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公开(公告)号：WO2022216585A1
公开(公告)日：2022-10-13
申请号：PCT/US2022/023266
申请日：2022-04-04
Applicant: NEC LABORATORIES AMERICA, INC.
Inventor： COSATTO, Eric
Abstract: Methods and systems for processing a scanned tissue section include locating (210) cells within a scanned tissue. Cells in the scanned tissue are classified (214) using a classifier model. A tumor-cell ratio (TCR) map is generated (508) based on classified normal cells and tumor cells. A TCR isoline is generated (510) 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 (311) is performed on the tissue sample to isolate an area identified by the isoline.
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公开(公告)号：WO2017177008A1
公开(公告)日：2017-10-12
申请号：PCT/US2017/026365
申请日：2017-04-06
Applicant: NEC LABORATORIES AMERICA, INC
Inventor： COSATTO, Eric , MELVIN, Iain , GRAF, Hans Peter
IPC: B60W30/095 , B60W50/14 , B60W40/04
CPC classification number: G06K9/00805 , B60Q9/008 , G06K9/00201 , G06K9/46 , G06K9/4628 , G06K9/627 , G06N3/0454 , G06N3/08
Abstract: A video device for predicting driving situations while a person drives a car is presented. The video device includes multi-modal sensors and knowledge data for extracting feature maps, a deep neural network trained with training data to recognize real-time traffic scenes (TSs) from a viewpoint of the car, and a user interface (UI) for displaying the real-time TSs. The real-time TSs are compared to predetermined TSs to predict the driving situations. The video device can be a video camera. The video camera can be mounted to a windshield of the car. Alternatively, the video camera can be incorporated into the dashboard or console area of the car. The video camera can calculate speed, velocity, type, and/or position information related to other cars within the real-time TS. The video camera can also include warning indicators, such as light emitting diodes (LEDs) that emit different colors for the different driving situations.
Abstract translation: 提出了一种用于预测人在驾驶汽车时的驾驶状况的视频装置。 视频设备包括用于提取特征地图的多模态传感器和知识数据，利用训练数据训练的深度神经网络，以从汽车的角度识别实时交通场景（TS），以及用于显示的用户界面（UI） 实时TS。 实时TS与预定的TS进行比较以预测驾驶情况。 视频设备可以是摄像机。 摄像机可以安装在汽车的挡风玻璃上。 或者，摄像机可以集成到汽车的仪表板或控制台区域。 摄像机可以在实时TS内计算与其他车辆有关的速度，速度，类型和/或位置信息。 摄像机还可以包括警告指示器，例如发光二极管（LED），它们针对不同的驾驶情况发出不同的颜色。
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公开(公告)号：WO2017177005A1
公开(公告)日：2017-10-12
申请号：PCT/US2017/026362
申请日：2017-04-06
Applicant: NEC LABORATORIES AMERICA, INC
Inventor： COSATTO, Eric , MELVIN, Iain , GRAF, Hans Peter
IPC: G08G1/01 , G08G1/0965 , G08G1/0969 , G06N99/00
CPC classification number: G06K9/00805 , B60Q9/008 , G06K9/00201 , G06K9/46 , G06K9/4628 , G06K9/627 , G06N3/0454 , G06N3/08
Abstract: A computer-implemented method for training a deep neural network to recognize traffic scenes (TSs) from multi-modal sensors and knowledge data is presented. The computer-implemented method includes receiving data from the multi-modal sensors and the knowledge data and extracting feature maps from the multi-modal sensors and the knowledge data by using a traffic participant (TS) extractor to generate a first set of data, using a static objects extractor to generate a second set of data, and using an additional information extractor. The computer-implemented method further includes training the deep neural network, with training data, to recognize the TSs from a viewpoint of a vehicle.
Abstract translation: 提出了一种用于训练深度神经网络以识别来自多模式传感器和知识数据的交通场景（TS）的计算机实现的方法。 该计算机实现的方法包括通过使用流量参与者（TS）提取器从多模式传感器和知识数据接收数据并且从多模式传感器和知识数据中提取特征映射以生成第一组数据 一个静态对象提取器来生成第二组数据，并使用一个附加信息提取器。 计算机实现的方法还包括训练具有训练数据的深度神经网络以从车辆的角度识别TS。
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公开(公告)号：WO2022140487A1
公开(公告)日：2022-06-30
申请号：PCT/US2021/064755
申请日：2021-12-21
Applicant: NEC LABORATORIES AMERICA, INC.
Inventor： HAN, Shaobo , HUANG, Ming-Fang , COSATTO, Eric
Abstract: Aspects of the present disclosure describe an unsupervised context encoder-based fiber sensing method that detects anomalous vibrations proximate to a sensor fiber that is part of a distributed fiber optic sensing system (DFOS) such that damage to the sensor fiber by activities producing and anomalous vibrations are preventable. Advantageously, our method requires only normal data streams and a machine learning based operation is utilized to analyze the sensing data and report abnormal events related to construction or other fiber-threatening activities in real-time. Our machine learning algorithm is based on waterfall image inpainting by context encoder and is self-trained in an end-to-end manner and extended every time the DFOS sensor fiber is optically connected to a new route. Accordingly, our inventive method and system it is much easier to deploy as compared to supervised methods of the prior art
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公开(公告)号：WO2021207096A1
公开(公告)日：2021-10-14
申请号：PCT/US2021/025815
申请日：2021-04-05
Applicant: NEC LABORATORIES AMERICA, INC.
