Apple Inc.

Samuel Noble

Rudolph Van Der Merwe

Christopher G Wilson

Gregory B Novick

Tyler D Hawkins

Conrad Sauerwald

Paul William Chinn

Jason Klivington

Neil Gareth Crane

Douglas P Mitchell

Arwen V Bradley

Ralph T Brunner

Bertrand Serlet

Richard E Crandall

Joseph M Triscari

Stanislav Kounitski

Duncan Robert Kerr

Yang Liu

Jaron Waldman

Jan Erik Solem

Xiaqing Wu

John Kassebaum

Nikhil P Singh

A method of establishing communications with a first device is disclosed. The method includes: the first device presenting connection information to a second device; receiving a response from a second device; establishing an association with the second device; transmitting, in response to a determination that the first device and the second device are connected for data, first data to the second device, the first data comprising addressing information for a server; receiving second data from the second device, the second data comprising second information for establishing communications with the first device; and configuring the first device to receive third data from a location remote to the first device using the second information from the second data.

Method of establishing communications

Techniques and devices for creating an AutoLoop output video include performing postgate operations. The AutoLoop output video is created from a set of frames. After generating the AutoLoop output video based on a plurality of loop parameters and at least a portion of the frames, postgate operations determine one or more dynamism metrics based on a variability metric and a dynamic range metric for a plurality of pixels within the video loop. Postgate operations compare the dynamism metrics to one or more postgate threshold values and reject the video loop based on the comparison of the dynamism metrics to the postgate threshold values.

Automated seamless video loop

Techniques and devices for creating an AutoLoop output video include performing pregate operations. The AutoLoop output video is created from a set of frames. Prior to creating the AutoLoop output video, the set of frames are automatically analyzed to identify one or more image features that are indicative of whether the image content in the set of frames is compatible with creating a video loop. Pregate operations assign one or more pregate scores for the set of frames based on the one or more identified image features, where the pregate scores indicate a compatibility to create the video loop based on the identified image features. Pregate operations automatically determine to create the video loop based on the pregate scores and generate an output video loop based on the loop parameters and at least a portion of the set of frames.

Techniques and devices for creating an AutoLoop output video include identifying optimal loops within short videos or within a series of image. The AutoLoop output video may be automatically created using casually shot, handheld videos, and may include an AutoLoop pipeline that may comprise obtaining an input video, stabilizing the input video, detecting optimal loop parameters and baking out the AutoLoop output video with crossfade and playing back the AutoLoop output video. Video stabilization can include a cascade of video stabilization algorithms including a tripod-direct mode and a tripod-sequential mode. After stabilization, an AutoLoop operation may determine optimal loop parameters. Once optimal loop parameters are determined, a crossfade may be added to smooth out any temporal and spatial discontinuities in the AutoLoop output video.

Techniques and devices for creating an AutoLoop output video by adding synthetic camera motion to the AutoLoop output video. The AutoLoop output video is created from a set of frames. After generating the AutoLoop output video based on a plurality of loop parameters and at least a portion of the frames, synthetic camera motion is combined with the AutoLoop output video. The synthetic camera loop is based on the subset of the input frames and exhibits some amount of camera motion for the subset of the input frames. Once the synthetic camera loop is generated, the synthetic camera loop and the video loop is combined to enhance the AutoLoop output video.

A method of activating a first device is disclosed. The method includes: the first device pairing with a second device; receiving a connection request from a second device; connecting to the second device; opening a communication channel to the second device; transmitting an activation package to the second device; receiving an activation payload from the second device; and performing an activation using information from the activation payload.

Device activation method and system

Methods and apparatus for a roof analysis tool for constructing a parameter set, where the parameter set is derived from mapping data for a map region, and where the parameter set describes the roofs for the buildings within the map region. In some cases, the parameter set includes a list of roof type identification values and the respective buildings in the map region for which a given roof type identification value corresponds. The roof analysis tool may operate on a server and work in conjunction with a mobile device, where the mobile device may display map views of a map region such that the map view is based on a three-dimensional model of the map region, and where a portion of the three-dimensional model is based on data generated on the mobile device and a portion of the three-dimensional model is based on data generated on the server.

Identifying and parameterizing roof types in map data

A barcode decoding system and method are disclosed that use a data-driven classifier for transforming a potentially degraded barcode signal into a digit sequence. The disclosed implementations are robust to signal degradation through incorporation of a noise model into the classifier construction phase. The run-time computational cost is low, allowing for efficient implementations on portable devices. Implementations are disclosed for intelligent preview scaling, barcode-aware autofocus augmentation and multi-scale signal feature extraction.

Barcode recognition using data-driven classifier

Embodiments of the present disclosure provide a method and system for sharing information between a first computing device and a second computing device. In the described embodiments, an optical label, such as, for example a QR code, is generated on the first computing device. In embodiments, the optical label is color encoded and displayed in such a way that the optical label is not perceptible to a user. The second computing device may capture the encoded optical label and subject the captured images to a processing technique that decodes the encoded optical label.

Invisible optical label for transmitting information between computing devices

Methods, systems, and apparatus, including computer program products, for identifying regions of interest in an image and identifying a barcode in a degraded image are provided. A region of interest is identified by pre-processing an image, generating a binary image based on a metric calculated on the pre-processed image, and analyzing regions of the image identified using connected components and other analysis. A barcode is identified by searching a population of barcodes, degrading ideal image intensity profiles of candidate barcodes, and comparing the degraded ideal image intensity profiles to an image intensity profile of the degraded image.

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Portland, OR, United States of America

Samuel Noble