Google Inc.

Adobe, Inc.

Aravind Krishnaswamy

Stephen J DiVerdi

Sevket Derin Babacan

David Cohen

Nicholas Butko

Krzysztof Kulewski

Timothy Sepkoski St Clair

Sunil Sharadchandra Hadap

Nathan Aaron Carr

Shengyang Dai

Erik Murphy-Chutorian

Gavin S P Miller

Shinko Yuanhsien Cheng

Eric Charles Henry

Ivan Neulander

Jingyu Cui

Vincent Yanton Mo

Flavio Lerda

Jay Steele

Koji Ashida

Qi Gu

Jared M Johnson

Pardis Beikzadeh

Radomir Mech

Gregg Darryl Wilensky

Matthew Stephen Steiner

Jerry Glen Harris

Ronald Frank Wotzlaw

David Gary Besbris

Tinglin Liu

Stephen Joseph Diverdi

Justin Lewis

Gavin James

Nils Kokemohr

Daniel Pettigrew

Scott L Byer

Jason Chang

Peter F Falco, Jr

Grayson Squier Lang

Peter Joseph McNerney

Michael W Tao

Nikolai Svakhin

Michael O Clifton

Jose Ignacio Echevarria Vallespi

Walter Michael Shaw

Hao Xu

Daniel K Marcolina

Daniel Frederik Schwarz

Daniel Marconlina

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 providing regions of interest in an image. In some implementations, a method includes receiving an image. The method further includes determining one or more image features. The method further includes grouping the one or more image features into one or more regions of interest.

Providing regions of interest in an image

Implementations relate to editing options for image regions. Some implementations include detecting a region portion of an image based on one or more characteristics of pixels of the image. The detected region has an identified class selected from multiple identifiable classes, and each identifiable class is associated with a set of multiple editing options. Each editing option is associated with edit operation(s) operable to adjust value(s) of one or more image pixels. One of the sets of editing options is presented for selection, where the presented set is associated with the identified class of the detected region and each of the editing options in the presented set is operable to adjust value(s) of one or more pixels of the detected region. The presented set of editing options is different from at least one of the other sets of editing options associated with the other classes.

Editing options for image regions

Implementations can relate to providing computer-assisted text and visual styling for images. In some implementations, a computer-implemented method includes determining a set of characteristics of an image, and applying one or more first visual modifications to the image based on one or more of the set of characteristics of the image. The method can include receiving user input defining user text, providing the user text in the image, and applying one or more second visual modifications to the image based on the user text and based on at least one of the set of characteristics of the image.

Computer-assisted text and visual styling for images

Methods and systems for modifying an image by applying an effect to an image are described. The effects include a pop effect, a light adjustment, or a color adjustment to an image. The methods and systems include providing a user slider for applying an effect to the image. The methods and systems further include determining a first portion of the image including a face and creating a protection mask to protect the face in the first portion during image modification. The protection mask may include an enhancement threshold for modifying the first portion of the image. The modification of the image may include modifying the second portion of the image differently than the first portion of the image. A method for enforcing different resolutions of a same input image to produce similar visual results is also described.

User sliders for simplified adjustment of images

Implementations relate to prioritized data transmission features over networks. A method can include deriving description data from content data. The description data describes content of the content data and includes one or more portions corresponding to one or more portions of the content data. The description data is transmitted to a destination over a network to be made accessible over the network at the destination to one or more users. The content data also is transmitted to the destination to be made accessible. At least one portion of the description data finishes transmitting and is made accessible before its corresponding portion of the content information finishes transmitting and is made accessible.

Prioritized data transmission over networks

Implementations relate to providing visual effects for images. In some implementations, a method includes detecting one or more objects in an image. The method identifies one or more important objects of the objects, where the important objects are determined to have an importance measurement satisfying a predetermined threshold indicating their importance to a viewer of the image. The method determines an application of a visual image effect to the image based on the important objects.

Providing visual effects for images

Implementations relate to simulating long-exposure images. In some implementations, a method includes examining a series of images, determining an optical flow of pixel features between the image and an adjacent image in the series of images, and blurring one or more regions in one or more of the images, where the one or more regions are spatially defined based on one or more attributes of the optical flow.

Simulating long-exposure images

Implementations relate to visualizing and measuring impact of image modifications. In some implementations, a method to measure and indicate impact of image modification includes applying an edit operation to a first image, including modifying one or more pixels of the first image to provide a modified image. The method determines an impact score associated with the edit operation and indicative of a degree of visual impact of the edit operation to the first image. The method provides, based on the impact score, the modified image in a visualization of image modification for the first image, and provides the visualization for display by a display device.

Measuring and visualizing impact of image modifications

Implementations relate to adjusting images using a texture mask. In some implementations, a method includes detecting one or more texture regions having detected texture in an image, and generating a mask from the image based on the detected texture regions. The detected texture regions are distinguished in the mask from other regions of the image that do not have detected texture. The method applies one or more adjustment operations to the image in amounts based on values of the mask.

Image adjustment using texture mask

Methods and apparatus for reproducing the appearance of a photographic print on a display device are disclosed. An environment model is built from received light conditions at a light sensor attached to a display surface. The environment model and a surface model are applied to an input image to generate an output image. The surface model represents reflective characteristics of a simulated surface on which display of the input image is simulated. The output image simulates an effect of the received light conditions on the input image as simulated on the surface.

Methods and apparatus for reproducing the appearance of a photographic print on a display device

Aspects of the subject technology relate to automatically and selectively applying a fill light filter to an image. A process includes determining an edge-preserved, smoothed version of the image, and determining a grayscale version of the image. The process also includes comparing each pixel of the edge-preserved, smoothed version of the image to each corresponding pixel of the grayscale version of the image. The process also includes applying the fill light filter to the image based on the comparison. The fill light filter is automatically adjusted based on identifying regions in the image. Selectively applying the fill light filter can reduce artifacts and noise from forming or being amplified as a result of the fill light filter.

Automated selective application of fill light filter

Implementations generally relate to image editing. In some implementations, a method includes receiving an edited image, where the edited image includes an edit list and an image signature. The method further includes retrieving an original image based on the image signature. The method further includes applying the edit list to the original image to obtain a modified original image. The method further includes providing the modified original image to a user if the comparing of the edited image to the modified original image meets a similarity threshold.

Providing non-destructive editing reconciliation

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San Jose, CA, United States of America

Aravind Krishnaswamy