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公开(公告)号：US20180025242A1
公开(公告)日：2018-01-25
申请号：US15637465
申请日：2017-06-29
Applicant: NEC Laboratories America, Inc. , NEC Hong Kong Limited
Inventor： Manmohan Chandraker , Xiang Yu , Eric Lau , Elsa Wong
CPC classification number: G06F21/32 , G06F21/6218 , G06F2221/2133 , G06K9/00221 , G06K9/00228 , G06K9/00255 , G06K9/00281 , G06K9/00288 , G06K9/00624 , G06K9/00791 , G06K9/00906 , G06K9/4652 , G06K9/66 , G06N99/005 , G07C9/00158 , G07C9/00166 , H04L63/0861 , H04L63/1483
Abstract: A facility access control system and corresponding method are provided. The facility access control system includes a camera configured to capture an input image of a subject attempting to enter or exit a restricted facility. The facility access control system further includes a memory storing a deep learning model configured to perform multi-task learning for a pair of tasks including a liveness detection task and a face recognition task. The facility access control system also includes a processor configured to apply the deep learning model to the input image to recognize an identity of the subject in the input image regarding being authorized for access to the facility and a liveness of the subject. The liveness detection task is configured to evaluate a plurality of different distracter modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task.
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公开(公告)号：US09489768B2
公开(公告)日：2016-11-08
申请号：US14073726
申请日：2013-11-06
Applicant: NEC Laboratories America, Inc.
Inventor： Yingze Bao , Manmohan Chandraker , Yuanqing Lin , Silvio Savarese
CPC classification number: G06T17/00 , G06T3/00 , G06T7/579 , G06T2207/20076 , G06T2207/20081
Abstract: A method to reconstruct 3D model of an object includes receiving with a processor a set of training data including images of the object from various viewpoints; learning a prior comprised of a mean shape describing a commonality of shapes across a category and a set of weighted anchor points encoding similarities between instances in appearance and spatial consistency; matching anchor points across instances to enable learning a mean shape for the category; and modeling the shape of an object instance as a warped version of a category mean, along with instance-specific details.
Abstract translation: 一种重建对象的3D模型的方法包括：利用处理器从各种视点接收包括对象的图像的一组训练数据; 学习一个先前的包括一个描述一个类别的形状的共同性的平均形状，以及编码外观和空间一致性之间的实例之间的相似性的一组加权锚点; 在实例之间匹配锚点，以便学习类别的平均形状; 并将对象实例的形状建模为类别的翘曲版本，以及实例特定的细节。
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公开(公告)号：US09336601B2
公开(公告)日：2016-05-10
申请号：US14528704
申请日：2014-10-30
Applicant: NEC Laboratories America, Inc.
Inventor： Manmohan Chandraker
CPC classification number: G06T7/0055 , G06T7/514 , G06T7/55 , G06T2207/10021
Abstract: A computer vision method that includes deriving a relationship of spatial and temporal image derivatives of an object to bidirectional reflectance distribution function (BRDF) derivatives under camera motion, and deriving with a processor a quasilinear partial differential equation for solving surfaced depth for orthographic projections using the relationship of spatial and temporal image derivatives without requiring knowledge of the BRDF. The method may further recover surface depth for an object with unknown BRDF under perspective projection.
Abstract translation: 一种计算机视觉方法，其包括在摄像机运动下导出对象的空间和时间图像导数与双向反射分布函数（BRDF）导数的关系，以及使用处理器推导出准线性偏微分方程，以使用 空间和时间图像衍生物的关系，而不需要BRDF的知识。 该方法可以进一步恢复具有未知BRDF的物体在透视投影下的表面深度。
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公开(公告)号：US09148650B2
公开(公告)日：2015-09-29
申请号：US13858040
申请日：2013-04-06
Applicant: NEC Laboratories America, Inc.
Inventor： Manmohan Chandraker , Shiyu Song
CPC classification number: H04N13/204 , G06T7/579 , G06T7/74 , G06T2207/30244 , G06T2207/30252
Abstract: Systems and methods are disclosed for multithreaded visual odometry by acquired with a single camera on-board a vehicle; using 2D-3D correspondences for continuous pose estimation; and combining the pose estimation with 2D-2D epipolar search to replenish 3D points.
Abstract translation: 公开了通过用车上单个摄像机采集的用于多线程视觉测距的系统和方法; 使用2D-3D对应连续姿态估计; 并将姿态估计与2D-2D对极搜索相结合，以补充3D点。
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公开(公告)号：US09070202B2
公开(公告)日：2015-06-30
申请号：US14184766
申请日：2014-02-20
Applicant: NEC Laboratories America, Inc.
Inventor： Manmohan Chandraker , Shiyu Song , Yuanqing Lin , Xiaoyu Wang
CPC classification number: G06T7/2033 , G06K9/00805 , G06T7/246 , G06T7/277 , G06T7/579 , G06T7/73 , G06T2207/10016 , G06T2207/20076 , G06T2207/20081 , G06T2207/30252
Abstract: Systems and methods are disclosed for autonomous driving with only a single camera by moving object localization in 3D with a real-time framework that harnesses object detection and monocular structure from motion (SFM) through the ground plane estimation; tracking feature points on moving cars a real-time framework to and use the feature points for 3D orientation estimation; and correcting scale drift with ground plane estimation that combines cues from sparse features and dense stereo visual data.
