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公开(公告)号：US10296793B2
公开(公告)日：2019-05-21
申请号：US15479408
申请日：2017-04-05
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Yang Gao , Eric Cosatto
Abstract: A method, a computer program product, and a system are provided for video based action recognition. The system includes a processor. One or more frames from one or more video sequences are received. A feature vector for each patch of the one or more frames is generated using a deep convolutional neural network. An attention factor for the feature vectors is generated based on a within-frame attention and a between-frame attention. A target action is identified using a multi-layer deep long short-term memory process applied to the attention factor, said target action representing at least one of the one or more video sequences. An operation of a processor-based machine is controlled to change a state of the processor-based machine, responsive to the at least one of the one or more video sequences including the identified target action.
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公开(公告)号：US20180307967A1
公开(公告)日：2018-10-25
申请号：US15785796
申请日：2017-10-17
Applicant: NEC Laboratories America, Inc.
Inventor： Hans Peter Graf , Eric Cosatto , Iain Melvin
CPC classification number: G06N3/04 , G01S7/417 , G01S13/931 , G06N3/08
Abstract: A computer-implemented method executed by a processor for training a neural network to recognize driving scenes from sensor data received from vehicle radar is presented. The computer-implemented method includes extracting substructures from the sensor data received from the vehicle radar to define a graph having a plurality of nodes and a plurality of edges, constructing a neural network for each extracted substructure, combining the outputs of each of the constructed neural networks for each of the plurality of edges into a single vector describing a driving scene of a vehicle, and classifying the single vector into a set of one or more dangerous situations involving the vehicle.
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公开(公告)号：US20180081053A1
公开(公告)日：2018-03-22
申请号：US15689656
申请日：2017-08-29
Applicant: NEC Laboratories America, Inc.
Inventor： Iain Melvin , Eric Cosatto , Igor Durdanovic , Hans Peter Graf
CPC classification number: G01S13/931 , B60G2400/823 , B60Q9/008 , B60R1/00 , B60R2300/301 , B60R2300/8093 , B60W30/09 , B60W2420/42 , B60W2420/52 , G01S7/20 , G01S7/2955 , G01S7/417 , G01S13/867 , G01S17/936 , G01S2013/936 , G01S2013/9367 , G01S2013/9375 , G06K9/00805 , G06K9/46 , G06K9/6215 , G06K9/6232 , G06N3/0454 , G06N3/08 , G06N3/084
Abstract: A computer-implemented method and system are provided. The system includes an image capture device configured to capture image data relative to an ambient environment of a user. The system further includes a processor configured to detect and localize objects, in a real-world map space, from the image data using a trainable object localization Convolutional Neural Network (CNN). The CNN is trained to detect and localize the objects from image and radar pairs that include the image data and radar data for different scenes of a natural environment. The processor is further configured to perform a user-perceptible action responsive to a detection and a localization of an object in an intended path of the user.
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公开(公告)号：US20170294091A1
公开(公告)日：2017-10-12
申请号：US15479430
申请日：2017-04-05
Applicant: NEC Laboratories America, Inc.
Inventor： Renqiang Min , Yang Gao , Eric Cosatto
CPC classification number: G06K9/00711 , G06K9/4628 , G06K9/6274 , G06K2009/00738 , G06N3/0427 , G06N3/0445 , G06N3/0454 , G06N3/08 , G06N5/045 , G08B13/19613 , G08B13/19695 , H04N5/77 , H04N7/183
Abstract: A video monitoring system and method are provided. The video monitoring system includes a camera. The camera is positioned to monitor an area and capture live video to provide a live video stream. The video monitoring system also includes a security processing system. The security processing system includes a processor and memory coupled to the processor. The security processing system is programmed to detect and identify a target action sequence in the live video stream using a multi-layer deep long short-term memory process on are attention factor that is based on an within-frame attention and an between-frame attention. The security processing system is further programmed to trigger an action to alert that a target action sequence has been detected.
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公开(公告)号：US20160300111A1
公开(公告)日：2016-10-13
申请号：US15088530
申请日：2016-04-01
Applicant: NEC Laboratories America, Inc.
Inventor： Eric Cosatto
CPC classification number: G06K9/00718 , G06K9/00771 , G06K9/6254
Abstract: Systems and methods are disclosed for computer vision and object detection by extracting tracks of moving objects on a set of video sequences; selecting a subset of tracks for training; rendering a composite of each selected track into a single image; labeling tracks using the rendered images; training a track classifier by supervised machine learning using the labeled tracks; applying the trained track classifier to the remainder of the tracks; and selecting tracks classified with a low confidence by the classifier.
