Xerox Corporation

Conduent Business Services, LLC

United Technologies Corporation

Raytheon Technologies Corporation

Goodrich Corporation

Other

Carrier Corporation

Rtx Corporation

Xerox Corporartion

Edgar A Bernal

Robert P Loce

Wencheng Wu

Lalit Keshav Mestha

Qun Li

Orhan Bulan

Beilei Xu

Raja Bala

Matthew Adam Shreve

Shen-Ge Wang

Peter Paul

Michael C Mongeon

Aaron Michael Burry

Thomas F Wade

Jayant Kumar

Michael J Giering

Ozgur Erdinc

Kishore K Reddy

Zhigang Fan

Alan Matthew Finn

Yao Rong Wang

Graham Stephen Pennington

Richard W Osborne, Iii

Stuart Alan Schweid

Ziyou Xiong

Richard Leighton Howe

Daniel S Hann

Ryan B Noraas

Jose Miguel Pasini

Xuejin Wen

Paul Ronald Austin

Joseph A Sylvestro

Gene B Donskoy

Sergio S Frutuoso

Valerie J Raburn

Peter A Crean

Catalin G Fotache

Amit Surana

Xiaodong Yang

Himanshu Madhu

Yusuf Oguzhan Artan

Pramod Sankar Kompalli

Olusegun T Oshin

Eribaweimon Shilla

Matthew O Williams

Peter Johan Nystrom

Howard A Mizes

Edul N Dalal

Peter M Gulvin

Palghat Srinivas Ramesh

Ankit Tiwari

Gavan Leonard Tredoux

Peter J Zehler

Martin Edward Hoover

Michael Robert Furst

Saurabh Prabhat

Sriganesh Madhvanath

Abu Saeed Islam

Nagendra Somanath

David Preston Cummins

Premkumar Rajendran

Helen HaeKyung Shin

George Cunha Cardoso

Himanshu Jayantkumar Madhu

Jeyasri Subramanian

Kin Gwn Lore

Francesca Stramandinoli

Binu M Nair

Josephine Smith

Olusegun Oshin

William L Rall

Yun Zhang

David Chuang

Xitong Yang

Diana L Delibaltov

Yi Liu

A method for human-robot collaboration including: acquiring visual temporal data of a human partner to a robot; determining, using a generative module, predicted future visual temporal data in response to the visual temporal data, the visual temporal data including current visual temporal data and previous visual temporal data; and determining, using a discriminative module, a vector of probabilities indicating the likelihood that a future action of the human partner belongs to each class among a set of classes being considered in response to at least the future visual temporal data and the visual temporal data.

System and method for early event detection using generative and discriminative machine learning models

A method for robotic inspection of a part, includes the steps of: supporting the part with a robot mechanism; obtaining part-related sensor input with a sensor positioned to inspect the part supported by the robot mechanism; and controlling movement of the robot mechanism relative to the sensor, wherein the controlling is done by a feedback control unit which receives the sensor input, and the feedback control unit is configured to control the robot mechanism based upon the sensor input.

System and method for robotic inspection

A process for automated component inspection includes the steps of calibrating an imaging device mounted on a table; calibrating a coordinate measuring machine mounted on the table, the coordinate measuring machine comprising a fixture coupled to an arm of the coordinate measuring machine; coupling a component to the fixture; acquiring an image of said component with said imaging device; registering a baseline dimensioned image to the component image; applying the baseline dimensioned image to a damage detection algorithm; and determining component damage by the damage detection algorithm.

System and method for determining part damage

A method for detecting a double-parked vehicle includes identifying a parking region in video data received from an image capture device monitoring the parking region. The method includes defining an enforcement region at least partially surrounding the parking region. The method includes detecting a stationary vehicle in the enforcement region. The method includes determining the occurrence of an event relative to the stationary vehicle. In response to the determined occurrence of the event, the method includes classifying the stationary vehicle as being one of double parked and not double parked.

Video-based system for automated detection of double parking violations

A system processes a set of images to verify whether the images depict a type of property damage, such as hail damage from a hail storm. The system receives digital images of a roof or other facet of the property that were taken by an image capturing device after the storm event. The system also receives storm data relating to the storm event and claim data related to a property damage claim. The system uses the claim data to process the digital images to automatically identify damage to the roof or other facet. The system then analyzes the storm data to determine whether the damage is consistent with an actual, naturally-occurring storm event, and it generates a report depicting its findings.

