Microsoft Technology Licensing, LLC

Leland Stanford Junior University

Mitsubishi Electric Research Laboratories, Inc.

Neel Suresh Joshi

Brian Kevin Guenter

Michael F Cohen

Sing Bing Kang

Hugues Herve Hoppe

Zicheng Liao

Richard S Szeliski

Charles Lawrence Zitnick, Iii

Ashish Kapoor

Daniel Morris

Paul David Tischhauser

Matthieu Tony Uyttendaele

Joan Li

Montine Andrea Rummel

Johanna Hawkins

Jeannine Boone

Mark Healy

Michael A Toelle

Sudipta Narayan Sinha

T Scott Saponas

Wolf Kienzle

David John Los

Huixuan Tang

Siddharth Khullar

Oscar Beijbom

Mark A Horowitz

Geoffrey P McKnight

Steven Mark Drucker

Wojciech Matusik

Kalyan Krishna Sunkavalli

Andrew C Keefe

Steven Maxwell Seitz

Christopher J Buehler

Meredith June Morris

Shmuel Avidan

Yasuyuki Matsushita

Chandresh Kumar Jain

Baoyuan Wang

William Hart Holmes

Bennett Sampson Wilburn

Debadeepta Dey

Tae-Hyun Oh

Shital Shah

Tor-Helge Persett

Lamia Benmouffok

Kyle Rector

Yichang Shih

Wei-Sheng Lai

Sisil Sanjeev Mehta

Yunpeng Li

Yujia Huang

Priyam Chatterjee

Mike Roberts

Christine Ergonis

Marc C Levoy

Minh Phuoc Vo

Libin Sun

Daniel Scott Morris

Charles Lawrence Zitnick

Searching and locating an event on a calendar with a timeline is provided. An application such as a calendar application detects a search on the calendar or the timeline. One or more events are located on the calendar or on the timeline that match a search term of the search. The search term on the one or more events is highlighted. The one or more events with the search term that is highlighted are displayed on the calendar or the timeline.

Search and locate event on calendar with timeline

Techniques enable contextually-aware selections of event forums. Scheduling data can be analyzed to identify a context associated with an appointment. A forum that is appropriate for the identified context can be selected and utilized. The analysis can also utilize traffic data, map data, weather data, user preferences, and other contextual data to select one or more forums. A forum can be a place or communication medium. A service provider can associate one or more forums with a particular skill set. For example, a legal office can associate an online forum, such a videoconference, with a first category of services, and an on-site location for a second category of services. Depending on a category of services identified in one or more user preferences and/or meeting requests, a forum can be selected. Other factors, which may include weather conditions, traffic conditions, and commute times can also influence the selection of a forum.

Contextually-aware selection of event forums

Locating an event on a timeline is provided. A calendar application may locate an event on a timeline by detecting a selection action on the event on a calendar. A context menu is displayed on the calendar, where the context menu includes a search control. An activation of the search control is detected. The event is located on the timeline of the calendar. The event is displayed on the timeline.

Locating event on timeline

Hyperlapse results are generated from wide-angled, panoramic video. A set of wide-angled, panoramic video data is obtained. Video stabilization is performed on the obtained set of wide-angled, panoramic video data. Without user intervention, a smoothed camera path is automatically determined using at least one region of interest that is determined using saliency detection and semantically segmented frames of stabilized video data resulting from the video stabilization. A set of frames is determined to vary the velocity of wide-angled, panoramic rendered display of the hyperlapse results.

Controlling generation of hyperlapse from wide-angled, panoramic videos

Examples of the present disclosure relate to generating optimal scanning trajectories for 3D scenes. In an example, a moveable camera may gather information about a scene. During an initial pass, an initial trajectory may be used to gather an initial dataset. In order to generate an optimal trajectory, a reconstruction of the scene may be generated based on the initial data set. Surface points and a camera position graph may be generated based on the reconstruction. A subgradient may be determined, wherein the subgradient provides an additive approximation for the marginal reward associated with each camera position node in the camera position graph. The subgradient may be used to generate an optimal trajectory based on the marginal reward of each camera position node. The optimal trajectory may then be used by to gather additional data, which may be iteratively analyzed and used to further refine and optimize subsequent trajectories.

Optimal scanning trajectories for 3D scenes

Examples of the present disclosure describe systems and methods for scene reconstruction from bursts of image data. In an example, an image capture device may gather information from multiple positions within the scene. At each position, a burst of image data may be captured, such that other images within the burst may be used to identify common image features, anchor points, and geometry, in order to generate a scene reconstruction as observed from the position. Thus, as a result of capturing bursts from multiple positions in a scene, multiple burst reconstructions may be generated. Each burst may be oriented within the scene by identifying a key frame for each burst and using common image features and anchor points among the key frames to determine a camera position for each key frame. The burst reconstructions may then be combined into a unified reconstruction, thereby generating a high-quality reconstruction of the scene.

