Microsoft Technology Licensing, LLC

Liang Wang

Steven Nabil Bathiche

Vivek Pradeep

Luis Eduardo Cabrera-Cordon

Pablo Luis Sala

Sing Bing Kang

Karlton David Powell

Jamie Daniel Joseph Shotton

John Michael Lutian

Matheen Siddiqui

Luis E Cabrera-Cordon

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

Optical stylus interaction techniques are described. In an implementation, a display of a computing device includes optical sensors capable of detecting images projected by a stylus. A stylus may include a projection system to project various images used to convey interaction information that may be interpreted by the computing device. Based on recognition of different projected images, a context for interaction of the stylus may be ascertained and corresponding operations may be performed by the computing device. This may include resolving a spatial position of the stylus relative to the display device as well as movement of the stylus and images that defines various stylus-based gestures. In addition, the environment for optical stylus interaction enables a writing mode that emulates natural writing by mapping different projectable images to changes in pressure applied to the stylus when in contact with the display.

Optical stylus interaction

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.

Object detection in optical sensor systems

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Sammamish, WA, United States of America

Liang Wang