Intel Corporation

Applied Materials Israel Limited

Innoviz Technologies Ltd.

Applied Materials, Inc.

Pointgrab Ltd.

Wanib

Ovadya Menadeva

Kfir Viente

Maoz Madmony

Julian Vlaiko

Nir Osiroff

Guy Zohar

Amir Day

Aviram Tam

Avigdor Dori Eldar

Omer Keilaf

Haim Perski

Amit Steinberg

Ronen Eshel

Oren Buskila

David Elooz

Oren Rosenzweig

Amir Kaplan

Youval Nehmadi

Maxim Schwartz

Zvika Rosenberg

Yair Alpern

Smadar David Raly

Oren Shamir

Itamar Glazer

Eran Eilat

Sergey Latinsky

Zamir Abraham

Itamar Gilad

Nadav Wertsman

Sergey Latinski

Avi Mandelberg

Orit Afek

Kris Roman

Gidi Gottlib

Liraz Gershtein

A system includes at least one processor configured to detect, based on point cloud information, portions of a particular object, and determine, based on the detected portions, at least a first portion having a first reflectivity corresponding to a license plate, and at least two additional spaced-apart portions corresponding to locations on the particular object other than a location of the first portion. The at least two additional portions have reflectivity substantially lower than the first reflectivity. The at least one processor is further configured to classify the particular object as a vehicle, based on a spatial relationship and a reflectivity relationship between the first portion and the at least two additional portions.

LIDAR systems and methods for detection and classification of objects

A vehicle-assistance system for classifying objects in a vehicle's surroundings. The system includes: at least one memory configured to store classification information for classifying a plurality of objects; and at least one processor configured to receive a plurality of detection results associated with light detection and ranging system (LIDAR) detection results, each detection result including location information, and further information indicative of at least two of the following detection characteristics: object surface reflectivity; temporal spreading of signal reflected from the object; object surface physical composition; ambient illumination measured at a LIDAR dead time; difference in detection information from a previous frame; and confidence level associated with another detection characteristic. The at least one processor is also configured to: access the classification information; and based on the classification information and the detection results, classify an object in the vehicle's surroundings.

A vehicle-assistance system for classifying objects in a vehicle's surroundings is provided. The system may include at least one memory configured to store classification information for classifying a plurality of objects and at least one processor configured to receive, on a pixel-by-pixel basis, a plurality of measurements associated with LIDAR detection results. The measurements may include at least one of: a presence indication, a surface angle, object surface physical composition, and a reflectivity level. The at least one processor may also be configured to receive, on the pixel-by-pixel basis, at least one confidence level associated with each received measurement, and access the classification information. The at least one processor may further be configured to, based on the classification information and the received measurements with the at least one associated confidence level plurality, identify a of pixels as being associated with a particular object.

Classifying objects with additional measurements

A system for hand gesture recognition includes a display, camera, memory, and processor. When the processor is to execute instructions, the processor is to estimate one or more motion vectors of an object using a pair of consecutive frames and estimate an average motion vector of the object. The processor may also estimate an average motion vector of the object, determine a first histogram of optical flow (HOOF) based on the one or more motion vectors and the average motion vector, determine depth values based on motion vectors from the first HOOF, and determine a second histogram of optical flow (HOOF) based on the depth values. The processor is also to obtain a descriptor based on histogram values from a histogram of optical flow (HOOF) of the one or more motion vectors, a shape descriptor, and the average motion vector and classify the descriptor as a gesture.

Hand gesture recognition for virtual reality and augmented reality devices

A system for hand gesture recognition is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to estimate one or more motion vectors of an object using a pair of consecutive frames and estimate an average motion vector of the object. The processor is also to obtain a descriptor based on histogram values from a histogram of optical flow (HOOF) of the one or more motion vectors and the average motion vector and classify the descriptor as a gesture.

Hand gesture recognition for cursor control

A system for a tracker for cursor navigation is described herein. The system includes a display, camera, memory, and processor. The memory that is to store instructions and is communicatively coupled to the camera and the display. The processor is communicatively coupled to the camera, the display, and the memory. When the processor is to execute the instructions, the processor is to extract an object mask of an object and execute an optical flow on good feature points from the object mask. The processor is also to estimate a movement of the object and render a cursor on the display based on the movement of the object.