Image analysis system for verification of property roof damage

A system segments a set of images of a property to identify a type of damage to the property. The system receives, from an image capturing device, a digital image of a roof or other feature of the property. The system processes the image to identify a set of segments, in which each segment corresponds to a piece of the feature, such as a tab or tooth of a shingle on the roof. The system saves a result of the processing to a data file as a segmented image of the property, and it uses the segmented image to identify a type of damage to the property.

Image segmentation system for verification of property roof damage

A multi-camera system for a component inspection comprising a table having a table top or, alternatively, another sufficiently rigid surface; a first camera having a narrow field-of-view lens; a second camera having a wide field-of-view lens linked to said first camera, wherein said first camera and said second camera are configured to move identical distances along a common axis relative to said table top or surface; and a pre-defined pattern defined on said table top or surface.

Multi-camera system for simultaneous registration and zoomed imagery

A method for nondestructive inspection of a component, the method includes determining a first pulse-echo scan from a first side of a component; determining a second pulse-echo scan from a second side of the component; determining a through-transmission scan based on the first pulse-echo scan, the second pulse-echo scan, and a model of the component, the model comprises a rigid internal structure of the component; and classifying the component based on comparing the through-transmission scan to a 'gold' model.

Nondestructive inspection using dual pulse-echo ultrasonics and method therefor

A system and method for performing vehicle-velocity aware image enhancement. Embodiments generally include a video capture module configured to receive image data of the scene being monitored, an image extraction module configured to extract still images from incoming video data, a vehicle detection module that detects the approximate location of a target vehicle in the scene being monitored, a velocity determination module configured to determine the amplitude and direction of a vector that describes the velocity of the target vehicle in image pixel coordinates, and a velocity-aware enhancing module configured to enhance the image(s) of the target vehicle extracted from the video feed based on the vehicle's velocity. Embodiments may also include an infraction detection module configured to detect the occurrence of a violation of traffic law(s) by a target vehicle.

System and method for enhancing images and video frames

A method of compressing data in the context of a decision-making task includes receiving raw data, analyzing the raw data to determine content of the raw data, and adjusting one or more one data compression parameters in a compression algorithm. The adjustment of the one or more compression parameters is based on the content of the raw data and a received decision-making task to produce a modified compression algorithm. The raw data is thereafter compressed using the modified compression algorithm and output as compressed data.

Systems and methods for compressing data

A material characterization system includes an imaging unit, a material characterization controller, and an imaging unit controller. The electronic imaging unit generates a test image of a specimen composed of a material. The electronic material characterization controller determines values of a plurality of parameters and maps the parameters to corresponding ground truth labeled outputs. The mapped parameters are applied to at least one test image to predict a presence of at least one target attribute of the specimen in response to applying the learned parameters. The test image is convert to a selected output image format so as to generate a synthetic image including the predicted at least one attribute. The electronic imaging unit controller performs a material characterization analysis that characterizes the material of the specimen based on the predicted at least one attribute included in the synthetic image.

Automated material characterization system including conditional generative adversarial networks

A system for assessing a property damage claim uses an imaging device to capture images of a property that has reportedly been damaged by an incident. The system receives a property damage incident type, along with image processing criteria for processing images that are associated with the incident type. The system automatically processes the images according to the image processing criteria to identify one or more characteristics in the images and, based on the identified characteristics, determines whether certain claim processing criteria are satisfied. The claim processing criteria may help the system determine whether the property actually was damaged by the reported incident. The system will generate a command to process the property damage claim only if the system determines that one or more images show that the claim processing criteria are satisfied.

Image analysis system for property damage assessment and verification

A method for nondestructive vibrothermography inspection of a component, the method includes generating ultrasonic excitations in a component over a range of frequencies; determining a thermal signature in the component from the excitations; registering a model with the thermal signature; determining damage based on the thermal signal and model; and classifying the component based on the determining.

Nondestructive inspection using thermoacoustic imagery and method therefor

An imaging method includes obtaining an image with a first field of view and first effective resolution and the analyzing the image with a visual attention algorithm to one identify one or more areas of interest in the first field of view. A subsequent image is then obtained for each area of interest with a second field of view and a second effective resolution, the second field of view being smaller than the first field of view and the second effective resolution being greater than the first effective resolution.

Active visual attention models for computer vision tasks

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Webster, NY, United States of America

Edgar A Bernal