Google Inc.

Microsoft Technology Licensing, LLC

Waymo LLC

Other

Ryan M Geiss

Mark James Finocchio

Oliver M Williams

Samuel William Hasinoff

Marc Stewart Levoy

Zsolt Mathe

Charles Claudius Marais

Craig C Peeper

Johnny Hsienchow Lee

Jamie Daniel Joseph Shotton

Tommer Leyvand

Adrian Wong

Robert Matthew Craig

Momin Mahmoud Al-Ghosien

Matt Bronder

Hayes Solos Raffle

Alex A Kipman

Gabriel Taubman

Max Benjamin Braun

Roman Lewkow

Thad Eugene Starner

Kathryn Stone Perez

Stephen J DiVerdi

Harvey Ho

Jonathan Tilton Barron

Kudo Tsunoda

Jamie Shotton

R Stephen Polzin

Andrew William Fitzgibbon

Darren Bennett

Lucian Ion

Matthew L Bronder

Changyin Zhou

Carl Warren Craddock

Zhinan Xu

Ajay Chandra Venkata Gummalla

Xiaoying He

Pieter Kapsenberg

Alex Aben-Athar Kipman

Mitchell Joseph Heinrich

Alexandru Octavian Balan

Johnny Chung Lee

Chorong Johnston

Darren Alexander Bennett

Michelle Chen

Richard E Moore

Tianfan Xue

Casey Ho

Alexander Schiffhauer

Oliver Michael Christian Williams

Joe Bertolami

Stephen Joseph Diverdi

Matheen Siddiqui

Samuel William Hasinhoff

Kelly Tsai

Ruiduo Yang

A system is provided that includes an image sensor coupled to a vehicle, and control circuitry configured to perform operations including receiving, from the image sensor, an input stream comprising high dynamic range (HDR) image data associated with an environment of the vehicle, and processing the input stream at the vehicle by applying a global tone mapping, followed by offline image processing that can include applying a local tone mapping to the globally tone mapped images of the same input stream.

Systems and methods for a tone mapper solution for an autonomous driving system

Apparatus and methods related to photography are provided. A computing device can receive an input image. An object detector of the computing device can determine an object region of interest of the input image that is associated with an object detected in the input image. A trained machine learning algorithm can determine an output photographic region of interest for the input image based on the object region of interest and the input image. The machine learning algorithm can be trained to identify an output photographic region of interest that is suitable for use by a photographic function for image generation. The computing device can generate an output related to the output photographic region of interest.

Determining regions of interest for photographic functions

Apparatus and methods related to applying lighting models to images of objects are provided. A neural network can be trained to apply a lighting model to an input image. The training of the neural network can utilize confidence learning that is based on light predictions and prediction confidence values associated with lighting of the input image. A computing device can receive an input image of an object and data about a particular lighting model to be applied to the input image. The computing device can determine an output image of the object by using the trained neural network to apply the particular lighting model to the input image of the object.

Dual exposure control in a camera system

Display screen or portion thereof with a graphical user interface

The present disclosure relates to methods and systems that may improve and/or modify images captured using multiscopic image capture systems. In an example embodiment, burst image data is captured via a multiscopic image capture system. The burst image data may include at least one image pair. The at least one image pair is aligned based on at least one rectifying homography function. The at least one aligned image pair is warped based on a stereo disparity between the respective images of the image pair. The warped and aligned images are then stacked and a denoising algorithm is applied. Optionally, a high dynamic range algorithm may be applied to at least one output image of the aligned, warped, and denoised images.

Systems and methods for multiscopic noise reduction and high-dynamic range

Methods and systems involving a graphic display in a head mounted display (HMD) are disclosed herein. An exemplary system may be configured to: (1) at a computing system associated with a head-mountable display, receive head-movement data indicative of head movement; (2) use one or more context signals to determine a first activity associated with the head-mountable device; (3) determine a head-movement interpretation scheme corresponding to the first activity; (4) apply the determined head-movement interpretation scheme to determine input data corresponding to the received head-movement data; and (5) provide the determined input data for at least one function of the head-mountable display.

Content display methods

A system recognizes human beings in their natural environment, without special sensing devices attached to the subjects, uniquely identifies them and tracks them in three dimensional space. The resulting representation is presented directly to applications as a multi-point skeletal model delivered in real-time. The device efficiently tracks humans and their natural movements by understanding the natural mechanics and capabilities of the human muscular-skeletal system. The device also uniquely recognizes individuals in order to allow multiple people to interact with the system via natural movements of their limbs and body as well as voice commands/responses.

Device for identifying and tracking multiple humans over time

Example embodiments may help multi-camera devices determine disparity information scene, and use the disparity information in an autofocus process. An example method involves: (a) receiving image data of a scene that comprises at least one image of the scene captured by each of two or more image-capture systems of a computing device that includes a plurality of image-capture systems; (b) using the image data captured by the two or more image-capture systems as a basis for determining disparity information for the scene; and (c) performing, by the computing system, an autofocus process based at least in part on the disparity information, wherein the autofocus process provides a focus setting for at least one of the image-capture systems of the computing device.

Depth-assisted focus in multi-camera systems

A method of tracking a target includes classifying a pixel having a pixel address with one or more pixel cases. The pixel is classified based on one or more observed or synthesized values. An example of an observed value for a pixel address includes an observed depth value obtained from a depth camera. Examples of synthesized values for a pixel address include a synthesized depth value calculated by rasterizing a model of the target; one or more body-part indices estimating a body part corresponding to that pixel address; and one or more player indices estimating a target corresponding to that pixel address. One or more force vectors are calculated for the pixel based on the pixel case, and the force vector is mapped to one or more force-receiving locations of the model representing the target to adjust the model representing the target into an adjusted pose.

Visual target tracking

A plurality of images of a scene may be obtained. These images may have been captured by an image sensor, and may include a first image and a second image. A particular gain may have been applied to the first image. An effective color temperature and a brightness of a first pixel in the first image may be determined, and a mapping between pixel characteristics and noise deviation of the image sensor may be selected. The pixel characteristics may include pixel brightness. The selected mapping may be used to map at least the brightness of the first pixel to a particular noise deviation. The brightness of the first pixel and the particular noise deviation may be compared to a brightness of a second pixel of the second image. The comparison may be used to determine whether to merge the first pixel and the second pixel.

Noise models for image processing

Growing community of inventors

San Jose, CA, United States of America

Ryan M Geiss