Microsoft Technology Licensing, LLC

Filip Panjevic

Vuk Jovanovic

Alexandru Octavian Balan

Michael Edward Samples

Salim Sirtkaya

Taras Khapko

Andrew William Fitzgibbon

Drew Edward Steedly

Milos Raskovic

Milos Lazarevic

Milan Sesum

Aljosa Obuljen

William Douglas Guyman

Ivan Razumenic

Vladimir Carapic

Jelena Mojasevic

Ronald Boskovic

Vanja Petrovic Tankovic

Ruven Jaime Rivera, Iii

Examples are disclosed that relate to a camera model for a machine vision application. One example provides instructions executable to receive image data obtained by an image sensor of a camera, the image data capturing a calibration pattern comprising a plurality of calibration features, for each of one or more imaged calibration features in the image data, determine an object space location of the imaged calibration feature, and determine a distance between the object space location and a corresponding ray of a camera model, the camera model defining a plurality of rays that each represent a relationship of an image space location on the image sensor to object space. The instructions are further executable to determine a value of a cost function based on the distances, adjust the camera model until the cost function meets a target condition, and use the camera model in a machine vision application.

Calibrating a machine vision camera

One disclosed example provides a computing device configured to receive from an image sensor of a head-mounted device environmental tracking exposures and handheld object tracking exposures, determine a pose of the handheld object with respect to the head-mounted device based upon the handheld object tracking exposures, determine a pose of the head-mounted device with respect to a surrounding environment based upon the environmental tracking exposures, derive a pose of the handheld object relative to the surrounding environment based upon the pose of the handheld object with respect to the head-mounted device and the pose of the head-mounted device with respect to the surrounding environment, and output the pose of the handheld object relative to the surrounding environment for controlling a user interface displayed on the head-mounted device.

Tracking wearable device and handheld object poses

Examples are disclosed herein that relate to determining a pose of a handheld object. One example provides a computing system configured to determine a pose of a handheld object comprising a plurality of light sources by acquiring image data of a surrounding environment, detecting a subset of light sources of the plurality of light sources of the handheld object in the image data, and performing a search, without using previous pose data, to determine the pose of the handheld object relative to the computing system. The computing system is further configured to use the pose determined to perform a later search for an updated pose of the handheld object, and if the later search fails to find the updated pose, determine the updated pose by again performing the search without using previous pose data.

Determining a pose of a handheld object

One disclosed example provides a head-mounted device including a stereo camera arrangement, a logic device configured to execute instructions, and a storage device storing instructions executable by the logic device to, for each camera in the stereo camera arrangement, receive image data of a field of view of the camera, detect light sources of a handheld object in the image data, and based upon the light sources detected, determine a pose of the handheld object. The instructions are executable to, based upon the pose of the handheld object determined for each camera in the stereo camera arrangement, calibrate the stereo camera arrangement.

Calibration of stereo cameras and handheld object

A paragraph alignment detection engine and a section reconstruction engine. The paragraph alignment detection engine determines the paragraph alignment of a paragraph and updates the paragraph alignment property of the paragraph in the data store for single line and multi-line paragraphs. The paragraph alignment detection engine employs per paragraph comparisons and relative comparisons to other paragraphs to determine the paragraph alignment of a single line paragraph. The paragraph alignment detection engine employs per paragraph comparisons and relative comparisons of the lines of a paragraph to determine the paragraph alignment of a multi-line paragraph. The section reconstruction engine minimizes the number of sections created in the flow format document by identifying the columns on each page, combining contiguous pages with the same column layout into a single section, and creating alternative objects to contain regions associated special cases in lieu of creating additional sections.

Paragraph alignment detection and region-based section reconstruction

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Belgrade, Republic of Serbia

Filip Panjevic