Tracker for cursor navigation

An example apparatus for classifying target objects using three-dimensional geometric filtering includes a patch receiver to receive patches with objects to be classified. The apparatus also includes a geometric filter to filter out patches including objects with sizes outside a target range using three dimensional geometry to generate filtered patches. The apparatus further includes a background remover to remove background pixels from the filtered patches to generate preprocessed patches. The apparatus includes a classification score calculator to calculate a classification score for each of the preprocessed patches.

Target object classification using three-dimensional geometric filtering

A method, a system and a computer program product for evaluating an actual structural element of an electrical circuit. The method includes: detecting an actual structural element contour by processing a scanning electron microscope image of the actual structural element; aligning the actual structural element contour with a simulated contour to provide an aligned actual structural element contour; wherein the simulated contour is obtained by simulating a lithographic process that is responsive to a design contour; and comparing between the aligned actual structural element contour and reference information.

System, method and computer program product for evaluating an actual structural element of an electrical circuit

A system and method for computer vision based tracking of a hand may include receiving a sequence of images, the images including at least one object having a shape of a hand. A first selected feature is tracked from within the hand shaped object. Shape recognition algorithms are applied at a suspected location of the hand shaped object in an image from the sequence of images to detect a shape of a hand in the image and a second feature from within the detected shape of the hand is then selected and tracked, thereby providing verification and updating of the location of the hand shaped object.

Computer vision based tracking of a hand

A method for processing an image of a person, the method including: (i) defining a first search area in response to a value of a metric parameter and to a location of an element of interest within the image; (ii) generating an edge detection data structure, wherein some of the elements of the edge detection data structure are indicative of edges of the image which are located within an edge detection search area that is contained within the first search area; (iii) determining a contour path in the edge detection data structure in response to multiple edges of the edge detection data structure, wherein the contour path includes a single data structure element from each column of the data structure; and (iv) retrieving a face portion of the image, wherein the face portion is included within a mask that is responsive to the contour path.

Method, a system, and a computer program product for processing images

A system and method for determining a cross sectional feature of a measured structural element having a sub-micron cross section, the cross section is defined by an intermediate section that is located between a first and a second traverse sections. The method starts by a first step of scanning, at a first tilt state, a first portion of a reference structural element and at least the first traverse section of the measured structural element, to determine a first relationship between the reference structural element and the first traverse section. The first step is followed by a second step of scanning, at a second tilt state, a second portion of a reference structural element and at least the second traverse section of the measured structural element, to determine a second relationship between the reference structural element and the second traverse section. The method ends by a third step of determining a cross sectional feature of the measured structural element in response to the first and second relationships.

System and method for determining a cross sectional feature of a structural element using a reference structural element

A system software product and a method for evaluating a mask, the method including the stages of: defining multiple CD measurement target windows; defining multiple pattern recognition windows such that the multiple CD measurements windows do not overlap the multiple pattern recognition windows, wherein each CD measurement target window and an associated pattern recognition window are positioned within a measurement area that is scannable without introducing a substantial mechanical movement; performing multiple critical dimension measurements of multiple patterns of an object being manufactured by exposing the mask to radiation, wherein the performing comprises using at least one CD measurement target window and at least one pattern recognition window; and evaluating the mask.

System, method and computer software product for inspecting charged particle responsive resist

A method for analyzing an image includes identifying a curved segment of a contour that is associated with noise. The curved segment is smoothed so as to reduce the noise that is associated with the curved segment, thereby providing a smoothed segment. The smoothed segment is transformed to a natural coordinate system, thereby providing a transformed segment. A line is fitted to the transformed segment in order to determine a radius of curvature of the curved segment.

Measurement of corner roundness

A method for calibrating a metrology tool, the method includes: determining a relationship between an upper portion of a milled reference structural element and between a lower portion of the milled reference structural element; and defining a calibration parameter in response to the relationship. A metrology system that includes: a miller adapted to mill a reference structural element; and a measurement device adapted to determine a relationship between an upper portion of a milled reference structural element and between a lower portion of the milled reference structural element; wherein the metrology system is further adapted to define a calibration parameter in response to the relationship.

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Modiin, Israel

Ovadya Menadeva