Daqri, LLC

Eric Douglas Lundquist

Brian Mullins

Nalin Senthamil

Laura Berman

Arye Barnehama

Teresa Ann Nick

A device for multi-spectrum segmentation for computer vision is described. A first optical sensor operates within a first spectrum range and generates first image data corresponding to a first image captured by the first optical sensor. A second optical sensor operates within a second spectrum range different from the first spectrum range and generates second image data corresponding to a second image captured by the second optical sensor. The device identifies a first region in the first image, maps a first portion of the first image to a second portion of the second image data, provides the second portion of the second image data to a server that generates augmented reality content based on the second portion of the second image data. The device displays the augmented reality content.

Multi-spectrum segmentation for computer vision

A system and method for multi-spectrum segmentation for computer vision is described. A first sensor captures an image within a first spectrum range and generates first sensor data. A second sensor captures an image within a second spectrum range different than the first spectrum range and generates second sensor data. A multi-spectrum segmentation module identifies a segmented portion of the image within the second spectrum range based on: the second sensor data, a subset of the first sensor data corresponding to the segmented portion of the image within the second spectrum range, and a segmented portion of the image at the second spectrum range corresponding to the subset of the first sensor data. The multi-spectrum segmentation module identifies a physical object in the segmented portion of the image within the second spectrum range, and a device generates augmented reality content based on the identified physical object.

A system and method for identifying changes in a state or activity is described. A real-time event monitoring application, implemented in a hardware processor, computes features over varying sampling periods, which are stored in cascading buffers. The various sampling periods enable determination of trends of a computed feature, comparison of the trends of the computed feature with trends of other features, normalization of the computed feature based on the same or different features at the same or different time scales, scaling of the normalized computed feature relative to other features at the same or different time scale, and identification of a change in state or activity based on a scaled normalized computed feature.

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Los Angeles, CA, United States of America

Eric Douglas Lundquist