Microsoft Technology Licensing, LLC

Koninklijke Philips Corporation N.V.

Technion Research & Development Foundation Ltd

Carmel—haifa University Economic Corp Ltd.

Guy Gilboa

Sagi Katz

Avishai Adler

Liran Goshen

Yechiel Lamash

Tali Treibitz

Ori Spier

Giora Yahav

John Allen Tardif

David Cohen

Shlomo Gotman

Shlomo Felzenshtein

Eli Larry

Shachar Praisler

Yossi Bar Erez

Ilya Tcenov

Adam Wolff

A method comprising: receiving an image of a scene, segmenting the image into a plurality of segments, obtaining at least one depth sample from each of at least some of the segments, and with respect to each of the at least some of the segments, assigning a value of the depth sample to each pixel in the segment, to create a depth image of the image.

Image-guided depth sampling and reconstruction

A submersible vehicle comprising: an image sensor; a first illuminator; at least one hardware processor operatively coupled with said image sensor and first illuminator, and the at least one hardware processor being configured to: acquire a first image of a scene inside a medium, when there is no object along the line-of-sight (LOS), under illumination from a first location or orientation, acquire a second image of the scene inside the medium, when there is no object along the LOS, under illumination from a second, different location or orientation, and compute attenuation coefficients, one per color channel, based on backscatter in the first and second images.

Target-less calibration of turbid media

A method comprising acquiring a first image of a scene inside a medium, under illumination from a first location or orientation. The method comprises an action of acquiring a second image of the scene inside the medium, under illumination from a second, different location or orientation. The method comprises an action of computing attenuation coefficients, one per color channel, based on backscatter in the first and second images.

A method includes obtaining a boundary condition estimate. The boundary condition estimate includes at least an estimated outlet resistance of a vessel with a stenosis. The method further includes correcting the boundary condition estimate based on a severity of the stenosis, thereby creating a corrected boundary condition. The method further includes determining an FFR index based on the corrected boundary condition. The method further includes displaying the FFR index. A computing system includes a computer readable storage medium with instructions that iteratively determine at least an FFR index based on a severity of a stenosis of a vessel. The computing system further includes a computer processor that processes the instructions and generates the FFR index based on the severity of the stenosis of the vessel.

Fractional flow reserve (FFR) index with adaptive boundary condition parameters

A method includes at least a portion of an image displayed on a video screen or film, and generating a signal indicative thereof, wherein the at least a portion of the image includes encoded information identifying at least one of a visualization tool or information that are not available without the encoded information, identifying and reading the encoded information, and at least one of invoking the visualization tool or displaying the information identified and read from the encoded information.

Image visualization

As described herein, an unknown FFR is classified based on certain extracted features. In addition, an estimation of the unknown FFR can be determined based on certain extracted features. Furthermore, a confidence interval can be determined for the estimated FFR. In another instance, boundary conditions for determining an FFR via simulation are determined. The boundary conditions can be used to classify the unknown FFR.

Fractional flow reserve (FFR) index

A depth camera computing device is provided, including a depth camera and a data-holding subsystem holding instructions executable by a logic subsystem. The instructions are configured to receive a raw image from the depth camera, convert the raw image into a processed image according to a weighting function, and output the processed image. The weighing function is configured to vary test light intensity information generated by the depth camera from a native image collected by the depth camera from a calibration scene toward calibration light intensity information of a reference image collected by a high-precision test source from the calibration scene.

Learning from high quality depth measurements

An embodiment of the invention provides a time of flight 3D camera comprising a photosensor having a plurality of pixels that generate and accumulate photoelectrons responsive to incident light, which photosensor is tiled into a plurality of super pixels, each partitioned into a plurality of pixel groups and a controller that provides a measure of an amount of light incident on a super pixel responsive to quantities of photoelectrons from pixel groups in the super pixel that do not saturate a readout pixel comprised in the photosensor.

Adaptive high dynamic range camera

Compatibility between a depth image consumer and a plurality of different depth image producers is provided by receiving a native depth image having unsupported depth camera parameters that are not compatible with a depth image consumer, and converting the native depth image to a virtual depth image having supported virtual depth camera parameters that are compatible with the depth image consumer. This virtual depth image is then output to the depth image consumer.

Depth camera compatibility

Compatibility between a depth image consumer and a depth image producer is provided by receiving a native depth image having an unsupported type that is not supported by a depth image consumer, and processing the native depth image into an emulation depth image having a supported type that is supported by the depth image consumer. This emulation depth image is then output to the depth image consumer.

Techniques are provided for de-aliasing depth images. The depth image may have been generated based on phase differences between a transmitted and received modulated light beam. A method may include accessing a depth image that has a depth value for a plurality of locations in the depth image. Each location has one or more neighbor locations. Potential depth values are determined for each of the plurality of locations based on the depth value in the depth image for the location and potential aliasing in the depth image. A cost function is determined based on differences between the potential depth values of each location and its neighboring locations. Determining the cost function includes assigning a higher cost for greater differences in potential depth values between neighboring locations. The cost function is substantially minimized to select one of the potential depth values for each of the locations.

Growing community of inventors

Kiryat Tivon, Israel

Guy Gilboa