2004 | A1 |
20040183775 | Bell | Sep 2004 | A1 |
20040184640 | Bang et al. | Sep 2004 | A1 |
20040184659 | Bang et al. | Sep 2004 | A1 |
20040258314 | Hashimoto | Dec 2004 | A1 |
20050031166 | Fujimura et al. | Feb 2005 | A1 |
20050088407 | Bell et al. | Apr 2005 | A1 |
20050089194 | Bell | Apr 2005 | A1 |
20050110964 | Bell et al. | May 2005 | A1 |
20050117132 | Agostinelli | Jun 2005 | A1 |
20050122308 | Bell et al. | Jun 2005 | A1 |
20050162381 | Bell et al. | Jul 2005 | A1 |
20050190972 | Thomas et al. | Sep 2005 | A1 |
20050254726 | Fuchs et al. | Nov 2005 | A1 |
20050265583 | Covell et al. | Dec 2005 | A1 |
20060010400 | Dehlin et al. | Jan 2006 | A1 |
20060092138 | Kim et al. | May 2006 | A1 |
20060110008 | Vertegaal et al. | May 2006 | A1 |
20060115155 | Lui et al. | Jun 2006 | A1 |
20060139314 | Bell | Jun 2006 | A1 |
20060149737 | Du et al. | Jul 2006 | A1 |
20060159344 | Shao et al. | Jul 2006 | A1 |
20060187196 | Underkoffler et al. | Aug 2006 | A1 |
20060239670 | Cleveland | Oct 2006 | A1 |
20060248475 | Abrahamsson | Nov 2006 | A1 |
20070078552 | Rosenberg | Apr 2007 | A1 |
20070154116 | Shieh | Jul 2007 | A1 |
20070230789 | Chang et al. | Oct 2007 | A1 |
20080062123 | Bell | Mar 2008 | A1 |
20080094371 | Forstall et al. | Apr 2008 | A1 |
20080123940 | Kundu et al. | May 2008 | A1 |
20080150890 | Bell et al. | Jun 2008 | A1 |
20080150913 | Bell et al. | Jun 2008 | A1 |
20080170776 | Albertson et al. | Jul 2008 | A1 |
20080236902 | Imaizumi | Oct 2008 | A1 |
20080252596 | Bell et al. | Oct 2008 | A1 |
20080256494 | Greenfield | Oct 2008 | A1 |
20080260250 | Vardi | Oct 2008 | A1 |
20080287189 | Rabin | Nov 2008 | A1 |
20090009593 | Cameron et al. | Jan 2009 | A1 |
20090027335 | Ye | Jan 2009 | A1 |
20090027337 | Hildreth | Jan 2009 | A1 |
20090031240 | Hildreth | Jan 2009 | A1 |
20090040215 | Afzulpurkar et al. | Feb 2009 | A1 |
20090077504 | Bell et al. | Mar 2009 | A1 |
20090078473 | Overgard et al. | Mar 2009 | A1 |
20090083122 | Angell et al. | Mar 2009 | A1 |
20090083622 | Chien et al. | Mar 2009 | A1 |
20090096783 | Shpunt et al. | Apr 2009 | A1 |
20090183125 | Magal et al. | Jul 2009 | A1 |
20090195392 | Zalewski | Aug 2009 | A1 |
20090228841 | Hildreth | Sep 2009 | A1 |
20090256817 | Perlin et al. | Oct 2009 | A1 |
20090297028 | De Haan | Dec 2009 | A1 |
20100002936 | Khomo et al. | Jan 2010 | A1 |
20100007717 | Spektor et al. | Jan 2010 | A1 |
20100034457 | Berliner et al. | Feb 2010 | A1 |
20100036717 | Trest | Feb 2010 | A1 |
20100053151 | Marti et al. | Mar 2010 | A1 |
20100071965 | Hu et al. | Mar 2010 | A1 |
20100149096 | Migos et al. | Jun 2010 | A1 |
20100164897 | Morin et al. | Jul 2010 | A1 |
20100177929 | Kurtz et al. | Jul 2010 | A1 |
20100177933 | Willmann et al. | Jul 2010 | A1 |
20100199228 | Latta et al. | Aug 2010 | A1 |
20100234094 | Gagner et al. | Sep 2010 | A1 |
20100235786 | Maizels et al. | Sep 2010 | A1 |
20110006978 | Yuan | Jan 2011 | A1 |
20110018795 | Jang | Jan 2011 | A1 |
20110029918 | Yoo et al. | Feb 2011 | A1 |
20110052006 | Gurman et al. | Mar 2011 | A1 |
20110081072 | Kawasaki et al. | Apr 2011 | A1 |
20110164032 | Shadmi | Jul 2011 | A1 |
20110164141 | Tico et al. | Jul 2011 | A1 |