Inventor： JI, Philip , COSATTO, Eric , WANG, Ting
Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously provide traffic monitoring, and traffic management which improves the safety and efficiency of a roadway.
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Patent Agency Ranking
公开(公告)号：WO2018200026A1
公开(公告)日：2018-11-01
申请号：PCT/US2017/057074
申请日：2017-10-18
Applicant: NEC LABORATORIES AMERICA, INC
Inventor： MELVIN, Iain , COSATTO, Eric , GRAF, Hans Peter
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：WO2018052714A3
公开(公告)日：2018-03-22
申请号：PCT/US2017/049327
申请日：2017-08-30
Applicant: NEC LABORATORIES AMERICA, INC.
Inventor： MALVIN, Iain , COSATTO, Eric , DURDANOVIC, Igor , GRAF, Hans, Peter
Abstract: A computer-implemented method and system are provided. The system includes an image capture device (510) configured to capture image data relative to an ambient environment of a user. The system further includes a processor (511) 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 (511) 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：WO2019133316A1
公开(公告)日：2019-07-04
申请号：PCT/US2018/065900
申请日：2018-12-17
Applicant: NEC LABORATORIES AMERICA, INC.
Inventor： NICULESCU-MIZIL, Alexandru , COSATTO, Eric , FONTAINE, Xavier
Abstract: Methods and systems for detecting and correcting anomalies include predicting normal behavior of a monitored system based on training data that includes only sensor data collected during normal behavior of the monitored system. The predicted normal behavior is compared to recent sensor data to determine that the monitored system is behaving abnormally. A corrective action is performed responsive to the abnormal behavior to correct the abnormal behavior.
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公开(公告)号：WO2018052714A2
公开(公告)日：2018-03-22
申请号：PCT/US2017/049327
申请日：2017-08-30
Applicant: NEC LABORATORIES AMERICA, INC.
Inventor： MALVIN, Iain , COSATTO, Eric , DURDANOVIC, Igor , GRAF, Hans, Peter
CPC classification number: G06K9/4628 , G06K9/00664 , G06K9/00791 , G06K9/6274 , G06K9/6289
Abstract: A computer-implemented method and system are provided. The system includes an image capture device (510) configured to capture image data relative to an ambient environment of a user. The system further includes a processor (511) 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 (511) 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.
Abstract translation: 提供了一种计算机实现的方法和系统。 该系统包括图像捕获设备（510），其被配置成捕获与用户的周围环境相关的图像数据。 该系统还包括处理器（511），其被配置为使用可训练对象定位卷积神经网络（CNN）从图像数据检测和定位真实世界地图空间中的对象。 美国有线电视新闻网的训练是检测和定位来自图像和雷达对的物体，其中包括自然环境不同场景的图像数据和雷达数据。 处理器（511）还被配置为响应于用户的预期路径中的对象的检测和定位来执行用户可感知的动作。
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10.发明申请MITOTIC FIGURE DETECTOR AND COUNTER SYSTEM AND METHOD FOR DETECTING AND COUNTING MITOTIC FIGURES 审中-公开MITOTIC图形检测器和计数器系统以及用于检测和计数的图形的方法
公开(公告)号：WO2010003041A2
公开(公告)日：2010-01-07
申请号：PCT/US2009/049484
申请日：2009-07-02
Applicant: NEC LABORATORIES AMERICA, INC.
Inventor： COSATTO, Eric , BURGER, Harold, Christopher , MILLER, Matthew, L.
CPC classification number: G06K9/00147
Abstract: A method and system for detecting and counting mitotic figures in an image of a biopsy sample stained with at least one dye, includes color filtering the image in a computer process to identify pixels in the image that have a color which is indicative a mitotic figure; extracting the mitotic pixels in the image that are connected to one another in a computer process, thereby producing blobs of mitotic pixels; shape-filtering and clustering the blobs of mitotic pixels in a computer process to produce mitotic figure candidates; extracting sub-images of mitotic figures by cropping the biopsy sample image at the location of the blobs; extracting two sets of features from the mitotic figure candidates in two separate computer processes; determining which of the mitotic figure candidates are mitotic figures in a computer classification process based on the extracted sets of features; and counting the number of mitotic figures per square unit of biopsy sample tissue.
Abstract translation: 一种用于检测和计数用至少一种染料染色的活组织检查样品的图像中的有丝分裂象的方法和系统，包括在计算机过程中对图像进行颜色过滤以识别图像中具有指示有丝分裂图的颜色的像素; 提取在计算机过程中彼此连接的图像中的有丝分裂像素，从而产生有丝分裂像素的斑点; 在计算机过程中形状过滤和聚集有丝分裂像素的斑点以产生有丝分裂的人物候选者; 通过在斑点的位置裁剪活检样本图像来提取有丝分裂图像的子图像; 在两个独立的计算机进程中从有丝分裂的人物中提取两组特征; 基于提取的特征集合确定计算机分类过程中的有丝分裂象候选者中的哪一个是有丝分裂图; 并计算每平方单位活检样本组织的有丝分裂数目。
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