Microsoft Technology Licensing, LLC

Pablo Luis Sala

Vivek Pradeep

Steven Nabil Bathiche

Karlton David Powell

Dan S Johnson

Luis Eduardo Cabrera-Cordon

Norberto Goussies

Liang Wang

Sing Bing Kang

Jamie Daniel Joseph Shotton

Moshe Randall Lutz

John Guido Atkins Weiss

Matheen Siddiqui

Luis E Cabrera-Cordon

Dan Johnson

An accessory for a touch-sensitive display includes an image transfer structure and a capacitive marker. The image transfer structure is configured to relay or transfer, above the touch-sensitive display, an optical display plane of an image displayed by the touch-sensitive display. Further, the capacitive marker is capacitively-recognizable by the touch-sensitive display such that the touch-sensitive display visually presents the image in alignment with the image transfer structure.

Touch-display accessory with relayed display plane

A method for touch classification includes obtaining frame data representative of a plurality of frames captured by a touch-sensitive device, analyzing the frame data to define a respective blob in each frame of the plurality of frames, the blobs being indicative of a touch event, computing a plurality of feature sets for the touch event, each feature set specifying properties of the respective blob in each frame of the plurality of frames, and determining a type of the touch event via machine learning classification configured to provide multiple non-bimodal classification scores based on the plurality of feature sets for the plurality of frames, each non-bimodal classification score being indicative of an ambiguity level in the machine learning classification.

Touch classification

An entity-tracking computing system receives sensor information from a plurality of different sensors. The positions of entities detected by the various sensors are resolved to an environment-relative coordinate system so that entities identified by one sensor can be tracked across the fields of detection of other sensors.

Entity-tracking computing system

A system includes a memory and a processor configured to select a set of scene point pairs, to determine a respective feature vector for each scene point pair, to find, for each feature vector, a respective plurality of nearest neighbor point pairs in feature vector data of a number of models, to compute, for each nearest neighbor point pair, a respective aligning transformation from the respective scene point pair to the nearest neighbor point pair, thereby defining a respective model-transformation combination for each nearest neighbor point pair, each model-transformation combination specifying the respective aligning transformation and the respective model with which the nearest neighbor point pair is associated, to increment, with each binning of a respective one of the model-transformation combinations, a respective bin counter, and to select one of the model-transformation combinations in accordance with the bin counters to detect an object and estimate a pose of the object.

Real-time, model-based object detection and pose estimation

Embodiments are disclosed that relate to selectively enhancing content displayed on a display device. For example, one disclosed embodiment provides a method comprising tracking a gaze location at which a gaze of a user intersects the graphical user interface, and upon detecting a gaze pattern that meets a defined gaze pattern condition indicative of difficulty in perceiving an item at the gaze location, displaying the item in at the gaze location a visually enhanced form via a user interface object displayed separately from the item at the gaze location.

Visual enhancements based on eye tracking

Global and local light detection techniques in optical sensor systems are described. In one or more implementations, a global lighting value is generated that describes a global lighting level for a plurality of optical sensors based on a plurality of inputs received from the plurality of optical sensors. An illumination map is generated that describes local lighting conditions of respective ones of the plurality of optical sensors based on the plurality of inputs received from the plurality of optical sensors. Object detection is performed using an image captured using the plurality of optical sensors along with the global lighting value and the illumination map.

Global and local light detection in optical sensor systems

Object detection techniques for use in conjunction with optical sensors is described. In one or more implementations, a plurality of inputs are received, each of the inputs being received from a respective one of a plurality of optical sensors. Each of the plurality of inputs are classified using machine learning as to whether the inputs are indicative of detection of an object by a respective said optical sensor.

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Bothell, WA, United States of America

Pablo Luis Sala