International Business Machines Corporation

Asaf Tzadok

Eugeniusz Walach

Joseph Shtok

Eugene Walach

Yochay Tzur

Leonid Karlinsky

Vladimir Kluzner

Alberto Valdes Garcia

Mattias Marder

Dan S Chevion

Ella Barkan

Sivan Harary

Bodhisatwa Sadhu

Doron Tal

Pavel Kisilev

Andre Heilper

Arun Paidimarri

Tal Drory

Amnon Ribak

Martin Tross

Uzi Shvadron

Yaakov Navon

Benjamin M Cohen

Guy Rosman

Israel Berger

Eugene Wallach

Andrei Heilper

Mark Yeck

Sharbell Hashoul

Huijuan Liu

Itoko Toshinari

Dan Chevion

Motion-related 3D feature extraction by receiving a set of sequential radar signal data frames associated with a subject, determining radar signal amplitude and phase for each data frame, determining radar signal phase changes between sequential data frames, and extracting, by a trained machine learning model, one or more three-dimensional features from the sequential radar signal data frames according to the radar signal amplitude and the radar signal phase changes between sequential data frames.

Three-dimensional feature extraction from frequency modulated continuous wave radar signals

A method includes generating a first modulated continuous wave from a generating location; transmitting the first modulated continuous wave to an object positioned at a distance from the generating location; generating a second modulated continuous wave from the generating location, wherein the second modulated continuous wave is generated at a predetermined time that is different from a predetermined time at which the first modulated continuous wave is generated; receiving, at a mixer, the first modulated continuous wave from the object; receiving, at the mixer, the second modulated continuous wave from the generating location; mixing the received first modulated continuous wave with the second modulated continuous wave to produce a beat signal to determine a range of the object from the generating location; and outputting the determined range of the object from the generating location.

Multi range radar system

A radar-based security screening system for detecting objects is described. The screening system includes a radar transmitter, a radar receiver, and a processing unit. In use, the radar transmitter steers a radar beam across a screening volume. The radar receiver, in turn, receives a return signal from an object over time as the object moves in the screening volume to create a three-dimensional temporal signature for the object. The processing unit classifies the three-dimensional temporal signature utilizing a classification process based on a deep neural network model, and provides an alert when the object is classified as an object of interest. During screening, a screened person is not required to remain still in a confined volume and is not exposed to harmful radiation.

Radar-based detection of objects while in motion

Methods and system for shaping radiation patterns are described. Given a plurality of radiation patterns corresponding to spatial combinations of a plurality of signals, a system can perform beam switching between the given plurality of radiation patterns within a configured time. The beam switching within the configured time can create a beam having a new radiation pattern within the signal modulation bandwidth.

Time-based beam switching in phased arrays

While a radar waveform generator coupled to a phased antenna array is inactive, a beam steering command is provided to the phased antenna array to cause the phased antenna array be configured for a given transmit and receive direction. While the radar waveform generator is active, received radar results are assigned to the given transmit and receive direction. the providing of the command and the assigning are repeated for a plurality of additional transmit and receive directions corresponding to a desired scan area.

Coordinated waveform generation, control, and associated beam steering

A method for a Human-Computer-Interaction (HCI) processing includes receiving data derived from reflected signals of a steerable-beam antenna and processing the received data in a deep learning machine architecture.

Gesture recognition using 3D MM-wave radar

There is provided a method of computing a camera pose of a digital image, comprising: computing query-regions of a digital image, each query-region maps to training image region(s) of training image(s) by a 2D translation and/or a 2D scaling, each training image associated with a reference camera pose, each query-region associated with a center point and a computed weighted mask that weights the query-region pixels according to computed correlations with the corresponding training image region, mapping cloud points corresponding to pixels of matched training image region(s) to corresponding images pixels of the matched query-regions according to a statistically significant correlation requirement between the center point of the query-region and the matched training image region, and according to the computed weight mask, and computing the camera pose according to an aggregation of the camera poses, and the mapped cloud points and corresponding image pixels of the matched query-regions.

Systems and methods for determining a camera pose of an image

There is provided a method of identifying objects in an image, comprising: extracting query descriptors from the image, comparing each query descriptor with training descriptors for identifying matching training descriptors, each training descriptor is associated with a reference object identifier and with relative location data (distance and direction from a center point of a reference object indicated by the reference object identifier), computing object-regions of the digital image by clustering the query descriptors having common center points defined by the matching training descriptors, each object-region approximately bounding one target object and associated with a center point and a scale relative to a reference object size, wherein the object-regions are computed independently of the identifier of the reference object associated with the object-regions, wherein members of each cluster point toward a common center point, and classifying the target object of each object-region according to the reference object identifier of the cluster.

Systems and methods for identifying a target object in an image

A system for identifying specific instances of objects in a three-dimensional (3D) scene, comprising: a camera for capturing an image of multiple objects at a site; at least one processor executable to: use a location and orientation of the camera to create a 3D model of the site including multiple instances of objects expected to be in proximity to the camera, and generate multiple candidate clusters each representing a different projection of the 3D model, detect at least two objects in the image, and determine a spatial configuration for each detected object; and match the detected image objects to one of the multiple candidate cluster using the spatial configurations, associate the detected objects with the expected object instances of the matched cluster, and retrieve information of one of the detected objects that is stored with the associated expected object instance; and a head-wearable display configured to display the information.

Object instance identification using three-dimensional spatial configuration

A method comprising: performing a first object recognition round on an image to detect at least a first object; matching the first detected object to a first reference object, thereby recognizing the first object; determining a chromatic adaptation transform between the first recognized object and the first reference object; applying the chromatic adaptation transform to the image; performing a second object recognition round on the chromatically adapted image to detect a second object that is different than the first recognized object; and matching the second detected object with a second reference object, thereby recognizing the second object.

Recognition-based color corrections

A computer implemented method, a computerized system and a computer program product for generating questions. The computer implemented method comprising obtaining a question, wherein the question comprises one or more elements that define an answer for the question. The method further comprising obtaining the answer. The method further comprises automatically generating, by a processor, a new question based on the question and the answer. The automatic generation comprises determining a variant of the one or more elements, wherein the variant defines the answer, wherein the new question comprises the variant.

Question generator based on elements of an existing question

A method of training an object identification system and identifying three dimensional objects using semantic segments includes receiving, into a non-volatile memory, an input file containing a geometric description of a three dimensional object having one or more semantic segments and one or more annotations for each of the one or more semantic segments, receiving, into the non-volatile memory one or more training images of the three dimensional object, identifying, through a processor, the one or more segments in the one or more training images, computing, through a training module, one or more descriptors to the one or more segments, and generating an output file representing a machine vision of the three dimensional object.

System, method and computer program product for training a three dimensional object indentification system and identifying three dimensional objects using semantic segments

Machines, systems and methods for character recognition disambiguation are provided. The method comprises selecting a first set of characters that match a first visual profile based on results of a character recognition process applied to target content; selecting a subset of the first set based on criteria associated with at least one of confidence level with which characters grouped in the subset are recognized or fragmentation associated with the characters grouped in the subset; and disambiguating recognition results for the characters grouped in the subset by displaying the characters along with context information, wherein reviewing two or more of the characters on a display screen along with context information associated with said two or more characters allows a human operator to select one or more suspect characters from among the two or more characters.

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Nesher, Israel

Asaf Tzadok