Microsoft Technology Licensing, LLC

Joshua Bryan Weisberg

Baoyuan Wang

Sing Bing Kang

David J Lee

Richard D Prologo

Wei-Chih Chen

Owen W Paulus

Joshua Debner

Mark Sievert Larsen

Noranart Vesdapunt

Stacia L C Scott

Gil Moshe Nahmias

Kandarpkumar J Makwana

Melissa Regalia

Utkarsh Sinha

Gil M Nahmias

A 'Best of Burst Selector,' or 'BoB Selector,' automatically selects a subjectively best image from a single set of images of a scene captured in a burst or continuous capture mode, captured as a video sequence, or captured as multiple images of the scene over any arbitrary period of time and any arbitrary timing between images. This set of images is referred to as a burst set. Selection of the subjectively best image is achieved in real-time by applying a machine-learned model to the burst set. The machine-learned model of the BoB Selector is trained to select one or more subjectively best images from the burst set in a way that closely emulates human selection based on subjective subtleties of human preferences. Images automatically selected by the BoB Selector are presented to a user or saved for further processing.

Automated selection of subjectively best image frames from burst captured image sequences

Techniques for automatically selecting image frames from a video and providing the selected image frames to a device for display are disclosed.

Deep learning on image frames to generate a summary

Media files may be curated for a user based on a location of a client device associated with the user. A user carrying a client device may arrive at a particular location on a given day. A computer-implemented process may include determining whether the given day and/or the particular location is 'unique' to the user in some fashion, which may be based on one or more criteria used to quantify or otherwise evaluate uniqueness of the day or location. At least partly in response, media file sharing service may be queried to identify shared media files that were created at the particular location, wherein the shared media files are associated with one or more entities having social connections to the user. A curated subset of the identified media files may be automatically created and a notification may be issued to the user.

Curating media from social connections

A 'Moment Capture System' automatically captures and buffers multiple image frames, both prior to and following digital camera shutter activation. This capture and buffering is performed in combination with real-time automatic control of camera settings (e.g., exposure time, capture rate, ISO, white balance, aperture, etc.) based on an ongoing real-time evaluation of contents and characteristics of most recent previously buffered frames. Whenever a shutter activation (e.g., a 'tap') is detected, the Moment Capture System pulls some number of pre-tap frames from the buffer and adds some number of post-tap frames to create an &#x201c;image moment.&#x201d; Image moments are defined as sets of sequential frames spanning a time period before and after the detected tap. In various implementations, the Moment Capture System performs automated selection of perceptually best images from the buffered frames associated with each tap. This automated selection closely emulates human selection based on subjective subtleties of human preferences.

Variable pre- and post-shot continuous frame buffering with automated image selection and enhancement

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Redmond, WA, United States of America

Joshua Bryan Weisberg