Abstract translation: 公开的系统和方法仅用单个摄像机进行自主驾驶，通过利用来自运动（SFM）的对象检测和单目结构通过接地平面估计的实时框架来移动3D物体定位; 跟踪移动汽车上的特征点实时框架并使用特征点进行3D定位估计; 并且通过地面平面估计来校正尺度漂移，其结合来自稀疏特征和密集立体视觉数据的线索。
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Patent Agency Ranking
公开(公告)号：US20150117758A1
公开(公告)日：2015-04-30
申请号：US14528704
申请日：2014-10-30
Applicant: NEC Laboratories America, Inc.
Inventor： Manmohan Chandraker
IPC: G06T7/00
CPC classification number: G06T7/0055 , G06T7/514 , G06T7/55 , G06T2207/10021
Abstract: A computer vision method that includes deriving a relationship of spatial and temporal image derivatives of an object to bidirectional reflectance distribution function (BRDF) derivatives under camera motion, and deriving with a processor a quasilinear partial differential equation for solving surfaced depth for orthographic projections using the relationship of spatial and temporal image derivatives without requiring knowledge of the BRDF. The method may further recover surface depth for an object with unknown BRDF under perspective projection.
Abstract translation: 一种计算机视觉方法，其包括在摄像机运动下导出对象的空间和时间图像导数与双向反射分布函数（BRDF）导数的关系，以及使用处理器推导出准线性偏微分方程，以使用 空间和时间图像衍生物的关系，而不需要BRDF的知识。 该方法可以进一步恢复具有未知BRDF的物体在透视投影下的表面深度。
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公开(公告)号：US20140270484A1
公开(公告)日：2014-09-18
申请号：US14184766
申请日：2014-02-20
Applicant: NEC Laboratories America, Inc.
Inventor： Manmohan Chandraker , Shiyu Song , Yuanqing Lin , Xiaoyu Wang
IPC: G06K9/00
CPC classification number: G06T7/2033 , G06K9/00805 , G06T7/246 , G06T7/277 , G06T7/579 , G06T7/73 , G06T2207/10016 , G06T2207/20076 , G06T2207/20081 , G06T2207/30252
Abstract: Systems and methods are disclosed for autonomous driving with only a single camera by moving object localization in 3D with a real-time framework that harnesses object detection and monocular structure from motion (SFM) through the ground plane estimation; tracking feature points on moving cars a real-time framework to and use the feature points for 3D orientation estimation; and correcting scale drift with ground plane estimation that combines cues from sparse features and dense stereo visual data.
Abstract translation: 公开的系统和方法仅用单个摄像机进行自主驾驶，通过利用来自运动（SFM）的对象检测和单目结构通过接地平面估计的实时框架来移动3D物体定位; 跟踪移动汽车上的特征点实时框架并使用特征点进行3D定位估计; 并且通过地面平面估计来校正尺度漂移，其结合来自稀疏特征和密集立体视觉数据的线索。
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公开(公告)号：US20250118044A1
公开(公告)日：2025-04-10
申请号：US18891590
申请日：2024-09-20
Applicant: NEC Laboratories America, Inc.
Inventor： Jong-Chyi Su , Samuel Schulter , Sparsh Garg , Manmohan Chandraker , Mingfu Liang
Abstract: Systems and methods for identifying novel objects in an image include detecting one or more objects in an image and generating one or more captions for the image. One or more predicted categories of the one or more objects detected in the image and the one or more captions are matched to identify, from the one or more predicted categories, a category of a novel object in the image. An image feature and a text description feature are generated using a description of the novel object. A relevant image is selected using a similarity score between the image feature and the text description feature. A model is updated using the relevant image and associated description of the novel object.
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公开(公告)号：US20240354921A1
公开(公告)日：2024-10-24
申请号：US18616396
申请日：2024-03-26
Applicant: NEC Laboratories America, Inc.
Inventor： Sparsh Garg , Bingbing Zhuang , Samuel Schulter , Manmohan Chandraker
CPC classification number: G06T7/0002 , G06T7/10 , G06T7/50 , G06V20/588 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084 , G06T2207/30256
Abstract: Systems and methods for road defect level prediction. A depth map is obtained from an image dataset received from input peripherals by employing a vision transformer model. A plurality of semantic maps is obtained from the image dataset by employing a semantic segmentation model to give pixel-wise segmentation results of road scenes to detect road pixels. Regions of interest (ROI) are detected by utilizing the road pixels. Road defect levels are predicted by fitting the ROI and the depth map into a road surface model to generate road points classified into road defect levels. The predicted road defect levels are visualized on a road map.
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公开(公告)号：US11947626B2
公开(公告)日：2024-04-02
申请号：US17519950
申请日：2021-11-05
Applicant: NEC Laboratories America, Inc.
Inventor： Masoud Faraki , Xiang Yu , Yi-Hsuan Tsai , Yumin Suh , Manmohan Chandraker
IPC: G06F18/214 , G06N3/04 , G06V40/16
CPC classification number: G06F18/214 , G06N3/04 , G06V40/161
Abstract: A method for improving face recognition from unseen domains by learning semantically meaningful representations is presented. The method includes obtaining face images with associated identities from a plurality of datasets, randomly selecting two datasets of the plurality of datasets to train a model, sampling batch face images and their corresponding labels, sampling triplet samples including one anchor face image, a sample face image from a same identity, and a sample face image from a different identity than that of the one anchor face image, performing a forward pass by using the samples of the selected two datasets, finding representations of the face images by using a backbone convolutional neural network (CNN), generating covariances from the representations of the face images and the backbone CNN, the covariances made in different spaces by using positive pairs and negative pairs, and employing the covariances to compute a cross-domain similarity loss function.
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