Method and system for determining user input based on gesture

• US 10,228,242 B2
• Filed: 05/05/2015
• Issued: 03/12/2019
• Est. Priority Date: 07/12/2013
• Status: Active Grant

• Current Assignee: Magic Leap, Inc.
• Original Assignee: Magic Leap, Inc.
• Inventors: Abovitz, Rony, Schowengerdt, Brian T., Watson, Mathew D.
• Primary Examiner(s): Wang, Jin Cheng
• Application Number:
  US14/704,858
• Publication Number:
  US 20150234477A1
• Time in Patent Office: 1,407 Days
• Field of Search: 345633, 382103, 382190, 715852, 707609, 707722
• CPC Class Codes: A61B 2034/101 Computer-aided simulation of surgical operations
• CPC Class Codes: A61B 2562/0204 Acoustic sensors
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 2562/0247 Pressure sensors
• CPC Class Codes: A61B 3/0008 provided with illuminating means A61B3/117 takes precedence; goniolenses used for laser treatment A61F9/009; illuminating means for optical instruments G02B27/00
• CPC Class Codes: A61B 3/022 for testing contrast sensitivity
• CPC Class Codes: A61B 3/024 for determining the visual field, e.g. perimeter types
• CPC Class Codes: A61B 3/028 for testing visual acuity; for determination of refraction, e.g. phoropters
• CPC Class Codes: A61B 3/063 for testing light sensitivity, i.e. adaptation
• CPC Class Codes: A61B 3/066 for testing colour vision
• CPC Class Codes: A61B 3/08 for testing binocular or stereoscopic vision, e.g. strabismus
• CPC Class Codes: A61B 3/085 for testing strabismus
• CPC Class Codes: A61B 3/10 Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
• CPC Class Codes: A61B 3/1015 for wavefront analysis
• CPC Class Codes: A61B 3/102 for optical coherence tomography [OCT]
• CPC Class Codes: A61B 3/1035 for measuring astigmatism testing astigmatism A61B3/036
• CPC Class Codes: A61B 3/113 for determining or recording eye movement
• CPC Class Codes: A61B 3/12 for looking at the eye fundus, e.g. ophthalmoscopes A61B3/13 takes precedence
• CPC Class Codes: A61B 3/1216 for diagnostics of the iris
• CPC Class Codes: A61B 3/13 Ophthalmic microscopes
• CPC Class Codes: A61B 3/14 : Arrangements specially adapted for eye photography apparatus or arrangements for taking photographs per se G03B
• CPC Class Codes: A61B 3/165 : Non-contacting tonometers
• CPC Class Codes: A61B 34/10 : Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: A61B 5/0059 : using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/0066 : Optical coherence imaging
• CPC Class Codes: A61B 5/0077 : Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/01 : Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/14532 : for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1455 : using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/14555 : specially adapted for the eye fundus
• CPC Class Codes: A61B 5/369 : Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/398 : Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
• CPC Class Codes: A61B 5/6803 : Head-worn items, e.g. helmets, masks, headphones or goggles
• CPC Class Codes: A63F 13/00 : Video games, i.e. games using an electronically generated display having two or more dimensions gaming systems which provide a financial reward G07F17/32
• CPC Class Codes: A63F 13/213 : comprising photodetecting means, e.g. cameras, photodiodes or infrared cells A63F13/219, A63F13/655 take precedence
• CPC Class Codes: A63F 13/428 : involving motion or position input signals, e.g. signals representing the rotation of an input controller or a player's arm motions sensed by accelerometers or gyroscopes
• CPC Class Codes: G01B 11/303 : using photoelectric detection means
• CPC Class Codes: G02B 2027/0105 : Holograms with particular structures
• CPC Class Codes: G02B 2027/0127 : comprising devices increasing the depth of field
• CPC Class Codes: G02B 2027/0138 : comprising image capture systems, e.g. camera
• CPC Class Codes: G02B 2027/014 : comprising information/image processing systems
• CPC Class Codes: G02B 2027/0178 : Eyeglass type eyeglass details G02C
• CPC Class Codes: G02B 2027/0185 : Displaying image at variable distance
• CPC Class Codes: G02B 2027/0187 : slaved to motion of at least a part of the body of the user, e.g. head, eye
• CPC Class Codes: G02B 27/0101 : characterised by optical features G02B27/0172 takes precedence
• CPC Class Codes: G02B 27/017 : Head mounted
• CPC Class Codes: G02B 27/0172 : characterised by optical features
