Google Inc.

Koninklijke Philips Corporation N.v.

Jingyu Cui

Shengyang Dai

Erik Murphy-Chutorian

David Foster Lieb

Timothy Priestley Novikoff

David Cohen

Aravind Krishnaswamy

Sharadh Ramaswamy

Shinko Yuanhsien Cheng

Matthew John Bridges

Eric Charles Henry

Ivan Neulander

Madhur Khandelwal

Flavio Lerda

Juan Carlos Anorga

Timothy Sepkoski St Clair

Evan Stephen Millar

Leslie Ikemoto

Shaun Tungseth

Austin Suszek

Jay Steele

Jorge Verdu

Marc Cannon

Alexander Roe

Koji Ashida

Qi Gu

Pardis Beikzadeh

Mugdha Kulkarni

Abraham Stephens

Raja Ratna Murthy Ayyagari

Songbo Jin

Ruirui Jiang

Jeremy Selier

Gang Song

Matthew Stephen Steiner

Daniel Pettigrew

Ce Liu

Michael Rubinstein

Teresa Ko

James A Gallagher

Jason Chang

Kedar Kanitkar

Michael Krainin

Loren Puchalla Fiore

Julia Winn

Aaron Joseph Maschinot

Nan Wang

Michele Mae Covell

Vivek Kwatra

Mei Han

Craig Steven Levin

Sven Prevrhal

Feng Yang

Pascal Massimino

Saurabh Mathur

Guillem Pratx

Venkatesan Esakki

Haoran Man

Cecilia Rabess

Timothy Novikoff

Implementations relate to causing a command to be executed based on an image. In some implementations, a computer-implemented method includes obtaining and programmatically analyzing an image to determine suggested actions. The method causes a user interface to be displayed that includes user interface elements corresponding to default actions, and to suggested actions that are determined based on analyzing the image. The method receives user input indicative of selection of a particular action from the default actions and the suggested actions. The method causes a command to be executed by a computing device for the particular action that was selected.

Suggested actions for images

Some implementations relate to determining whether glare is present in captured image(s) of an object (e.g., a photo) and/or to determining one or more attributes of any present glare. Some of those implementations further relate to adapting one or more parameters for a glare removal process based on whether the glare is determined to be present and/or based on one or more of the determined attributes of any glare determined to be present. Some additional and/or alternative implementations disclosed herein relate to correcting color of a flash image of an object (e.g., a photo). The flash image is based on one or more images captured by a camera of a client device with a flash component of the client device activated. In various implementations, correcting the color of the flash image is based on a determined color space of an ambient image of the object.

Adaptive glare removal and/or color correction

A method includes compressing an image using a quality setting, determining a quality of the compressed image based on a quality metric, and determining if the quality of the compressed image is within a quality range. In response to determining the quality of the compressed image is within the quality range, store the compressed image; and in response to determining the quality of the compressed image is not within the quality range, modify the quality setting, and repeat the compressing step with the modified quality setting, both determining steps, and the applicable in response to step.

Adaptive image compression using a quality metric

Implementations relate to generating theme-based videos. In some implementations, a computer-implemented method to automatically generate a theme-based video includes obtaining image criteria for a theme from a theme definition, querying an image database to obtain a plurality of images that meet the image criteria, determining that a count of the plurality of images satisfies a threshold based on the theme definition, and generating the theme-based video that includes one or more of the plurality of images.

Generating theme-based videos

Implementations relate to sharing images and image albums over a communication network. In some implementations, a computer-implemented method includes determining that a shared album data structure is accessible by a device and includes references to album images. The device determines one or more suggested images from a collection of stored images associated with a user, based on determining sharing scores for the stored images. The sharing scores are based on comparing one or more characteristics of the stored images to one or more corresponding characteristics of the album images. The method includes causing display of the one or more suggested images by the device, receiving a selection of at least one selected image of the suggested images based on received user input, and causing an update of the shared album data structure with the at least one selected image to be accessible over a communication network by recipient users.

Sharing images and image albums over a communication network

Implementations relate to removal of one or more images from a view of a plurality of images. In some implementations, a method includes obtaining a plurality of images, programmatically analyzing the plurality of images to determine a plurality of image features, and determining one or more image categories for the plurality of images based on the image features. The method further includes identifying a subset of the plurality of images based on the image categories, wherein each image of the subset is associated with an image category for archival. The method further includes causing a user interface to be displayed that includes one or more images of the subset, receiving user input to archive at least one of the one or more images, and in response to the user input, removing the at least one of the images from a view of the plurality of images.

Image archival based on image categories

A method includes grouping media items associated with a user into segments based on a timestamp associated with each media item and a total number of media items. The method also includes selecting target media from the media items for each of the segments based on media attributes associated with the media item. The method also includes generating a video that includes the target media for each of the segments by generating a first animation that illustrates a first transition from a first item from the target media to a second item from the target media with movement of the first item from an onscreen location to an offscreen location, wherein the first item is adjacent to the second item in the first animation and determining whether the target media includes one or more additional items. The method also includes adding a song to the video.

Generating videos of media items associated with a user

Implementations generally relate to ranking and selecting images for display from a set of images. In some implementations, a computer-implemented method includes providing selected images for display, including examining characteristics of a plurality of images, where the examined characteristics include two or more of: visual capture characteristics, visual content characteristics, and social popularity characteristics of the images. The method determines individual scores for the respective examined characteristics of the images, determines overall scores of the images based on a combination of the individual scores for the examined characteristics of the images, and determines a ranking of the images based on the overall scores. The method selects one or more images based on the ranking of the images, and causes a display of the one or more selected images.

Ranking and selecting images for display from a set of images

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Milpitas, CA, United States of America

Jingyu Cui