Element Inc.

Siemens Medical Solutions Usa, Inc.

Siemens Aktiengesellschaft

Siemens Corporation

Siemens Healthcare Gmbh

Qunnect, Inc.

Yang Wang

Bogdan Georgescu

Dorin Comaniciu

Adam Perold

Dushyant Goyal

Razvan Ioan Ionasec

Yann Andre LeCun

Fengjun Lv

Ingmar Voigt

Saurabh Datta

Tommaso Mansi

Yefeng Zheng

Sasa Grbic

Helene C Houle

Dime Vitanovski

Sagar Waghmare

Puneet Sharma

Shaohua Kevin Zhou

Jan Boese

Matthias John

Viorel Mihalef

Fernando Vega-Higuera

Mael Flament

Mehdi Namazi

Alexander Craddock

Yann Lecun

A quantum memory device and methods for storage and retrieval of a qubit from the quantum memory device are described. The quantum memory device includes a first optical component to convert an input qubit encoded in an arbitrary polarization state of a photon into a spatial qubit propagating in a pair of parallel optical rails, an atomic vapor memory to store the spatial qubit in an atomic vapor, and a second optical component to combine the spatial qubit, when retrieved from the atomic vapor memory, into an output qubit encoded in an arbitrary polarization state of a photon.

High fidelity storage and retrieval of quantum information in a warm atomic vapor cell device

Provided herein is are computer-implemented methods, computer-implemented systems, and non-transitory computer-readable storage media for registering a user's eye gaze profile via a mobile device having a screen and a front facing camera. Also provided herein is are computer-implemented methods, computer-implemented systems, and non-transitory computer-readable storage media for determining a spoofing attempt by a user during a request to access a resource via a mobile device.

Methods, systems, and media for anti-spoofing using eye-tracking

Provided herein are devices, systems, and methods for detecting spoofing of a 3D object, using a 2D representation, in a mobile object authentication process, comprising capturing image data of the 3D object by a front-facing camera, to record a current spatial characteristic of the 3D object, while a front-facing screen displays an authentication pattern comprising a plurality of regions, wherein at least one of the regions varies in at least one of: brightness, position, size, shape, and color over time causing a variance of lighting effects which create highlights and shadows on the 3D object over time. The devices, systems, and methods thereby provide an efficient and secure process for determining if spoofing of the 3D object, using a 2D representation, is attempted in a mobile authentication process, by comparing the current spatial characteristic of the 3D object with a stored reference spatial characteristic of the 3D object.

Methods, systems, and media for detecting spoofing in mobile authentication

Described are methods, systems, and medias for detecting spoofing of biometric identity recognition and/or validating an identity recognition match by using the camera of a mobile device, processing the user's face image or set of images at a first and second distance to generate first and second data representations, processing the first data representation into a predictive model, and comparing the data representation with the predictive model.

Methods and systems for detecting spoofing of facial recognition in connection with mobile devices

The present invention relates generally to the use of biometric technology for authentication and identification, and more particularly to non-contact based solutions for authenticating and identifying users, via computers, such as mobile devices, to selectively permit or deny access to various resources. In the present invention authentication and/or identification is performed using an image or a set of images of an individual's palm through a process involving the following key steps: (1) detecting the palm area using local classifiers; (2) extracting features from the region(s) of interest; and (3) computing the matching score against user models stored in a database, which can be augmented dynamically through a learning process.

System and method for biometric authentication in connection with camera-equipped devices

Computerized characterization of cardiac wall motion is provided. Quantities for cardiac wall motion are determined from a four-dimensional (i.e., 3D+time) sequence of ultrasound data. A processor automatically processes the volume data to locate the cardiac wall through the sequence and calculate the quantity from the cardiac wall position or motion. Various machine learning is used for locating and tracking the cardiac wall, such as using a motion prior learned from training data for initially locating the cardiac wall and the motion prior, speckle tracking, boundary detection, and mass conservation cues for tracking with another machine learned classifier. Where the sequence extends over multiple cycles, the cycles are automatically divided for independent tracking of the cardiac wall. The cardiac wall from one cycle may be used to propagate to another cycle for initializing the tracking. Independent tracking in each cycle may reduce or avoid inaccuracies due to drift.