• CPC Class Codes: G02B 27/0179 : Display position adjusting means not related to the information to be displayed
• CPC Class Codes: G02B 27/42 : Diffraction optics , i.e. systems including a diffractive element being designed for providing a diffractive effectG02B27/60 takes precedence
• CPC Class Codes: G02B 27/4205 : having a diffractive optical element [DOE] contributing to image formation, e.g. whereby modulation transfer function MTF or optical aberrations are relevant
• CPC Class Codes: G02B 27/4227 : in image scanning systems
• CPC Class Codes: G02B 6/10 : of the optical waveguide type G02B6/02, G02B6/24 take precedence; devices or arrangements for the control of light by electric, magnetic, electro-magnetic or acoustic means G02F1/00; transferring the modulation of modulated light G02F2/00; optical logic elements G02F3/00; optical analogue/digital converters G02F7/00
• CPC Class Codes: G02B 6/34 : utilising prism or grating G02B6/293 takes precedence
• CPC Class Codes: G06F 16/7837 : using objects detected or recognised in the video content
• CPC Class Codes: G06F 18/22 : Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 3/005 : Input arrangements through a video camera
• CPC Class Codes: G06F 3/011 : Arrangements for interaction with the human body, e.g. for user immersion in virtual reality blind teaching G09B21/00
• CPC Class Codes: G06F 3/013 : Eye tracking input arrangements G06F3/015 takes precedence
• CPC Class Codes: G06F 3/017 : Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06F 3/0304 : Detection arrangements using opto-electronic means constructional details of pointing devices not related to the detection arrangement using opto-electronic means G06F3/033; optical digitisers G06F3/042
• CPC Class Codes: G06F 3/04815 : Interaction with a metaphor-based environment or interaction object displayed as three-dimensional, e.g. changing the user viewpoint with respect to the environment or object
• CPC Class Codes: G06F 3/04842 : Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/04845 : for image manipulation, e.g. dragging, rotation, expansion or change of colour
• CPC Class Codes: G06F 3/0485 : Scrolling or panning
• CPC Class Codes: G06F 3/0487 : using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser
• CPC Class Codes: G06F 3/04883 : for inputting data by handwriting, e.g. gesture or text
• CPC Class Codes: G06Q 30/0643 : Graphical representation of items or shoppers
• CPC Class Codes: G06T 19/006 : Mixed reality object pose determination, tracking or camera calibration for mixed reality G06T7/00
• CPC Class Codes: G06T 2200/04 : involving 3D image data
• CPC Class Codes: G06T 2200/24 : involving graphical user interfaces [GUIs]
• CPC Class Codes: G06T 2207/10004 : Still image; Photographic image
• CPC Class Codes: G06T 2207/10024 : Color image
• CPC Class Codes: G06T 2207/10148 : Varying focus
• CPC Class Codes: G06T 2207/10152 : Varying illumination
• CPC Class Codes: G06T 2207/30041 : Eye; Retina; Ophthalmic
• CPC Class Codes: G06T 2207/30196 : Human being; Person
• CPC Class Codes: G06T 2210/41 : Medical
• CPC Class Codes: G06T 2219/024 : Multi-user, collaborative environment
• CPC Class Codes: G06T 7/60 : Analysis of geometric attributes
• CPC Class Codes: G06V 10/20 : Image preprocessing
• CPC Class Codes: G06V 10/40 : Extraction of image or video features
• CPC Class Codes: G06V 10/758 : Involving statistics of pixels or of feature values, e.g. histogram matching
• CPC Class Codes: G06V 20/10 : Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59
• CPC Class Codes: G06V 20/20 : in augmented reality scenes
• CPC Class Codes: G06V 20/40 : in video content extracting overlay text G06V20/62; video retrieval G06F16/70; processing of video elementary streams in video servers H04N21/234; processing of video elementary streams in video clients H04N21/44
• CPC Class Codes: G06V 20/653 : by matching three-dimensional models, e.g. conformal mapping of Riemann surfaces
• CPC Class Codes: G06V 40/113 : Recognition of static hand signs
• CPC Class Codes: G06V 40/28 : Recognition of hand or arm movements, e.g. recognition of deaf sign language static hand signs G06V40/113
• CPC Class Codes: G16H 40/00 : ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
• CPC Class Codes: G16H 40/20 : for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: H04B 10/25891 : Transmission components H04B10/40 takes precedence