Abstract translation: 通过提取一组视频序列上的移动物体的轨迹，公开了用于计算机视觉和物体检测的系统和方法; 选择训练的轨道子集; 将每个所选轨道的复合渲染成单个图像; 使用渲染图像标记轨迹; 通过使用标记轨迹的监督机器学习训练轨道分类器; 将经训练的轨道分类器应用于轨道的其余部分; 并选择由分类器以低置信度分类的轨道。
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Patent Agency Ranking
公开(公告)号：US20240354953A1
公开(公告)日：2024-10-24
申请号：US18616983
申请日：2024-03-26
Applicant: NEC Laboratories America, Inc.
Inventor： Eric Cosatto
CPC classification number: G06T7/0014 , G01N1/30 , G06T2207/20081 , G06T2207/20084
Abstract: Methods and systems for training a model include performing color deconvolution on a set of training images, stained according to a first staining process, to generate channels that correspond to dyes used in the first staining process and dyes used in a second staining process. A channel is selected corresponds to a dye used in the second staining process. A machine learning model is trained, using the selected channel of the set of training images, to function with images stained according to the first staining process and images stained according to the second staining process.
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公开(公告)号：US20220319156A1
公开(公告)日：2022-10-06
申请号：US17711667
申请日：2022-04-01
Applicant: NEC Laboratories America, Inc.
Inventor： Alexandru Niculescu-Mizil , Eric Cosatto
IPC: G06V10/774 , G06V10/778 , G06V10/82
Abstract: Systems and methods for labelling data is provided. The method includes receiving data at a detector, and identifying a set of objects and features in the data using a neural network. The method further includes annotating the data based on the identified set of objects and features, and receiving a query from a user. The method further includes transforming the query into a representation that can be processed by a symbolic engine, and receiving the annotated data and a transformed query at the symbolic engine. The method further includes matching the transformed query with the annotated data, and presenting the annotated data that matches the transformed query to the user in a labelling interface. The method further includes applying new labels received from the user for the annotated data that matches the transformed query, recursively utilizing the newly annotated data to refine the detector.
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公开(公告)号：US20220319002A1
公开(公告)日：2022-10-06
申请号：US17711475
申请日：2022-04-01
Applicant: NEC Laboratories America, Inc.
Inventor： Eric Cosatto
Abstract: Methods and systems for processing a scanned tissue section include locating cells within a scanned tissue. Cells in the scanned tissue are classified using a classifier model. A tumor-cell ratio (TCR) map is generated based on classified normal cells and tumor cells. A TCR isoline is generated for a target TCR value using the TCR map, marking areas of the tissue section where a TCR is at or above the target TCR value. Dissection is performed on the tissue sample to isolate an area identified by the isoline.
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公开(公告)号：US20220028068A1
公开(公告)日：2022-01-27
申请号：US17380207
申请日：2021-07-20
Applicant: NEC Laboratories America, Inc.
Inventor： Eric Cosatto , Kyle Gerard
Abstract: Methods and systems for training a machine learning model include generating pairs of training pixel patches from a dataset of training images, each pair including a first patch representing a part of a respective training image, and a second patch, centered at the same location as the first, representing a larger part of the training image, being resized to a same size of as the first patch. A detection model is trained using the first pixel patches, to detect and locate cells in the images. A classification model is trained using the first pixel patches, to classify cells according to whether the detected cells are cancerous, based on cell location information generated by the detection model. A segmentation model is trained using the second pixel patches, to locate and classify cancerous arrangements of cells in the images.
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公开(公告)号：US11087452B2
公开(公告)日：2021-08-10
申请号：US16248897
申请日：2019-01-16
Applicant: NEC Laboratories America, Inc.
Inventor： Alexandru Niculescu-Mizil , Renqiang Min , Eric Cosatto , Farley Lai , Hans Peter Graf , Xavier Fontaine
Abstract: A false alarm reduction system and method are provided for reducing false alarms in an automatic defect detection system. The false alarm reduction system includes a defect detection system, generating a list of image boxes marking detected potential defects in an input image. The false alarm reduction system further includes a feature extractor, transforming each of the image boxes in the list into a respective set of numerical features. The false alarm reduction system also includes a classifier, computing as a classification outcome for the each of the image boxes whether the detected potential defect is a true defect or a false alarm responsive to the respective set of numerical features for each of the image boxes.
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