Google Inc.

Other

Emily B Cooper

Calvin F Quate

Gary Lee Vondran, Jr

Paul E Debevec

Shahram Izadi

Adarsh Prakash Murthy Kowdle

Jonathan James Taylor

Sean Ryan Francesco Fanello

Rohit Kumar Pandey

Philip L Davidson

Christoph Rhemann

Graham Fyffe

Scott R Manalis

Danhang Tang

Mingsong Dou

Xueming Yu

Jessica Lynn Busch

Jason Angelo Dourgarian

Sergio Orts Escolano

Geoffrey Douglas Harvey

Peter Christopher Lincoln

Shamik Ganguly

Chad Talbott

Kaiwen Guo

Systems and methods are described for generating image content. The systems and methods may include, in response to receiving a request to cause a sensor of a computing device to identify image content associated with optical data captured by the sensor, detecting a first sensor data stream having a first image resolution, and detecting a second sensor data stream having a second image resolution. The systems and method may also include identifying, by processing circuitry of the computing device, at least one region of interest in the first sensor data stream, determining cropping coordinates that define a first plurality of pixels in the at least one region of interest in the first sensor data stream, and generating a cropped image representing the at least one region of interest.

Low power machine learning using real-time captured regions of interest

A lighting stage includes a plurality of lights that project alternating spherical color gradient illumination patterns onto an object or human performer at a predetermined frequency. The lighting stage also includes a plurality of cameras that capture images of an object or human performer corresponding to the alternating spherical color gradient illumination patterns. The lighting stage also includes a plurality of depth sensors that capture depth maps of the object or human performer at the predetermined frequency. The lighting stage also includes (or is associated with) one or more processors that implement a machine learning algorithm to produce a three-dimensional (3D) model of the object or human performer. The 3D model includes relighting parameters used to relight the 3D model under different lighting conditions.

Volumetric performance capture with relighting

An example method includes determining, by a controller of an image capture system, a plurality of sets of exposure parameter values for one or more exposure parameters. The plurality of sets of exposure parameter values are determined at an exposure determination rate. The method further includes capturing, by an image capture device of the image capture system, a plurality of images. Each image of the plurality of images is captured according to a set of exposure parameter values of the plurality of sets of exposure parameter values. The method also includes sending, by the controller of the image capture system to an image processing unit, a subset of the plurality of images. Each subset of images is sent at a sampling rate, and the sampling rate is less than the exposure determination rate.

Systems and methods for sampling images

An atomic force microscope (AFM) tipped with a single-wall conductive nanotube is operated to write bits onto a metal substrate by oxidizing the surface. The oxidized microregions project above an otherwise flat surface, and can therefore be detected&mdash;that is, the written bits can be read&mdash;using the same AFM arrangement.

High-density data storage using atomic force microscope

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San Francisco, CA, United States of America

Emily B Cooper