Computerized characterization of cardiac motion in medical diagnostic ultrasound

System and method for biometric authentication in connection with camera equipped devices

A method and system for fusion of multi-modal volumetric images is disclosed. A first image acquired using a first imaging modality is received. A second image acquired using a second imaging modality is received. A model and of a target anatomical structure and a transformation are jointly estimated from the first and second images. The model represents a model of the target anatomical structure in the first image and the transformation projects a model of the target anatomical structure in the second image to the model in the first image. The first and second images can be fused based on estimated transformation.

Method and system for model-based fusion of multi-modal volumetric images

Velocities are unaliased using conditional random fields. To constrain the energy minimization function, a global term includes a measure of a level of aliasing. In one example, the measure of the level of aliasing is based on a change in volume, such as the volume of the left ventricle. The unaliasing is performed along one or more surfaces, such as surfaces intersecting the mitral annulus and the left ventricle outflow tract. The anatomy used is identified and/or tracked using one or more machine-learnt detectors. Both B-mode and velocity information may be used for detecting the anatomy.

Flow quantification in ultrasound using conditional random fields with global consistency

A mitral valve is detected in transthoracic echocardiography. The ultrasound transducer is positioned against the chest of the patient rather than being inserted within the patient. While data acquired from such scanning may be noisier or have less resolution, the mitral valve may still be automatically detected. Using both B-mode data representing tissue as well as flow data representing the regurgitant jet, the mitral valve may be detected automatically with a machine-learnt classifier. A series of classifiers may be used, such as determining a position and orientation of a valve region with one classifier, determining a regurgitant orifice with another classifier, and locating mitral valve anatomy with a third classifier. One or more features for some of the classifiers may be calculated based on the orientation of the valve region.

Mitral valve detection for transthoracic echocardiography

Heart valve operation is assessed with patient-specific medical diagnostic imaging data. To deal with the complex motion of the passive valve tissue, a hierarchal model is used. Rigid global motion of the overall valve, non-rigid local motion of landmarks of the valve, and surface motion of the valve are modeled sequentially. For the non-rigid local motion, a spectral trajectory approach is used in the model to determine location and motion of the landmarks more efficiently than detection and tracking. Given efficiencies in processing, more than one valve may be modeled at a same time. A graphic overlay representing the valve in four dimensions and/or quantities may be provided during an imaging session. One or more of these features may be used in combination or independently.

Valve assessment from medical diagnostic imaging data

A method quantifies cardiac volume flow for an imaging sequence. The method includes receiving data representing three-dimensions and color Doppler flow data over a plurality of frames, constructing a ventricular model based on the data representing three-dimensions for the plurality of frames, the ventricular model including a sampling plane configured to measure the cardiac volume flow, computing volume flow samples based on the sampling plane and the color Doppler flow data, and correcting the volume flow samples for aliasing based on volumetric change in the ventricular model between successive frames of the plurality of frames.

Cardiac flow quantification with volumetric imaging data

A method and system for patient-specific modeling of the whole heart anatomy, dynamics, hemodynamics, and fluid structure interaction from 4D medical image data is disclosed. The anatomy and dynamics of the heart are determined by estimating patient-specific parameters of a physiological model of the heart from the 4D medical image data for a patient. The patient-specific anatomy and dynamics are used as input to a 3D Navier-Stokes solver that derives realistic hemodynamics, constrained by the local anatomy, along the entire heart cycle. Fluid structure interactions are determined iteratively over the heart cycle by simulating the blood flow at a given time step and calculating the deformation of the heart structure based on the simulated blood flow, such that the deformation of the heart structure is used in the simulation of the blood flow at the next time step. The comprehensive patient-specific model of the heart representing anatomy, dynamics, hemodynamics, and fluid structure interaction can be used for non-invasive assessment and diagnosis of the heart, as well as virtual therapy planning and cardiovascular disease management. Parameters of the comprehensive patient-specific model are changed or perturbed to simulate various conditions or treatment options, and then the patient specific model is recalculated to predict the effect of the conditions or treatment options.

Growing community of inventors

Princeton, NJ, United States of America

Yang Wang