Intel Corporation

Technion Research & Development Foundation Ltd

Other

Hewlett-Packard Development Company, L.p.

New York Society for the Relief of the Ruptured and Crippled, Maintaining the Hospital for Special Surgery

Mediguide Ltd.

Intel Benelux B.v.

Ron Kimmel

Michael Bronstein

Alex Bronstein

Aviad Zabatani

Erez Sperling

Vitaly Surazhsky

Alexander Bronstein

Ohad Menashe

Sagi Ben Moshe

Sagy Bareket

David Haim Silver

Aaron Wetzler

Barak Freedman

Alon Wolf

Alfred M Bruckstein

Ofir Mulla

Howard J Hillstrom

Ofir Weber

Benjamin Groisser

Roger F Widmann

Yohai Devir

Ilya Blayvas

Guy Rosman

Yonathan Aflalo

Yael Petrank

Oded Sudarsky

Avishav Sidlesky

Matan Sela

Sagi BenMoshe

Daniel R Tretter

Doron Shaked

Jonathan Yen

Ron Maurer

Irwin E Sobel

Tal Nir

Michael Elad

Eitan Zeiler

Joseph Gil

Anastasia Dubrovina

Rom Hershkovitz

Yaron Honen

Roberto Mecca

Zachi Karni

Ohad Manashe

An example apparatus includes: a camera to record an image; memory to store instructions; and a processor in circuit with the memory, the processor to execute the instructions to: determine a depth based on: (a) the image and (b) a calibration parameter of the camera; and adjust the calibration parameter based on a temperature of the camera and the depth.

Online compensation of thermal distortions in a stereo depth camera

A method comprising: generating a parametrized three-dimensional (3D) body surface model on a training set comprising a plurality of 3D scans of subjects, wherein at least some of said 3D scans are of subjects having a skeletal deformity; receiving one or more target 3D scans of a target subject; optimizing said body surface model with respect to said one or more target 3D scans to calculate a target body surface model of said target subject; training a skeletal estimation model on a training set comprising: (i) body surface models of a plurality of subjects, and (ii) skeletal landmarks sets of said plurality of subjects; and applying said trained skeletal estimation model to said calculated target body surface model of said target subject, to estimate a skeletal shape of said target subject.

Detecting spinal shape from optical scan

A method comprising: receiving a radiographic image dataset representing a sequential radiographic scan of a region of a human subject; receiving three-dimensional (3D) image data representing an optical scan of a surface of said region, wherein said 3D image data is performed simultaneously with said sequential radiographic scan; estimating a time-dependent motion of said subject during said acquisition, relative to a specified position, based, at least in part, on said 3D image data; and using said estimating to determine corrections for said radiographic image dataset, based, at least in part, on a known transformation between corresponding coordinate systems of said radiographic image dataset and said 3D image data.

Correcting motion-related distortions in radiographic scans

Example range estimation apparatus disclosed herein include a first signal processor to estimate a signal power parameter and a noise power parameter of a LIDAR system based on first data to be output from a light capturing device of the LIDAR system. Disclosed example range estimation apparatus also include a second signal processor to generate templates corresponding to different possible propagation delays associated with second data to be output from the light capturing device, the second data associated with a modulated light beam projected by the LIDAR system, the templates generated based on the signal power parameter and the noise power parameter, and the second data to have a higher sampling rate and a lower quantization resolution than the first data. In some examples, the second signal processor is also to determine, based on the templates, an estimated propagation delay associated with the second data.

Range estimation for light detecting and ranging (LIDAR) systems

A tracking system, comprising: multiple beacons, each associated with a different cyclic equivalence class of code-word length n, and each configured to broadcast a bit-stream comprising a repeating code-word, where the code-word belongs to the associated cyclic equivalence class; and a mobile tracking unit, comprising: a sensor, and a processor, wherein the sensor is configured to simultaneously detects at least some of the bit streams, and provide each sensed bit stream in real-time to the processor, wherein for each bit-stream received by the processor from the sensor, the processor is configured to identify the beacon that broadcasted the bit-stream using the first n received bits.