20110193939 | Vassigh et al. | Aug 2011 | A1 |
20110211754 | Litvak et al. | Sep 2011 | A1 |
20110225536 | Shams et al. | Sep 2011 | A1 |
20110227820 | Haddick et al. | Sep 2011 | A1 |
20110231757 | Haddick et al. | Sep 2011 | A1 |
20110248914 | Sherr | Oct 2011 | A1 |
20110254765 | Brand | Oct 2011 | A1 |
20110254798 | Adamson et al. | Oct 2011 | A1 |
20110260965 | Kim et al. | Oct 2011 | A1 |
20110261058 | Luo | Oct 2011 | A1 |
20110279397 | Rimon et al. | Nov 2011 | A1 |
20110291926 | Gokturk et al. | Dec 2011 | A1 |
20110292036 | Sali et al. | Dec 2011 | A1 |
20110293137 | Gurman et al. | Dec 2011 | A1 |
20110310010 | Hoffnung et al. | Dec 2011 | A1 |
20120001875 | Li et al. | Jan 2012 | A1 |
20120078614 | Galor et al. | Mar 2012 | A1 |
20120169583 | Rippel et al. | Jul 2012 | A1 |
20120202569 | Maizels et al. | Aug 2012 | A1 |
20120204133 | Guendelman et al. | Aug 2012 | A1 |
20120223882 | Galor et al. | Sep 2012 | A1 |
20120313848 | Galor et al. | Dec 2012 | A1 |
20130014052 | Frey et al. | Jan 2013 | A1 |
20130044053 | Galor et al. | Feb 2013 | A1 |
20130055120 | Galor et al. | Feb 2013 | A1 |
20130055150 | Galor | Feb 2013 | A1 |
20130058565 | Rafii et al. | Mar 2013 | A1 |
20130106692 | Maizels et al. | May 2013 | A1 |
20130107021 | Maizels et al. | May 2013 | A1 |
20130127854 | Shpunt et al. | May 2013 | A1 |
20130147686 | Clavin et al. | Jun 2013 | A1 |
20130155070 | Luo | Jun 2013 | A1 |
20130207920 | Mccann et al. | Aug 2013 | A1 |
20130222239 | Galor | Aug 2013 | A1 |
20130263036 | Berenson et al. | Oct 2013 | A1 |
20130265222 | Berenson et al. | Oct 2013 | A1 |
20130283208 | Bychkov et al. | Oct 2013 | A1 |
20130283213 | Bychkov et al. | Oct 2013 | A1 |
Number | Date | Country |
---|---|---|
2002228413 | Aug 2002 | JP |
2008090807 | Apr 2008 | JP |
2009151380 | Jul 2009 | JP |
2010067062 | Mar 2010 | JP |
9935633 | Jul 1999 | WO |
03071410 | Aug 2003 | WO |
2004107272 | Dec 2004 | WO |
2005003948 | Jan 2005 | WO |
2005094958 | Oct 2005 | WO |
2007078639 | Jul 2007 | WO |
2007135376 | Nov 2007 | WO |
2012107892 | Aug 2012 | WO |
Entry |
---|
CN Application # 201180027794.5 Office Action dated Jul. 3, 2014. |
Hart, D., U.S. Appl. No. 09/616,606 “Method and System for High Resolution , Ultra Fast 3-D Imaging” filed Jul. 14, 2000. |
International Application PCT/IL2007/000306 Search Report dated Oct. 2, 2008. |
International Application PCT/IL2007/000574 Search Report dated Sep. 10, 2008. |
International Application PCT/IL2006/000335 Preliminary Report on Patentability dated Apr. 24, 2008. |
Avidan et al., “Trajectory triangulation: 3D reconstruction of moving points from amonocular image sequence”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 22, No. 4, pp. 348-3537, Apr. 2000. |
Leclerc et al., “The direct computation of height from shading”, The Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 552-558, USA, Jun. 1991. |
Zhang et al., “Shape from intensity gradient”, IEEE Transactions on Systems, Man and Cybernetics—Part A: Systems and Humans, vol. 29, No. 3, pp. 318-325, May 1999. |
Zhang et al., “Height recovery from intensity gradients”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 508-513, Jun. 21-23, 1994. |
