Amazon Technologies, Inc.

Rawles LLC

Motiv Power Systems, Inc.

Samuel Henry Chang

Ning Yao

Edward Dietz Crump

Qiang Liu

Sowmya Gopalan

Renwei Yan

Weijian Wang

Wai Chung Chu

Manika Puri

Yuhua Zhu

Christopher D Coley

Steven Diamond

Jessica Riley

Olusanya Temitope Soyannwo

Vishal Parikh

Jim Michael Castelaz

Steve G Gonzalez

Petr Shepelev

William Spencer Worley, Iii

Eric Gifford Marason

Rahul Agrawal

Prasanna Venkatesh Krishnasamy

Lynnda Gray

A system efficiently selects at least one device from multiple devices based on received audio signals. In some instances, the system receives audio signals from devices that each comprise at least one microphone. A respective audio signal of the audio signals includes a representation of a sound originating from a location. The system then determines a device to be used to respond to the sound. In some instances, the system analyzes times in which the received audio signals that represent the sound are generated and/or volumes of the sound as represented by the received audio signals. The system can then select the device based on the analysis.

Analyzing audio signals for device selection

A two-dimensional (2D) image and a three-dimensional (3D) of an environment may be captured. Upon identifying a location and/or contour of an object from the 3D image, the object from the 3D image may be mapped onto the 2D image. The object, including its location and contour, may be identified from the 2D image. Based at least partly on a comparison between the object from the 3D image and the object from the 2D image, a disparity may be calculated. The location and contour of the object may be determined when it is determined that the disparity is less than or equal to a predetermined threshold. Otherwise, the object from the 3D image may be remapped onto the 2D image.

Object detection and identification

Detection of human hands within an environment is used for gesture recognition for input to a computing system. Detecting a hand is non-trivial, however, because the appearances of a hand vary widely with many independent digits and moving parts. A feature-based similarity measurement is used to detect two specific hand positions&#x2014;an open hand and a closed hand.

Open and closed hand detection

Within a particular environment, a projection that may include light or one or more patterns may be directed at one or more mirrors. The projection may be reflected by a first one of the mirrors onto the surroundings of the environment, which may include a floor, one or more walls, and/or objects in the environment having a surface. A second mirror may represent an entire view of the surroundings of the environment. As a result, by taking an image directed at the second mirror, a single image may be captured that represents a view of the entire environment without having to rotate the device that captures the image. The single image may then be utilized to generate a three-dimensional model of the environment.

Three-dimensional model generation

Techniques are described for performing automatic focusing of a projected image on a mobile projection surface. Specific regions of the projected image are identified that are likely to be desired to stay in optimal focus, and attributes of those specific regions, such as sharpness and contrast, can be used to determine the need to refocus the image. Advanced knowledge of the image data being projected can be utilized to determine the specific regions of the projected image that require monitoring for optimal focusing.

Automatic projection focusing

A hand gesture may be characterized mathematically as a set of motion parameters applied to a dynamic motion model. Training may be conducted to compile a library of motion parameter sets corresponding to various gestures. Motion parameters corresponding to observed gestures may than be compared to the library of motion parameter sets to classify the observed gestures.

Gesture recognition

A virtual user interface in a 3D environment is projected onto a display object, such as an open hand. An operating object, such as a stylus or a second hand with an index finger extended, may be used to select input features of the projected user interface. In one scenario, for example, a telephone keypad may be projected onto the open hand and the index finger may be used to select a particular number on the keypad. Images of the hands may be segmented to produce contours of the finger and open hand, and these contours are used to determine the location of the finger relative to the open hand.

Obtaining input from a virtual user interface

Systems and techniques for detecting gestures of a user based on analysis of acquired depth maps as well as identified distortion within one or more acquired color images. For instance, a system may project content onto a display surface within an environment. A user may then interact with the content via gestures, which may include hand gestures, gestures with a stylus or the like. In some instances, the content includes multiple selection portions, such as soft buttons, icons, and the like, that a user may select by pointing or touching with a finger or other type of selection tool. For instance, the projected content may include one or more projected icons and the user may select one of the icons by touching the display surface onto which the respective display icon is being projected.

Gesture recognition from depth and distortion analysis

A sound source locator efficiently employs a distributed physical or logical microphone array to determine a location of a source of a sound. In some instances, the sound source locator is deployed in an augmented reality environment. The sound source locator detects sound at a plurality of microphones, generates a signal corresponding to the sound, and causes attributes of signal as generated at the plurality of microphones to be stored in association with the corresponding microphone. The sound source locator uses these stored attributes to identify multiple groups of the plurality of microphones from which delays between the times the signal is generated can be used to compute the location of the source of the sound.

Sound source locator with distributed microphone array

Techniques are described for determining the pose of a surface based on a point set representing the surface. Corner models are matched to a contour of the point set using a difference minimization technique to identify corner poses of the surface. Based on the corner poses, a virtual model of the surface is created and matched to the point set to determine the pose of the surface.

Edge-based pose detection

An object observable in various images of an environment is recognized as being the same object, even when partially occluded in some images. In one particular implementation, the object is a hand which may be used for gesture input to an augmented reality system. A flexible similarity measurement metric is computed to aid in determining whether hands captured through 2D images of the environment are the same or different, regardless of whether the hands are fully observed or partially occluded. The similarity measurement metric accommodates for translation, rotation, and scaling of the hand to enable recognition of a common hand through a sequence of images even if that hand is partially occluded in some images.

Occluded object recognition

A number of images of an environment may be obtained over time by a number of sensors. The data obtained by the sensors may be used to generate contours of objects in the environment. In some cases, hands with pointing fingers in the environment may be identified based on the contours of the objects in the environment. In particular, contours having a palm segment and a pointing finger segment may be identified as hands with pointing fingers. In an implementation, 1-dimensional curvature and distance information may be used to identify contours having a palm segment and a pointing finger segment.

Pointing finger detection

Techniques are described for identifying suitable surfaces within a room upon which visual content may be displayed. One or more images of the room are obtained and used to identify planar surfaces. The images may also be analyzed to determine the visual textures of the surfaces within the room. Suitable projection surfaces may then be selected from the identified planar surfaces, based on the visual textures of the planar surfaces.

Automatic identification of projection surfaces

A number of images of an environment may be obtained over time. In some images, a portion of an object included in the environment may be occluded by another object in the environment. In these images, features of the occluded portion of the object may be absent. The absent features may be estimated based on features generated from previous images including the object where the occluded portion of the object was not occluded in the previous images. In some cases, the absent features may be estimated based on data associated with a reference object that corresponds to the occluded object with the reference object being included in a catalog of reference objects.

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San Jose, CA, United States of America

Samuel Henry Chang