Google Inc.

Ronald Frank Wotzlaw

Thomas Binder

Aravind Krishnaswamy

Nils Kokemohr

Daniel Pettigrew

Sevket Derin Babacan

Jason Chang

Eunyoung Kim

Daniel Frederik Schwarz

Kinan Hakim

Image editing on a wearable device includes a system which obtains sensor data via the wearable device. The sensor data includes a representation of hand movement, head movement or voice command associated with a user. The system executes an application for editing an image based on the obtained sensor data. The system provides for display a list of image adjustment types associated with the application. The system selects an image adjustment type based on one or more of the hand movement, the head movement or the voice command. The system provides for display a prompt having options to adjust a property of the selected image adjustment type. The system selects one of the options included in the prompt. The system modifies an image based on the selected option. The system then provides the modified image for storage in a data structure of a memory unit in the wearable device.

Image editing with audio data

Image editing on a wearable device

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

A method, computer readable medium and an apparatus are presented for providing composition and position guidance for a camera. One method includes detecting an image received on a capturing device. The image is analyzed to identify objects and determine attributes of the objects. Suggestions are provided to adjust at least one of position or setting of the capturing device in accordance to rules, based on the analysis of the image. Adjustment of the capturing device based on the suggestions provided, are detected and used to receive an adjusted image. The adjusted image is captured by the capturing device.

Photo composition and position guidance in a camera or augmented reality system

Implementations relate to feature mask determination for images. In some implementations, a computer-implemented method to determine a feature mask for an image includes estimating one or more prior regions in the image that define a feature in the image. The method determines superpixels based on multiple pixels of the image similar in color. The method constructs a graph, each node of the graph corresponding to a superpixel, and determines a superpixel score for each superpixel based on a number of pixels of the superpixel. The method determines one or more segmented regions in the image based on applying a graph cut technique to the graph based at least on the superpixel scores, and determines the feature mask based on the segmented regions. The feature mask indicates a degree to which pixels of the image depict the feature. The method modifies the image based on the feature mask.

Feature mask determination for images

Implementations generally relate to providing image parameters in a social network system. In some implementations, a method includes receiving a plurality of reference images associated with a target user in a social network system. The method also includes determining one or more image parameter values based on social activity of the target user. The method also includes modifying one or more target images based on the one or more determined image parameter values.

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Mountain View, CA, United States of America

Ronald Frank Wotzlaw