Horn, B., “Height and gradient from shading”, International Journal of Computer Vision, vol. 5, No. 1, pp. 37-76, Aug. 1990. |
Bruckstein, A., “On shape from shading”, Computer Vision, Graphics & Image Processing, vol. 44, pp. 139-154, year 1988. |
Zhang et al., “Rapid Shape Acquisition Using Color Structured Light and Multi-Pass Dynamic Programming”, 1st International Symposium on 3D Data Processing Visualization and Transmission (3DPVT), Italy, Jul. 2002. |
Besl, P., “Active, Optical Range Imaging Sensors”, Machine vision and applications, vol. 1, pp. 127-152, year 1988. |
Horn et al., “Toward optimal structured light patterns”, Proceedings of International Conference on Recent Advances in 3D Digital Imaging and Modeling, pp. 28-37, Ottawa, Canada, May 1997. |
Goodman, J.W., “Statistical Properties of Laser Speckle Patterns”, Laser Speckle and Related Phenomena, pp. 9-75, Springer-Verlag, Berlin Heidelberg, 1975. |
Asada et al., “Determining Surface Orientation by Projecting a Stripe Pattern”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 10, No. 5, pp. 749-754, Sep. 1988. |
Winkelbach et al., “Shape from Single Stripe Pattern Illumination”, Luc Van Gool (Editor), (DAGM 2002), Lecture Notes in Computer Science 2449, p. 240-247, Springer 2002. |
Koninckx et al., “Efficient, Active 3D Acquisition, based on a Pattern-Specific Snake”, Luc Van Gool (Editor), (DAGM 2002), Lecture Notes in Computer Science 2449, pp. 557-565, Springer 2002. |
Kimmel et al., “Analyzing and synthesizing images by evolving curves with the Osher-Sethian method”, International Journal of Computer Vision, vol. 24, No. 1, pp. 37-56, year 1997. |
Zigelman et al., “Texture mapping using surface flattening via multi-dimensional scaling”, IEEE Transactions on Visualization and Computer Graphics, vol. 8, No. 2, pp. 198-207, Apr. 2002. |
Dainty, J.C., “Introduction”, Laser Speckle and Related Phenomena, pp. 1-7, Springer-Verlag, Berlin Heidelberg, 1975. |
Mendlovic et al., “Composite harmonic filters for scale, projection and shift invariant pattern recognition”, Applied Optics Journal, vol. 34, No. 2, Jan. 10, 1995. |
Fua et al., “Human Shape and Motion Recovery Using Animation Models”, 19th Congress, International Society for Photogrammetry and Remote Sensing, Amsterdam, The Netherlands, Jul. 2000. |
Allard et al., “Marker-less Real Time 3D modeling for Virtual Reality”, Immersive Projection Technology, Iowa State University, year 2004. |
Howe et al., “Bayesian Reconstruction of 3D Human Motion from Single-Camera Video”, Advanced in Neural Information Processing Systems, vol. 12, pp. 820-826, USA 1999. |
Li et al., “Real-Time 3D Motion Tracking with Known Geometric Models”, Real-Time Imaging Journal, vol. 5, pp. 167-187, Academic Press 1999. |
Grammalidis et al., “3-D Human Body Tracking from Depth Images Using Analysis by Synthesis”, Proceedings of the IEEE International Conference on Image Processing (ICIP2001), pp. 185-188, Greece, Oct. 7-10, 2001. |
Segen et al., “Shadow gestures: 3D hand pose estimation using a single camera”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 479-485, Fort Collins, USA, 1999. |
