Microsoft Technology Licensing, LLC

Microsoft Corporation

Andrew Shaun Ivory

Franc Camara

Richard S Turner, Jr

Mark R Enstrom

Reed K Bement

William David Sproule

Nitin Suri

Tzong-Jhy Wang

Po Yuan

Vlad Sadovsky

Bruce Justin Lindbloom

Jin Li

Vladimir Sadovsky

Xian-Sheng Hua

Scott Robert Ramsby

Omkar Anand Mehendale

Ashley E Averett

Ignatius Setiadi

Michael Han-Young Kim

The importance of video sections of a video file may be determined from features of the video file. The video file may be decoded to obtain video frames and audio data associated with the video frames. Feature scores for each video frame may be obtained by analyzing features of the video frame or the audio data associated with the video frame based on a local rule, a global rule, or both. The feature scores are further combined to derive a frame importance score for the video frame. Based on the feature scores of the video frames in the video file, the video file may be segmented into video sections of different section importance values.

Rule-based video importance analysis

Technologies for a single-pass process for enhancing video quality with temporal smoothing. The process may include providing for user overrides of automatically enhanced video/frame characteristics and providing substantially immediate previews of enhanced video frames to a user. The process may also include detecting a degree of shakiness in a portion of the video, and performing or recommending stabilization based on the detected shakiness.

Automatic video quality enhancement with temporal smoothing and user override

Techniques for identifying prominent subjects in video content based on feature point extraction are described herein. Video files may be processed to detect faces on video frames and extract feature points from the video frames. Some video frames may include detected faces and extracted feature points and other video frames may not include detected faces. Based on the extracted feature points, faces may be inferred on video frames where no face was detected. The inferring may be based on feature points. Additionally, video frames may be arranged into groups and two or more groups may be merged. The merging may be based on some groups including video frames having overlapping feature points. The resulting groups each may identify a subject. A frequency representing a number of video frames where the subject appears may be determined for calculating a prominence score for each of the identified subjects in the video file.

Computerized prominent character recognition in videos

This disclosure describes techniques for training models from video data and applying the learned models to identify desirable video data. Video data may be labeled to indicate a semantic category and/or a score indicative of desirability. The video data may be processed to extract low and high level features. A classifier and a scoring model may be trained based on the extracted features. The classifier may estimate a probability that the video data belongs to at least one of the categories in a set of semantic categories. The scoring model may determine a desirability score for the video data. New video data may be processed to extract low and high level features, and feature values may be determined based on the extracted features. The learned classifier and scoring model may be applied to the feature values to determine a desirability score associated with the new video data.

Computerized machine learning of interesting video sections

A method and system to extending naming of captured multimedia data to secondary applications is provided. The secondary applications are registered and associated with identifiers. The identifiers are stored in naming templates utilized by a file-naming engine. A multimedia-acquisition engine captures the multimedia data and communicates with the file-naming engine to generate a path that specifies a location to store the captured multimedia data. The file-naming engine processes the naming template to determine whether the secondary applications or default values are utilized to generate the path for the captured multimedia data.

Extensible file and path renaming during multimedia acquisition

Even clusters are created based n a first metadata and second metadata of the electronic document. The event clusters are associated with an event id and each electronic document is associated with the event identifier of it corresponding event cluster. A user may then browse or otherwise access the electronic documents based on the event identifier.

Heuristic event clustering of media using metadata

An image acquisition system has a computer and one or more imaging devices coupled thereto. Each imaging device has a device memory and is capable of capturing and storing a digital image in its memory. An image device manager is implemented in software on the computer to control imaging device operation. The image device manager presents a user interface (UI) within the familiar graphical windowing environment. The UI has a context space to a particular imaging context (e.g., scanning, photography, and video). The UI also has a persistently-visible imaging menu positioned within the context space that lists options particular to the imaging context. The image acquisition system also includes a set of application program interfaces (APIs) that enable applications to manage loading and unloading of imaging devices, monitor device events, query device information properties, create device objects, capture images using the devices, and store or manipulate the captured images.

Computer-implemented image acquistion system

In one aspect, an application analyzes and adjusts image data (e.g., pixel data) automatically upon acquiring (e.g., from a source such as a digital camera) a digital image. Adjustments can be based on, for example, image orientation, red-eye detection, blurriness, color balance, exposure, or noise detection. Metadata corresponding to image adjustments can be stored in an adjusted image file to preserve the original image. In another aspect, a computer system includes image analysis and image adjustment software modules for analyzing and adjusting digital image data at image acquisition time. The image adjustment module can include one or more processing filters. A customizable software architecture allows customization of the image adjustment software module (e.g., by adding, removing or reordering processing filters). The described techniques and tools can be implemented as features of an operating system environment.

Automatic analysis and adjustment of digital images upon acquisition

A method and image data acquisition service arrangement are disclosed that facilitate standardizing the process of adding metadata to acquired image information downloaded from a connected image capture device to a computer system. An image acquisition service analyzes image information downloaded from the image capture device and renders new metadata values based upon applied analytical algorithms/filters. Thereafter, the image information and new metadata are rendered available to other processes that use the image information and new metadata.

Generation of metadata for acquired images

Computer-implemented image acquisition system

An image acquisition system has a computer and one or more imaging devices coupled thereto. Each imaging device has a device memory and is capable of capturing and storing a digital image in its memory. An image device manager is implemented in software on the computer to control imaging device operation. The image device manager presents a user interface (GI) within the familiar graphical windowing environment. The GI has a context space to a particular imaging context (e.g., scanning, photography, and video). The GI also has a persistently-visible imaging menu positioned within the context space that lists options particular to the imaging context. The image acquisition system also includes a set of application program interfaces (APIs) that enable applications to manage loading and unloading of imaging devices, monitor device events, query device information properties, create device objects, capture images using the devices, and store or manipulate the captured images.

An image acquisition system has a computer and one or more imaging devices coupled thereto. Each imaging device has a device memory and is capable of capturing and storing a digital image in its memory. An image device manager is implemented in software on the computer to control imaging device operation. The image device manager presents a user interface (UI) within the familiar graphical windowing environment. The UI has a context space pertaining to a particular imaging context (e.g., scanning, photography, and video). The UI also has a persistently-visible imaging menu positioned within the context space that lists options particular to the imaging context. The image acquisition system also includes a set of application program interfaces (APIs) that enable applications to manage loading and unloading of imaging devices, monitor device events, query device information properties, create device objects, capture images using the devices, and store or manipulate the captured images.

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Woodinville, WA, United States of America

Andrew Shaun Ivory