Tracking using encoded beacons

A method for planning a current medical procedure to be performed on a body part of a current patient includes obtaining a current first representation of a surface of the body part of the current patient, and either obtaining a representation of a desired result surface for the current medical procedure or selecting parameters for the current medical procedure. The method further includes retrieving at least one best matching record from a database of records of previously performed medical procedures of previous patients, based on a similarity criterion. Each record includes: parameters of a previously performed procedure of the previously performed medical procedures, a first representation of the body part of the previous patient prior to performance of the previously performed procedure, and a second representation of a body part of the previous patient after the performance of the previously performed procedure. The method further includes determining parameters of the current medical procedure based on the parameters of the retrieved record, or generating a representation of a predicted result for the current medical procedure on the body part of the current patient based on the retrieved record.

Surgery planning based on predicted results

A system comprising: a camera configured to capture one or more images of a user's hand; and a computer configured to: receive the one or more captured images, apply a mapping function to the received one or more images, thereby yielding one or more coordinates associated with at least one feature of the user's hand, wherein the mapping function is derived from a set of labeled images that are produced by applying a machine learning algorithm to training data which comprises images of a trainer's hand, wherein the images are labeled with coordinates obtained from multiple magnetic sensors attached to the trainer's hand.

Gesture recognition using multi-sensory data

A method and apparatus for auto range control are described. In one embodiment, the apparatus comprises a projector configured to project a sequence of light patterns on an object; a first camera configured to capture a sequence of images of the object illuminated with the projected light patterns; a controller coupled to the projector and first camera and operable to receive the sequence of images and perform range control by controlling power of the sequence of light patterns being projected on the object and exposure time of a camera based on information obtained from the sequence of images captured by the camera.

Auto range control for active illumination depth camera

Example range estimation apparatus disclosed herein include a first signal processor to process first data output from a light capturing device of a LIDAR system to estimate signal and noise power parameters of the LIDAR system. Disclosed example range estimation apparatus also include a second signal processor to generate templates corresponding to different possible propagation delays associated with second data output from the light capturing device while a modulated light beam is projected by the LIDAR system, the templates generated based on the signal and noise power parameters, and the second data having a higher sampling rate and a lower quantization resolution than the first data. In some examples, the second signal processor also cross-correlates the templates with the second data to determine an estimated propagation delay associated with the second data, the estimated propagation delay convertible to an estimated range to an object that reflected the modulated light beam.

Arrangements (e.g., apparatus, system, method, article of manufacture) for reconstructing a depth image of a scene. Some embodiments include: a processor; and a non-transitory computer readable medium to store a set of instructions for execution by the processor, the set of instructions to cause the processor to perform various operations. Operations include: collecting multiple data sets for a code-modulated light pulse reflected from an object in a scene, with each data set associated with a direction in a set of directions for the reflected code-modulated light pulse; assigning a fitness value to each data set based on one or more parameters of a model; and reconstructing a depth image providing a depth at each direction based on a corresponding data set and fitness value, the depth to correspond with a round-trip delay time of the code-modulated light pulse.

Range reconstruction using shape prior

In accordance with disclosed embodiments, there are provided systems, methods, and apparatuses for implementing maximum likelihood image binarization in a coded light range camera. For instance, a depth camera is described having therein a projector to project a collection of planes, each at a different angle of projection, onto a scene via a plurality of coded pattern images, each of the coded pattern images having encoded therein via a plurality of stripes, the angle of projection for the plane of projection within which the respective coded pattern image is projected; a detector to capture the plurality of coded pattern images from the scene; a processing component to adjust for ambient illumination and reflection properties of the scene; in which the processing component to further output a bit value for each pixel in the captured plurality of coded pattern images and to output a sub-pixel offset for the pixels positioned upon transitions of the plurality of stripes in the captured plurality of coded pattern images; a decoder to decode each of the plurality of coded pattern images and to adjust the decoded plurality of coded image patterns based on the sub-pixel offsets to determine the angle of projection for the corresponding plane of projection; and a triangulator to determine a position of an object in the scene based on an intersection of the determined angle of projection for the corresponding plane of projection with a known ray emanating from the detector that detected the plurality of the coded pattern images from the scene. Other related embodiments are disclosed.

Systems, methods, and apparatuses for implementing maximum likelihood image binarization in a coded light range camera

Growing community of inventors

Haifa, Israel

Ron Kimmel