Abstract

A waveguide apparatus includes a planar waveguide and at least one optical diffraction element (DOE) that provides a plurality of optical paths between an exterior and interior of the planar waveguide. A phase profile of the DOE may combine a linear diffraction grating with a circular lens, to shape a wave front and produce beams with desired focus. Waveguide apparati may be assembled to create multiple focal planes. The DOE may have a low diffraction efficiency, and planar waveguides may be transparent when viewed normally, allowing passage of light from an ambient environment (e.g., real world) useful in AR systems. Light may be returned for temporally sequentially passes through the planar waveguide. The DOE(s) may be fixed or may have dynamically adjustable characteristics. An optical coupler system may couple images to the waveguide apparatus from a projector, for instance a biaxially scanning cantilevered optical fiber tip.

Claims

1. A method for determining a user input, comprising:

capturing, at one or more image capturing sensors, an image of a field of view of a user, the image comprising a gesture created by the user;

determining a sequence for a plurality of gesture analysis processes based in part or in whole upon computational resource utilization of the plurality of gesture analysis processes;

analyzing, at least by a microprocessor, the image to determine a set of candidates and to identify a set of points associated with the gesture;

removing at least one candidate from gesture recognition with at least a first gesture analysis process of the plurality of gesture analysis processes to reduce the set of candidates to a remaining set of one or more remaining candidates while skipping one or more remaining gesture analysis processes of the plurality of gesture analysis processes for the at least one candidate;

generating respective scoring values for the one or more remaining candidates based in part or in whole on matching results of the one or more remaining candidates with predetermined gestures in a database; and

determining a user input based at least in part on a recognized gesture that is recognized by at least a second gesture analysis process.

2. The method of claim 1, further comprising:

accessing a networked memory to access the database of predetermined gestures;

recognizing the gesture when a scoring value exceeds a threshold value; and

comparing the set of points to at least one predetermined set of points associated with a database of predetermined gestures.

3. The method of claim 1, wherein the gesture comprises at least one of inter-finger interactions, pointing, tapping, and rubbing.

4. The method of claim 1, further comprising:

determining an action based on the user input; and

performing the action at the computing system comprising the microprocessor.

5. A system for determining a user input, comprising:

one or more image capturing sensors configured to capture an image of a field of view of a user, the image comprising a gesture created by the user;

the at least one microprocessor further configured to determine a sequence for a plurality of gesture analysis processes based in part or in whole upon computational resource utilization of the plurality of gesture analysis processes;

at least one microprocessor further configured to analyze the image with at least one of the plurality of gesture analysis processes according to the sequence to determine a set of candidates and to identify a set of points associated with the gesture;

the at least one microprocessor further configured to remove at least one candidate from gesture recognition to reduce the set of candidates to a remaining set of one or more remaining candidates while skipping one or more remaining gesture analysis processes of the plurality of gesture analysis processes for the at least one candidate;

the at least one microprocessor further configured to generate respective scoring values for the one or more remaining candidates based in part or in whole on matching results of the one or more remaining candidates with predetermined gestures in a database; and

the at least one microprocessor further configured to determine a user input based at least in part on a recognized gesture of the one or more remaining gestures.

6. A computer program product comprising a non-transitory computer-usable storage medium storing thereupon executable code which, when executed by at least one microprocessor, causes the at least one microprocessor to perform a set of acts for determining a user input, the set of acts comprising:

capturing, at one or more image capturing sensors, an image of a field of view of a user, the image comprising a gesture created by the user;

determining a sequence for a plurality of gesture analysis processes based in part or in whole upon computational resource utilization of the plurality of gesture analysis processes;

analyzing, at least by a microprocessor, the image with at least one of the plurality of gesture analysis processes according to the sequence to determine a set of candidates and to identify a set of points associated with the gesture;

removing at least one candidate from gesture recognition to reduce the set of candidates to a remaining set of one or more remaining candidates while skipping one or more remaining gesture analysis processes of the plurality of gesture analysis processes for the at least one candidate;

generating respective scoring values for the one or more remaining candidates based in part or in whole on matching results of the one or more remaining candidates with predetermined gestures in a database; and

determining a user input based at least in part on a recognized gesture of the one or more remaining gestures.