Vogler et al., “ASL recognition based on a coupling between HMMs and 3D motion analysis”, Proceedings of IEEE International Conference on Computer Vision, pp. 363-369, Mumbai, India, 1998. |
Nam et al., “Recognition of Hand Gestures with 3D, Nonlinear Arm Movements”, Pattern Recognition Letters, vol. 18, No. 1, pp. 105-113, Elsevier Science B.V. 1997. |
Nesbat, S., “A System for Fast, Full-Text Entry for Small Electronic Devices”, Proceedings of the 5th International Conference on Multimodal Interfaces, ICMI 2003, Vancouver, Nov. 5-7, 2003. |
Ascension Technology Corporation, “Flock of Birds: Real-Time Motion Tracking”, 2008. |
Segen et al., “Human-computer interaction using gesture recognition and 3D hand tracking”, ICIP 98, Proceedings of the IEEE International Conference on Image Processing, vol. 3, pp. 188-192, Oct. 4-7, 1998. |
Dekker, L., “Building Symbolic Information for 3D Human Body Modeling from Range Data”, Proceedings of the Second International Conference on 3D Digital Imaging and Modeling, IEEE computer Society, pp. 388-397, 1999. |
Holte et al., “Gesture Recognition using a Range Camera”, Technical Report CVMT-07-01 ISSN 1601-3646, Feb. 2007. |
Cheng et al., “Articulated Human Body Pose Inference from Voxel Data Using a Kinematically Constrained Gaussian Mixture Model”, CVPR EHuM2: 2nd Workshop on Evaluation of Articulated Human Motion and Pose Estimation, 2007. |
Microvision Inc., “PicoP® Display Engine—How it Works”, 1996-2012. |
Primesense Corporation, “PrimeSensor Nite 1.1”, USA, year 2010. |
ARM LTD., “AMBA Specification: AHB”, Version 2, pp. 35-92, year 1999. |
Commission Regulation (EC) No. 1275/2008, Official Journal of the European Union, Dec. 17, 2008. |
Primesense, “Natural Interaction”, YouTube Presentation, Jun. 9, 2010 http://www.youtube.com/watch?v=TzLKsex43z1˜. |
Manning et al., “Foundations of Statistical Natural Language Processing”, chapters 6,7,9 and 12, MIT Press 1999. |
U.S. Appl. No. 12/762,336 Official Action dated May 15, 2012. |
Tobii Technology, “The World Leader in Eye Tracking and Gaze Interaction”, Mar. 2012. |
Noveron, “Madison video eyewear”, year 2012. |
International Application PCT/IB2012/050577 Search Report dated Aug. 6, 2012. |
U.S. Appl. No. 12/683,452 Official Action dated Sep. 7, 2012. |
Koutek, M., “Scientific Visualization in Virtual Reality: Interaction Techniques and Application Development”, PhD Thesis, Delft University of Technology, 264 pages, Jan. 2003. |
Azuma et al., “Recent Advances in Augmented Reality”, IEEE Computer Graphics and Applications, vol. 21, issue 6, pp. 34-47, Nov. 2001. |
Breen et al., “Interactive Occlusion and Collision of Real and Virtual Objects in Augmented Reality”, Technical Report ECRC-95-02, ECRC, Munich, Germany, 22 pages, year 1995. |
Burdea et al., “A Distributed Virtual Environment with Dextrous Force Feedback”, Proceedings of Interface to Real and Virtual Worlds Conference, pp. 255-265, Mar. 1992. |
Bleiwess et al., “Fusing Time-of-Flight Depth and Color for Real-Time Segmentation and Tracking”, Dyn3D 2009, Lecture Notes in Computer Science 5742, pp. 58-69, Jena, Germany, Sep. 9, 2009. |
Bleiwess et al., “Markerless Motion Capture Using a Single Depth Sensor”, SIGGRAPH Asia 2009, Yokohama, Japan, Dec. 16-19, 2009. |
