Google Inc.

University of Washington

Emily Anne Fortuna

Aaron Michael Donsbach

Blaise Hilary Aguera-Arcas

Daniel Ramage

Krzysztof Duleba

Jon Gabriel Clapper

Juston Payne

Alison Lentz

Benjamin Vanik

Jess Holbrook

Joshua Denali Lovejoy

Oliver Fritz Lange

Iwona Bialynicka-Birula

Li Zhang

Hugh Brendan McMahan

Robert Douglas Fritz, Iii

Benjamin J McMahan

Divya Tyamagundlu

Kristine Kohlhepp

Luis Henrique Ceze

Susan Jane Eggers

Owen C Anderson

The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. In particular, the present disclosure provides low power frameworks for controlling image sensor mode in a mobile image capture device. On example low power frame work includes a scene analyzer that analyzes a scene depicted by a first image and, based at least in part on such analysis, causes an image sensor control signal to be provided to an image sensor to adjust at least one of the frame rate and the resolution of the image sensor.

Low power framework for controlling image sensor mode in a mobile image capture device

The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. The mobile image capture device is operable to input an image into at least one neural network and to receive at least one descriptor of the desirability of a scene depicted by the image as an output of the at least one neural network. The mobile image capture device is operable to determine, based at least in part on the at least one descriptor of the desirability of the scene of the image, whether to store a second copy of such image and/or one or more contemporaneously captured images in a non-volatile memory of the mobile image capture device or to discard a first copy of such image from a temporary image buffer without storing the second copy of such image in the non-volatile memory.

Systems and methods for selective retention and editing of images captured by mobile image capture device

The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. The mobile image capture device is operable to input an image into at least one neural network and to receive at least one descriptor of the desirability of a scene depicted by the image as an output of the at least one neural network. The mobile image capture device is operable to determine, based at least in part on the at least one descriptor of the desirability of the scene of the image, whether to store a second copy of such image in a non-volatile memory of the mobile image capture device or to discard a first copy of such image from a temporary image buffer without storing the second copy of such image in the non-volatile memory.

Systems and methods that leverage deep learning to selectively store images at a mobile image capture device

Devices and methods of providing hardware support for dynamic type checking are provided. In some embodiments, a processor includes a type check register and support for one or more checked load instructions. In some embodiments, normal load instructions are replaced by a compiler with the checked load instructions. In some embodiments, to perform a checked load, an error handler instruction location is stored in the type check register, and a type tag operand is compared to a type tag stored in the loaded memory location. If the comparison succeeds, execution may proceed normally. If the comparison fails, execution may be transferred to the error handler instruction. In some embodiments, type prediction is performed to determine whether a checked load instruction is likely to fail.

Systems and methods for hardware-assisted type checking

Growing community of inventors

Seattle, WA, United States of America

Emily Anne Fortuna