Scene reconstruction from bursts of image data

The subject disclosure is directed towards an image sensor that is controllable curved to adapt for differences in lens focal lengths. Variable data such as focal length data, measured curvature data and/or image quality data is received at a curve controller that curves the sensor based upon the variable data. In one aspect, a camera is provided with a lens having a variable focal length and a sensor capable of being dynamically curved. A curve controller receives image quality data and iteratively uses the image quality data to adjust the curvature to attempt to increase the image quality of a subsequent image to be captured.

Dynamically curved sensor for optical zoom lens

The mobile image viewing technique described herein provides a hands-free interface for viewing large imagery (e.g., 360 degree panoramas, parallax image sequences, and long multi-perspective panoramas) on mobile devices. The technique controls the imagery displayed on a display of a mobile device by movement of the mobile device. The technique uses sensors to track the mobile device's orientation and position, and front facing camera to track the user's viewing distance and viewing angle. The technique adjusts the view of a rendered imagery on the mobile device's display according to the tracked data. In one embodiment the technique can employ a sensor fusion methodology that combines viewer tracking using a front facing camera with gyroscope data from the mobile device to produce a robust signal that defines the viewer's 3D position relative to the display.

Natural user interfaces for mobile image viewing

The subject disclosure is directed towards a curved image sensor that is rotated in one or more rotational directions to compensate for camera movement, e.g., camera shake. In one aspect, sensors such as gyroscopes output camera movement data, which a controller uses to rotate the curved (e.g., hemispherical) image sensor about its center of curvature, e.g., via signals to electrical and/or magnetic movement mechanisms. Image processing may be used by the controller to rotate the image sensor.

Anti-shake correction system for curved optical sensor

Techniques for fabricating a semiconductor die having a curved surface can include placing a substantially flat semiconductor die in a recess surface of a concave mold such that corners or edges of the semiconductor die are unconstrained or are the only portions of the semiconductor die in physical contact with the concave mold. The semiconductor die can include through-die cut lines that can lead to substantially less tension in the semiconductor die as compared to the case where the semiconductor die does not include through-die cut lines. Accordingly, such through-die cut lines can allow for achieving relatively large curvatures.

Strain relieving die for curved image sensors

A system that comprises a receiver port to receive lens system design specifications; and a processor to: select a lens system type that includes one or more preliminary lenses, the selecting based, at least in part, on the lens system design specifications; from a lens database, select one or more catalog lenses to add to or replace the one or more preliminary lenses of the lens system type to construct a lens system; vary distances between or among the one or more catalog lenses and/or the one or more preliminary lenses of the lens system; and as the distances vary, determine optical behavior of the lens system.

Automatic lens design using off-the-shelf components

A for-your-information event can be received in a computer system. The for-your-information event can include an event time field that indicates a scheduled time for the for-your-information event. The computer system may not automatically block the scheduled time of the for-your-information event on a calendar in response to receiving the for-your-information event. User input indicating that the for-your-information event is to be converted to a regular event can be received. In response to the user input, the computer system can convert the for-your-information event to a regular event. Converting the for-your information event can include automatically including the scheduled time of the for-your-information event as a scheduled time for the converted regular event. Also in response to the user input, the computer system can automatically block the scheduled time of the for-your-information event on the calendar.

For-your-information events

A 'Food Logger' provides various approaches for learning or training one or more image-based models (referred to herein as 'meal models') of nutritional content of meals. This training is based on one or more datasets of images of meals in combination with &#x201c;meal features&#x201d; that describe various parameters of the meal. Examples of meal features include, but are not limited to, food type, meal contents, portion size, nutritional content (e.g., calories, vitamins, minerals, carbohydrates, protein, salt, etc.), food source (e.g., specific restaurants or restaurant chains, grocery stores, particular pre-packaged foods, school meals, meals prepared at home, etc.). Given the trained models, the Food Logger automatically provides estimates of nutritional information based on automated recognition of new images of meals provided by (or for) the user. This nutritional information is then used to enable a wide range of user-centric interactions relating to food consumed by individual users.

Food logging from images

Various technologies described herein pertain to creation of an output hyper-lapse video from an input video. Values indicative of overlaps between pairs of frames in the input video are computed. A value indicative of an overlap between a pair of frames can be computed based on a sparse set of points from each of the frames in the pair. Moreover, a subset of the frames from the input video are selected based on the values of the overlaps between the pairs of the frames in the input video and a target frame speed-up rate. Further, the output hyper-lapse video is generated based on the subset of the frames. The output hyper-lapse video can be generated without a remainder of the frames of the input video other than the subset of the frames.

Real-time hyper-lapse video creation via frame selection

Various technologies described herein pertain to generating a video loop. An input video can be received, where the input video includes values at pixels over a time range. An optimization can be performed to determine a respective input time interval within the time range of the input video for each pixel from the pixels in the input video. The respective input time interval for a particular pixel can include a per-pixel loop period and a per-pixel start time of a loop at the particular pixel within the time range from the input video. Moreover, an output video can be created based upon the values at the pixels over the respective input time intervals for the pixels in the input video.

Automated video looping with progressive dynamism

Growing community of inventors

Seattle, WA, United States of America

Neel Suresh Joshi