Bevilacqua et al., “People Tracking Using a Time-Of-Flight Depth Sensor”, Proceedings of the IEEE International Conference on Video and Signal Based Surveillance, Sydney, Australia, Nov. 22-24, 2006. |
Bradski, G., “Computer Vision Face Tracking for Use in a Perceptual User Interface”, Intel Technology Journal, vol. 2, issue 2 (2nd Quarter 2008). |
Comaniciu et al., “Kernel-Based Object Tracking”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, No. 5, pp. 564-577, May 2003. |
Gesturetec Inc., “Gesture Control Solutions for Consumer Devices”, Canada, 2009. |
Gokturk et al., “A Time-Of-Flight Depth Sensor—System Description, Issues and Solutions”, Proceedings of the 2004 Conference on Computer Vision and Patter Recognition Workshop (CVPRW'04), vol. 3, pp. 35, Jun. 27-Jul. 2, 2004. |
Grest et al., “Single View Motion Tracking by Depth and Silhouette Information”, SCIA 2007—Scandinavian Conference on Image Analysis, Lecture Notes in Computer Science 4522, pp. 719-729, Aalborg, Denmark, Jun. 10-14, 2007. |
Haritaoglu et al., “Ghost 3d: Detecting Body Posture and Parts Using Stereo”, Proceedings of the IEEE Workshop on Motion and Video Computing (MOTION'02), pp. 175-180, Orlando, USA, Dec. 5-6, 2002. |
Haritaoglu et al., “W4S : A real-time system for detecting and tracking people in 2<½>D”, ECCV 98—5th European conference on computer vision, vol. 1407, pp. 877-892, Freiburg , Germany, Jun. 2-6, 1998. |
Harville, M., “Stereo Person Tracking with Short and Long Term Plan-View Appearance Models of Shape and Color”, Proceedings of the IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSSS—2005), pp. 522-527, Como, Italy, Sep. 15-16, 2005. |
Holte, M., “Fusion of Range and Intensity Information for View Invariant Gesture Recognition”, IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW '08), pp. 1-7, Anchorage, USA, Jun. 23-28, 2008. |
Kaewtrakulpong et al., “An Improved Adaptive Background Mixture Model for Real-Time Tracking with Shadow Detection”, Proceedings of the 2nd European Workshop on Advanced Video Based Surveillance Systems (AVBS'01), Kingston, UK, Sep. 2001. |
Kolb et al., “ToF-Sensors: New Dimensions for Realism and Interactivity”, Proceedings of the IEEE Conference on Computer Vision and Patter Recognition Workshops, pp. 1-6, Anchorage, USA, Jun. 23-28, 2008. |
Kolsch et al., “Fast 2D Hand Tracking with Flocks of Features and Multi-Cue Integration”, IEEE Workshop on Real-Time Vision for Human Computer Interaction (at CVPR'04), Washington, USA, Jun. 27-Jul. 2, 2004. |
Krumm et al., “Multi-Camera Multi-Person Tracking for EasyLiving”, 3rd IEEE International Workshop on Visual Surveillance, Dublin, Ireland, Jul. 1, 2000. |
Leens et al., “Combining Color, Depth, and Motion for Video Segmentation”, ICVS 2009—7th International Conference on Computer Vision Systems, Liege, Belgium Oct. 13-15, 2009. |
MacCormick et al., “Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking”, ECCV '00—Proceedings of the 6th European Conference on Computer Vision—Part II , pp. 3-19, Dublin, Ireland, Jun. 26-Jul. 1, 2000. |
Malassiotis et al., “Real-Time Hand Posture Recognition Using Range Data”, Image and Vision Computing, vol. 26, No. 7, pp. 1027-1037, Jul. 2, 2008. |