7. A method of identifying a gesture, comprising:

capturing, at one or more image capturing sensors, a plurality of images of respective fields of view of a user;

determining a predetermined processing order for a plurality of gesture analysis processes based in part or in whole upon computational resource utilization of the plurality of gesture analysis processes;

analyzing, with at least one microprocessor, the plurality of images with at least one of the plurality of gesture analysis processes according to the sequence at least by performing a rejection cascade processing on a set of candidates to remove at least one candidate from a set of candidates for the plurality of images to generate a reduced set of one or more remaining candidates while skipping one or more gesture analysis processes based in part or in whole upon the predetermined processing order, the rejection cascade processing comprising;

a relatively less computational intensive stage using relatively less expensive computations and configured to remove one or more candidates to transform the set of candidates into a reduced set of candidates; and

a later, more computational intensive stage using relatively more expensive computations and configured to analyze the reduced set of candidates to determine one or more gestures from the plurality of images; and

identifying at least one gesture by performing at least a second gesture analysis process of the plurality of gesture analysis processes on the plurality of images.

8. A method of identifying a gesture, comprising:

capturing, at one or more image capturing sensors, a plurality of images of respective fields of view of a user;

generating a plurality of gesture candidates from the plurality of images at least by performing a depth segmentation analysis based in part or in whole upon depth data provided by the one or more one or more image capturing sensors;

determining a sequence for a plurality of gesture analysis processes based in part or in whole upon computational resource utilization of the plurality of gesture analysis processes;

generating analysis data values corresponding to each of the plurality of gesture candidates;

sorting the plurality of gesture candidates based on the analysis data values;

eliminating, with at least a first gesture analysis process, one or more gesture candidates with analysis data values less than a threshold to generate a reduced set of gesture candidates while skipping one or more remaining gesture analysis processes of the plurality of gesture analysis processes; and

identifying at least one gesture candidate from the reduced set of gesture candidates as the gesture for interaction with at least a second gesture analysis process executing on a computing system.

9. A method for classifying a gesture, comprising:

capturing, at one or more image capturing sensors, an image of a field of view of a user;

determining a sequence for a plurality of gesture analysis processes based in part or in whole upon computational resource utilization of the plurality of gesture analysis processes;

reducing a set of gesture candidates into a reduced set of gesture candidates at least by removing one or more gesture candidates with at least a first gesture analysis process of the plurality of gesture analysis processes while skipping one or more remaining gesture analysis processes of the plurality of gesture analysis processes for the image;

performing, at least by a microprocessor operatively coupled to the one or more image capturing sensors, depth segmentation on the image at least by performing a line search with a series of lines on data in the image to generate a depth map;

analyzing the depth map using a classifier mechanism to identify a part of a hand corresponding to a point in the depth map;

skeletonizing the depth map into a skeletonized depth map based at least in part on an identification of the part of the hand;

classifying the image as a gesture in the reduced set of gesture candidates with at least a second gesture analysis process of the plurality of gesture analysis processes based in part or in whole on the skeletonized depth map.

10. The method of claim 9, wherein the depth segmentation comprises the line search with one or more diagonal lines employed in a portion of the image.

11. The method of claim 9, further comprising performing a cascade analysis on the depth map to classify the image as the gesture.

12. The method of claim 9, further comprising performing depth augmentation on the depth map.

13. The method of claim 9, further comprising performing surface normalization on the depth map.

14. The method of claim 9, further comprising performing orientation normalization on the depth map.

15. The method of claim 9, further comprising performing background subtraction on the depth map.

16. The method of claim 9, further comprising performing depth comparison on the depth map.

17. The method of claim 9, further comprising classifying the image as the gesture based on the depth map, which has been skeletonized, and prior information.

18. The method of claim 9, wherein the classifier mechanism comprises a decision forest or a decision tree.

19. The method of claim 9, wherein the line search is performed with a plurality of flat lines in a first portion in the image and a plurality of diagonal lines in a second portion in the image.

20. The method of claim 9, further comprising:

checking an amount of light reflected off a part of the user in the image;

performing confidence enhancement for the depth map, which has been skeletonized, based at least in part or in whole upon a clear map of the part of the user and upon the amount of light; and

filtering out one or more identified objects as the part of the user at least by flood filling data from cascade processing.