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    Hybrid Indexing with Grouplets
    11.
    发明申请
    Hybrid Indexing with Grouplets 审中-公开
    使用Grouplets进行混合索引

    公开(公告)号：US20150254280A1

    公开(公告)日：2015-09-10

    申请号：US14628286

    申请日：2015-02-22

    Applicant: NEC Laboratories America, Inc.

    Inventor： Xiaoyu Wang ,  Yuanqing Lin ,  Qi Tian

    IPC: G06F17/30

    CPC classification number: G06F16/24578 ,  G06F16/532 ,  G06F16/583

    Abstract: Systems and methods are disclosed to respond to a query for one or more images by using a processor, applying an indexing strategy which processes images as grouplets rather than individual single images; generating a two layer indexing structure with a group layer, each associated with one or more images in an image layer; cross-indexing the images into two or more groups; and retrieving near duplicate images with the cross-indexed images and the grouplets.

    Abstract translation: 公开了系统和方法以通过使用处理器来响应对一个或多个图像的查询，应用将图像处理为小图而不是单个单个图像的索引策略; 生成具有组层的两层索引结构，每组与图像层中的一个或多个图像相关联; 将图像交叉索引到两个或更多个组中; 并使用交叉索引的图像和小组检索近似重复的图像。

    Regionlets with Shift Invariant Neural Patterns for Object Detection
    12.
    发明申请
    Regionlets with Shift Invariant Neural Patterns for Object Detection 有权
    具有移位不变神经模式的区域对象检测

    公开(公告)号：US20150117760A1

    公开(公告)日：2015-04-30

    申请号：US14517211

    申请日：2014-10-17

    Applicant: NEC Laboratories America, Inc.

    Inventor： Xiaoyu Wang ,  Yuanqing Lin ,  Will Zou ,  Miao Sun

    IPC: G06K9/66

    CPC classification number: G06K9/66 ,  G06K9/4628

    Abstract: Systems and methods are disclosed for detecting an object in an image by determining convolutional neural network responses on the image; mapping the responses back to their spatial locations in the image; and constructing features densely extract shift invariant activations of a convolutional neural network to produce dense features for the image.

    Abstract translation: 公开了通过确定图像上的卷积神经网络响应来检测图像中的对象的系统和方法; 将响应映射回图像中的空间位置; 并且构造特征密集地提取卷积神经网络的移位不变激活以产生图像的密集特征。

    Semantic-Aware Co-Indexing for Near-Duplicate Image Retrieval
    13.
    发明申请
    Semantic-Aware Co-Indexing for Near-Duplicate Image Retrieval 有权
    近似重复图像检索的语义意识共同索引

    公开(公告)号：US20140133759A1

    公开(公告)日：2014-05-15

    申请号：US14077424

    申请日：2013-11-12

    Applicant: NEC Laboratories America, Inc.

    Inventor： Ming Yang ,  Xiaoyu Wang ,  Yuanqing Lin ,  Qi Tian

    IPC: G06F17/30

    CPC classification number: G06F17/30247

    Abstract: An image retrieval method includes learning multiple object category classifiers with a processor offline and generating classifications scores of images as the semantic attributes; performing vocabulary tree based image retrieval using local features with semantic-aware co-indexing to jointly embed two distinct cues offline for near-duplicate image retrieval; and identifying top similar or dissimilar images using multiple semantic attributes.

    Abstract translation: 图像检索方法包括：处理器离线学习多个对象类别分类器，并生成分类图像分数作为语义属性; 使用具有语义感知共同索引的局部特征来执行基于词汇树的图像检索，以共同嵌入两个不同的线索以进行近似重复的图像检索; 并使用多个语义属性来识别顶部相似或不相似的图像。

    Semantic Dense 3D Reconstruction
    14.
    发明申请
    Semantic Dense 3D Reconstruction 有权
    语义密集3D重建

    公开(公告)号：US20140132604A1

    公开(公告)日：2014-05-15

    申请号：US14073726

    申请日：2013-11-06

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yingze Bao ,  Manmohan Chandraker ,  Yuanqing Lin ,  Silvio Savarese

    IPC: G06T17/00

    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。

    Semantic dense 3D reconstruction
    15.
    发明授权
    Semantic dense 3D reconstruction 有权
    语义密集3D重建

    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

    IPC: G06T17/00 ,  G06T3/00 ,  G06T7/00

    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。

    Efficient distance metric learning for fine-grained visual categorization
    16.
    发明授权
    Efficient distance metric learning for fine-grained visual categorization 有权
    高效的距离度量学习，用于细粒度视觉分类

    公开(公告)号：US09471847B2

    公开(公告)日：2016-10-18

    申请号：US14524441

    申请日：2014-10-27

    Applicant: NEC Laboratories America, Inc.

    Inventor： Shenghuo Zhu ,  Yuanqing Lin ,  Qi Qian

    IPC: G06K9/62

    CPC classification number: G06K9/6201 ,  G06K9/6232 ,  G06K9/6251

    Abstract: Methods and systems for distance metric learning include generating two random projection matrices of a dataset from a d-dimensional space into an m-dimensional sub-space, where m is smaller than d. An optimization problem is solved in the m-dimensional subspace to learn a distance metric based on the random projection matrices. The distance metric is recovered in the d-dimensional space.

    Abstract translation: 用于距离度量学习的方法和系统包括从d维空间向m维子空间生成数据集的两个随机投影矩阵，其中m小于d。 在m维子空间中解决了优化问题，以便基于随机投影矩阵来学习距离度量。 距离度量在d维空间中被恢复。

    Hyper-class Augmented and Regularized Deep Learning for Fine-grained Image Classification
    17.
    发明申请
    Hyper-class Augmented and Regularized Deep Learning for Fine-grained Image Classification 审中-公开
    超级增强和规范深度学习细粒度图像分类

    公开(公告)号：US20160140438A1

    公开(公告)日：2016-05-19

    申请号：US14884600

    申请日：2015-10-15

    Applicant: NEC Laboratories America, Inc.

    Inventor： Tianbao Yang ,  Xiaoyu Wang ,  Yuanqing Lin ,  Saining Xie

    IPC: G06N3/08

    CPC classification number: G06N3/08 ,  G06N3/0454 ,  G06N3/084

    Abstract: Systems and methods are disclosed for training a learning machine by augmenting data from fine-grained image recognition with labeled data annotated by one or more hyper-classes, performing multi-task deep learning; allowing fine-grained classification and hyper-class classification to share and learn the same feature layers; and applying regularization in the multi-task deep learning to exploit one or more relationships between the fine-grained classes and the hyper-classes.

    Abstract translation: 公开了用于训练学习机器的系统和方法，通过用由一个或多个超类注释的标记数据增强来自细粒度图像识别的数据，执行多任务深度学习; 允许细粒度分类和超类分类来共享和学习相同的特征层; 并在多任务深度学习中应用正则化，以利用细粒度类与超类之间的一个或多个关系。

    Moving Object Localization in 3D Using a Single Camera
    20.
    发明申请
    Moving Object Localization in 3D Using a Single Camera 有权
    使用单个相机在3D中移动对象本地化

    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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