Morano et al., “Structured Light Using Pseudorandom Codes”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, issue 3, pp. 322-327, Mar. 1998. |
Munoz-Salinas et al., “People Detection and Tracking Using Stereo Vision and Color”, Image and Vision Computing, vol. 25, No. 6, pp. 995-1007, Jun. 1, 2007. |
Nanda et al., “Visual Tracking Using Depth Data”, Proceedings of the 2004 Conference on Computer Vision and Patter Recognition Workshop, vol. 3, Washington, USA, Jun. 27-Jul. 2, 2004. |
Scharstein et al., “High-Accuracy Stereo Depth Maps Using Structured Light”, IEEE Conference on Computer Vision and Patter Recognition, vol. 1, pp. 195-2002, Madison, USA, Jun. 2003. |
Shi et al., “Good Features to Track”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 593-600, Seattle, USA, Jun. 21-23, 1994. |
Siddiqui et al., “Robust Real-Time Upper Body Limb Detection and Tracking”, Proceedings of the 4th ACM International Workshop on Video Surveillance and Sensor Networks, Santa Barbara, USA, Oct. 27, 2006. |
SoftKinetic S.A., IISU™—3D Gesture Recognition Platform for Developers of 3D Applications, Belgium, Brussels, 2007-2010. |
Sudderth et al., “Visual Hand Tracking Using Nonparametric Belief Propagation”, IEEE Workshop on Generative Model Based Vision at CVPR'04, Washington, USA, Jun. 27-Jul. 2, 2004. |
Tsap, L., “Gesture-Tracking in Real Time with Dynamic Regional Range Computation”, Real-Time Imaging, vol. 8, issue 2, pp. 115-126, Apr. 2002. |
Xu et al., “A Multi-Cue-Based Human Body Tracking System”, Proceedings of the 5ths International Conference on Computer Vision Systems (ICVS 2007), Germany, Mar. 21-24, 2007. |
Xu et al., “Human Detecting Using Depth and Gray Images”, Proceedings of the IEE Conference on Advanced Video and Signal Based Surveillance (AVSS'03), Miami, USA, Jul. 21-22, 2003. |
Yilmaz et al., “Object Tracking: A Survey”, ACM Computing Surveys, vol. 38, No. 4, article 13, Dec. 2006. |
Zhu et al., “Controlled Human Pose Estimation From Depth Image Streams”, IEEE Conference on Computer Vision and Patter Recognition Workshops, pp. 1-8, Anchorage, USA, Jun. 23-27, 2008. |
International Application PCT/IB2010/051055 Search Report dated Sep. 1, 2010. |
La Viola, J. Jr., “Whole-Hand and Speech Input in Virtual Environments”, Computer Science Department, Florida Atlantic University, USA, 1996. |
Martell, C., “Form: An Experiment in the Annotation of the Kinematics of Gesture”, Dissertation, Computer and Information Science, University of Pennsylvania, 2005. |
U.S. Appl. No. 12/352,622 Official Action dated Mar. 31, 2011. |
Prime Sense Inc., “Prime Sensor™ NITE 1.1 Framework Programmer's Guide”, Version 1.2, year 2009. |
Primesense Corporation, “PrimeSensor Reference Design 1.08”, USA, year 2010. |
International Application PCT/IB2011/053192 Search Report dated Dec. 6, 2011. |
U.S. Appl. No. 12/352,622 Official Action dated Sep. 30, 2011. |
Gordon et al., “The use of Dense Stereo Range Date in Augmented Reality”, Proceedings of the 1st International Symposium on Mixed and Augmented Reality (ISMAR), Darmstadt, Germany, pp. 1-10, Sep. 30-Oct. 1, 2002. |
