Apple Inc.

Pedro Manuel Da Silva Quelhas

Raffi A Bedikian

Eshan Verma

Moinul H Khan

Anselm Grundhoefer

Mohamed Selim Ben Himane

Daniele Casaburo

Omar Elafifi

Stefan Auer

Katharina Buckl

Jim Chen Chou

Tobias Eble

Adrian Peter Lindberg

Amritpal Singh Saini

Phillip N Smith

Julien Monat Rodier

In one implementation, a method of remedying a medical impairment of a user is performed by a device including a processor, non-transitory memory, one or more biometric sensors, an image sensor, and a display. The method includes detecting, based on data from at least one of the image sensor and the one or more biometric sensors, a medical impairment of a user of the head-mounted device from a plurality of potential medical impairments associated with a plurality of remedies. The method includes selecting, from the plurality of remedies, a remedy of the medical impairment of the user. The method includes controlling the display to effect the remedy of the medical impairment of the user.

Method and device for health monitoring

In some implementations, representations of applications are identified, positioned, and configured in a computer generated reality (CGR) environment based on context. The location at which the representation of the application is positioned may be based on the context of the CGR environment. The context may be determined based on non-image data that is separate from image data of the physical environment being captured for the CGR environment. As examples, the non-image data may relate to the user, a user preferences, a user attribute, a user gesture, motion, activity, or interaction, semantics related to user input or an external source of information, the current time, date, or time period, information from another device involved in the CGR, etc.

Context-based application placement

Various implementations disclosed herein include a method performed at an electronic device including one or more processors, a non-transitory memory, an image sensor, and a display device. The method includes obtaining, via the image sensor, an input image that includes an object. The method includes obtaining depth information characterizing the object, wherein the depth information characterizes a first distance between the image sensor and a portion of the object. The method includes determining a distance warp map for the input image based on a function of the depth information and a first offset value characterizing an estimated distance between eyes of a user and the display device. The method includes setting an operational parameter for the electronic device based on the distance warp map and generating, by the electronic device set to the operational parameter, a warped image from the input image.

Warping an input image based on depth and offset information

A method includes obtaining an image via an image sensor, and identifying, within the image, a physical object represented by a portion of the image. The method includes determining, based on the image, a visual feature characterizing the physical object. The method includes warping, based on the visual feature satisfying a first feature criterion, the portion of the image according to a first warping function that is based on the first feature criterion and a distance between the electronic device and a reference point. The method includes warping, based on the visual feature satisfying a second feature criterion that is different from the first feature criterion, the portion of the image according to a second warping function that is based on the second feature criterion and the distance between the electronic device and the reference point.

Adaptive image warping based on object and distance information

In one implementation, a method includes obtaining, for a particular pixel of an image, a real pixel value and a virtual pixel value. The method includes obtaining, for the particular pixel of the image, a first alpha and a second alpha. The method includes generating, for the particular pixel of the image, a combined pixel value as a weighted average of the real pixel value and the virtual pixel value, the weighting being based on at least one of the first alpha and the second alpha.

Method and device for combining real and virtual images

In some implementations, a method includes: obtaining a logical representation of a fixed function node; generating, by concerted operation of the logical representation of the fixed function node and a machine learning node that interfaces with the logical representation of the fixed function node, a candidate result based on a set of image data frames; determining whether error criteria are satisfied based at least in part on a comparison between the candidate result and a predetermined result for the set of image data frames; and, in response to determining that the error criteria are satisfied, modifying at least one of: a first portion of operating parameters of the machine learning node associated with operations of the machine learning node; and a second portion of operating parameters of the machine learning node associated with interfacing operations between the machine learning node and the logical representation of the fixed function node.

End-to-end training of a machine learning node that interfaces with a fixed function node

A method includes obtaining an image via an image sensor. The method includes determining a first perceptual quality value that is associated with a first portion of the image. The method includes determining a first image perceptual quality warping function that is based on the first perceptual quality value and an image warping map. The first image perceptual quality warping function is characterized by a first warping granularity level that is a function of the first perceptual quality value. The method includes warping the first portion of the image according to the first image perceptual quality warping function.

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Sunnyvale, CA, United States of America

Pedro Manuel Da Silva Quelhas