Microsoft Technology Licensing, LLC

Other

Andrew Blake

Antonio Criminisi

Vladimir Kolmogorov

Kentaro Toyama

Carsten Curt Eckard Rother

Philip H S Torr

Carsten Rother

Michel Gangnet

Patrick Perez

Geoffrey Cross

Bernhard Schoelkopf

Sami Romdhani

Ralf Herbrich

Toby Leonard Sharp

Lyndsay Williams

Ankur Agarwal

Andrew William Fitzgibbon

Jamie Shotton

Thore K H Graepel

William Vablais

James Srinivasan

Victor Lempitsky

Jamie Daniel Joseph Shotton

Pushmeet Kohli

Ingemar J Cox

Abigail Jane Sellen

William A S Buxton

Thore Graepel

Padmanabhan Anandan

Christopher Michael Bishop

Youssef Hamadi

Lucas Bordeaux

Victor Sergeyevich Lempitsky

Inmar-Ella Givoni

Kenneth Robert Woodberry

Sanjiv Kumar

Adam Gary Emfield

Arridhana Ciptadi

Daniel Joseph Holland

Lynn Faith Gladden

Ketaro Toyama

Bhaskara Marthi

Varun Gulshan

Bill Buxton

A real-time stereo video signal of a captured scene with a physical foreground object and a physical background is received. In real-time, a foreground/background separation algorithm is used on the real-time stereo video signal to identify pixels from the stereo video signal that represent the physical foreground object. A video sequence may be produced by rendering a 3D virtual reality based on the identified pixels of the physical foreground object.

Stereo video for gaming

Calibration of gaze tracking equipment is described, for example, in a desktop computing scenario. In various examples, an explicit calibration phase is carried out, optionally followed by an implicit calibration phase. In examples, the explicit calibration phase comprises requesting and receiving user manual input events associated with specified locations and measuring gaze associated with the manual input events. In examples, the implicit calibration phase is carried out without disturbing other activity of a user in the desktop computing environment, such as operating a graphical user interface. In various examples calibration data is stored in a plurality of buffers and used to control switching between explicit and implicit calibration phases.

Gaze calibration

Described herein are various technologies pertaining to diagnosing and/or prescribing treatment for osteoporosis. A bone of a patient is subjected to an MRI scan, and the resultant signal is subjected to a likelihood function. The output of the likelihood function are values that are parameters that are employed to ascertain connectivity of trabeculae in the bone of the patient and volumetric trabecular density of the bone.

MRI scanner that outputs bone strength indicators

An electronic stylus system includes an electronic stylus and base receiving unit. The electronic stylus includes a first ultrasonic transmitter, a second ultrasonic transmitter, an electromagnetic transmitter, and a writing tip. The base receiver unit includes a first ultrasonic receiver, a second ultrasonic receiver, and an electromagnetic receiver. The ultrasonic receivers of the base unit are operable to receive signals transmitted by the ultrasonic transmitters of the electronic stylus. Similarly, the electromagnetic receiver of the base unit is operable to receive signals transmitted by the electromagnetic transmitter of the stylus. The location of the tip of the electronic stylus relative to a given reference point is determined using the locations of two ultrasonic transmitters relative to the two ultrasonic receivers.

Determining the location of the tip of an electronic stylus

Image labeling with global parameters is described. In an embodiment a pose estimation system executes automatic body part labeling. For example, the system may compute joint recognition or body part segmentation for a gaming application. In another example, the system may compute organ labels for a medical imaging application. In an example, at least one global parameter, for example body height is computed for each of the images to be labeled. In an example, the global parameter is used to modify an image labeling process. For example the global parameter may be used to modify the input image to a canonical scale. In another example, the global parameter may be used to adaptively modify previously stored parameters of the image labeling process. In an example, the previously stored parameters may be computed from a reduced set of training data.

Image labeling with global parameters

Methods of image segmentation using reduced foreground training data are described. In an embodiment, the foreground and background training data for use in segmentation of an image is determined by optimization of a modified energy function. The modified energy function is the energy function used in image segmentation with an additional term comprising a scalar value. The optimization is performed for different values of the scalar to produce multiple initial segmentations and one of these segmentations is selected based on pre-defined criteria. The training data is then used in segmenting the image. In other embodiments further methods are described: one places an ellipse inside the user-defined bounding box to define the background training data and another uses a comparison of properties of neighboring image elements, where one is outside the user-defined bounding box, to reduce the foreground training data.

Image segmentation using reduced foreground training data

Methods of updating image segmentation following user input are described. In an embodiment, the properties used in computing the different portions of the image are updated as a result of one or more user inputs. Image elements which have been identified by a user input are given more weight when updating the properties than other image elements which have already been assigned to a particular portion of the image. In another embodiment, an updated segmentation is post-processed such that only regions which are connected to an appropriate user input are updated.

Updating image segmentation following user input

Existing remote workspace sharing systems are difficult to use. For example, changes made on a common work product by one user often appear abruptly on displays viewed by remote users. As a result the interaction is perceived as unnatural by the users and is often inefficient. Images of a display of a common work product are received from a camera at a first location. These images may also comprise information about objects between the display and the camera such as a user's hand editing a document on a tablet PC. These images are combined with images of the shared work product and displayed at remote locations. Advance information about remote user actions is then visible and facilitates collaborative mediation between users. Depth information may be used to influence the process of combining the images.

Remote workspace sharing

A method of labeling pixels in an image is described where the pixel label is selected from a set of three or more labels. The pixel labeling problem is reduced to a sequence of binary optimizations by representing the label value for each pixel as a binary word and then optimizing the value of each bit within the word, starting with the most significant bit. Data which has been learned from one or more training images is used in the optimization to provide information about the less significant bits within the word.

Optimization of multi-label problems in computer vision

Image segmentation using star-convexity constraints is described. In an example, user input specifies positions of one or more star centers in a foreground to be segmented from a background of an image. In embodiments, an energy function is used to express the problem of segmenting the image and that energy function incorporates a star-convexity constraint which limits the number of possible solutions. For example, the star-convexity constraint may be that, for any point p inside the foreground, all points on a shortest path (which may be geodesic or Euclidean) between the nearest star center and p also lie inside the foreground. In some examples continuous star centers such as lines are used. In embodiments a user may iteratively edit the star centers by adding brush strokes to the image in order to progressively change the star-convexity constraints and obtain an accurate segmentation.

Image segmentation using star-convexity constraints

An image processing system is described which automatically labels image elements of a digital image. In an embodiment an energy function describing the quality of possible labelings of an image is globally optimized to find an output labeled image. In the embodiment, the energy function comprises terms that depend on at least one non-local parameter. For example, the non-local parameter describes characteristics of image elements having the same label. In an embodiment the global optimization is achieved in a practical, efficient manner by using a tree structure to represent candidate values of the non-local parameter and by using a branch and bound process. In some embodiments, the branch and bound process comprises evaluating a lower bound of the energy function by using a min-cut process. For example, the min-cut process enables the lower bound to be evaluated efficiently using a graphical data structure to represent the lower bound.

Labeling image elements

A method of segmenting image elements into a foreground and background is described, such that only the foreground elements are part of a volume of interest for stereo matching. This reduces computational burden as compared with computing stereo matching over the whole image. An energy function is defined using a probabilistic framework and that energy function approximated to require computation only over foreground disparities. An optimization algorithm is used on the energy function to perform the segmentation.

Segmenting image elements

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Stapleford, United Kingdom

Andrew Blake