Agrawala et al., “The two-user Responsive Workbench :support for collaboration through individual views of a shared space”, Proceedings on the 24th conference on computer graphics and interactive techniques (SIGGRAPH 97), Los Angeles, USA, pp. 327-332 , Aug. 3-8, 1997. |
Harman et al., “ Rapid 2D-to 3D conversion”, Proceedings of SPIE Conference on Stereoscopic Displays and Virtual Reality Systems, vol. 4660, pp. 78-86, Jan. 21-23, 2002. |
Hoff et al., “Analysis of head pose accuracy in augmented reality”, IEEE Transactions on Visualization and Computer Graphics, vol. 6, No. 4, pp. 319-334, Oct.-Dec. 2000. |
Poupyrev et al., “The go-go interaction technique: non-liner mapping for direct manipulation in VR”, Proceedings of the 9th annual ACM Symposium on User interface software and technology (UIST '96), Washington, USA, pp. 79-80, Nov. 6-8, 1996. |
Wexelblat et al., “Virtual Reality Applications and Explorations”, Academic Press Inc., San Diego, USA, 262 pages, year 1993. |
U.S. Appl. No. 13/161,508 Office Action dated Apr. 10, 2013. |
U.S. Appl. No. 12/683,452 Office Action dated Jun. 7, 2013. |
Miller, R., “Kinect for XBox 360 Review”, Engadget, Nov. 4, 2010. |
U.S. Appl. No. 13/161,508 Office Action dated Sep. 9, 2013. |
International Application PCT/IB2013/052332 Search Report dated Aug. 26, 2013. |
U.S. Appl. No. 13/314,210 Office Action dated Jul. 19, 2013. |
U.S. Appl. No. 13/314,207 Office Action dated Aug. 5, 2013. |
Sun et al., “SRP Based Natural Interaction Between Real and Virtual Worlds in Augmented Reality”, Proceedings of the International Conference on Cyberworlds (CW'08), pp. 117-124, Sep. 22-24, 2008. |
Schmalstieg et al., “The Studierstube Augmented Reality Project”, Presence: Teleoperators and Virtual Environments, vol. 11, No. 1, pp. 33-54, Feb. 2002. |
Ohta et al., “Share-Z: Client/Server Depth Sensing for See-Through Head-Mounted Displays”, Presence: Teleoperators and Virtual Environments, vol. 11, No. 2, pp. 176-188, Apr. 2002. |
Gobbetti et al., “VB2: An Architecture for Interaction in Synthetic Worlds”, Proceedings of the 6th Annual ACM Symposium on User Interface Software and Technology (UIST'93), pp. 167-178, Nov. 3-5, 1993. |
Gargallo et al., “Bayesian 3D Modeling from Images Using Multiple Depth Maps”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 2, pp. 885-891, Jun. 20-25, 2005. |
ZRRO LTD., “TeleTouch Device”, year 2011 (http://www.zrro.com/products.html). |
Berliner et al., U.S. Appl. No. 61/732,354, filed Dec. 12, 2012. |
Shpunt et al., U.S. Appl. No. 61/764,554, filed Feb. 14, 2013. |
U.S. Appl. No. 13/244,490 Office Action dated Dec. 6, 2013. |
U.S. Appl. No. 13/423,314 Office Action dated Dec. 4, 2013. |
U.S. Appl. No. 13/423,322 Office Action dated Nov. 1, 2013. |
International Application # PCT/IB2013/061269 Search Report dated Apr. 3, 2014. |
JP Application # 2013-520267 Office Action dated Mar. 16, 2015. |
U.S. Appl. No. 13/726,129 Ex Parte Quayle Action dated Jun. 26, 2015. |
Number | Date | Country | |
---|---|---|---|
20130107021 A1 | May 2013 | US |
Number | Date | Country | |
---|---|---|---|
61365788 | Jul 2010 | US |
Number | Date | Country | |
---|---|---|---|
Parent | PCT/IB2011/053192 | Jul 2011 | US |
Child | 13726128 | US |