Google Inc.

Rodrigo Lima Carceroni

Marius Renn

Wei Hong

Pannag Raghunath Sanketi

Wei Hua

Abhijit S Ogale

Hrishikesh Balkrishna Aradhye

Amarnag Subramanya

Carole Dulong

Luc Vincent

Andrew Gerritt Huibers

Ruei-Sung Lin

Michael D Dodd

Alan Newberger

Jacob Mintz

Sascha Häberling

Derya Ozkan

Suril Shah

Brett Rolston Lider

Hui Fang

Huazhong Ning

Jeremy Bryant Pack

Soroosh Mariooryad

Peter Wilhelm Ludwig

Jennifer Ann Gillenwater

Seyed Mojtaba Seyedhosseini Tarzjani

Vicente Ignacio Ordonez Roman

Emre Sargin

Yifan Peng

In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for detecting motion in images. A computing system receives first and second images that were captured by a camera. The computing system generates, using the images, a mathematical transformation that indicates movement of the camera from the first image to the second image. The computing system generates, using the first image and the mathematical transformation, a modified version of the first image that presents the scene that was captured by the first image from a position of the camera when the second image was captured. The computing system determines a portion of the first image or second image at which a position of an object in the scene moved, by comparing the modified version of the first image to the second image.

Detecting motion in images

In general, techniques of this disclosure may enable a computing device to capture one or more images based on a natural language user input. The computing device, while operating in an image capture mode, receive an indication of a natural language user input associated with an image capture command. The computing device determines, based on the image capture command, a visual token to be included in one or more images to be captured by the camera. The computing device locates the visual token within an image preview output by the computing device while operating in the image capture mode. The computing device captures one or more images of the visual token.

Camera operable using natural language commands

In general, the subject matter can be embodied in methods, systems, and program products for identifying, by a computing system and using first and second frames of a video, a transformation that indicates movement of a camera with respect to the frames. The computing system generates a modified transformation so that the transformation is less representative of recent movement. The computing system uses the transformation and the modified transformation to generate a second transformation. The computing system identifies an anticipated distortion that would be present in a stabilized version of the second frame. The computing system determines an amount by which to reduce a stabilizing effect. The computing system applies the second transformation to the second frame to stabilize the second frame, where the stabilizing effect has been reduced based on the determined amount by which to reduce the stabilizing effect.

Stabilizing video

Stabilizing video using transformation matrices

In one example, a method includes determining, by a processor () of a wearable computing device () and based on motion data generated by a motion sensor () of the wearable computing device, one or more strokes. In this example, the method also includes generating, by the processor and based on the motion data, a respective attribute vector for each respective stroke from the one or more strokes and classifying, by the processor and based on the respective attribute vector, each respective stroke from the one or more strokes into at least one category. In this example, the method also includes determining, by the processor and based on a gesture library and the at least one category for each stroke from the one or more strokes, a gesture. In this example, the method also includes performing, by the wearable device and based on the gesture, an action.

Identifying gestures using motion data

Implementations of the disclosed technology include techniques for autonomously collecting image data, and generating photo summaries based thereon. In some implementations, a plurality of images may be autonomously sampled from an available stream of image data. For example, a camera application of a smartphone or other mobile computing device may present a live preview based on a stream of data from an image capture device. The live stream of image capture data may be sampled and the most interesting photos preserved for further filtering and presentation. The preserved photos may be further winnowed as a photo session continues and an image object generated summarizing the remaining photos. Accordingly, image capture data may be autonomously collected, filtered, and formatted to enable a photographer to see what moments they missed manually capturing during a photo session.

Systems and methods for autonomously generating photo summaries

An example method includes displaying, by a display () of a wearable device (), a content card (B); receiving, by the wearable device, motion data generated by a motion sensor () of the wearable device that represents motion of a forearm of a user of the wearable device; responsive to determining, based on the motion data, that the user has performed a movement that includes a supination of the forearm followed by a pronation of the forearm at an acceleration that is less than an acceleration of the supination, displaying, by the display, a next content card (C); and responsive to determining, based on the motion data, that the user has performed a movement that includes a supination of the forearm followed by a pronation of the forearm at an acceleration that is greater than an acceleration of the supination, displaying, by the display, a previous content card (A).

Touchless user interface navigation using gestures

A computer-implemented technique can receive a plurality of photos and automatically select a subset of the plurality of photos having a high degree of representativeness by jointly maximizing both photo quality and photo diversity to obtain a photo album. The technique can determine one or more clusters for the photo album using a hierarchical clustering algorithm, and store the photo album according to the one or more clusters. The technique can control the manner in which the photo album is displayed using the one or more clusters. The technique can adjust at least one of the one or more clusters and the automatic photo album generation based on user input. The user input can include at least one of adding, deleting, and moving a photo with respect to the one or more clusters. The technique can then re-cluster, automatically generate a new photo album, and/or adjust the presentation.

Techniques for user customization in a photo management system

A street-level imagery acquisition and selection process identifies which images are published in a street field view. An imagery database includes panoramas each corresponding to a set of images acquired from a single viewpoint. The panoramas are attached to corresponding positions on a road network graph. The graph is divided into a set of selection paths, each of which includes a topologically linear sequence of road segments. Each selection path is evaluated to select a set of panoramas to be published in the path. Panoramas of interior road segments are selected before panoramas at intersections. Selected panorama identifiers for each interior road segment of the selection paths and each intersection correspond to a position along the road network graph. The selected panorama identifiers are then published in the street field view.

Street-level imagery acquisition and selection

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Mountain View, CA, United States of America

Rodrigo Lima Carceroni