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    Coat rack
    1.
    外观设计
    Coat rack 有权

    公开(公告)号：USD1043199S1

    公开(公告)日：2024-09-24

    申请号：US35519832

    申请日：2024-01-08

    Applicant: Jie Wang

    Designer： Jie Wang

    Abstract: 1. Coat rack
    1.1 : Perspective
    1.2 : Perspective
    1.3 : Front
    1.4 : Back
    1.5 : Left
    1.6 : Right
    1.7 : Enlarged top view
    1.8 : Enlarged bottom view
    The broken lines in the reproductions depict portions of the coat rack that form no part of the claimed design.

    Drone
    2.
    外观设计
    Drone 有权

    公开(公告)号：USD1034315S1

    公开(公告)日：2024-07-09

    申请号：US29933895

    申请日：2024-03-22

    Applicant: Jie Wang

    Designer： Jie Wang

    Abstract: FIG. 1 is a front, right and top perspective view of a drone, showing my new design;
    FIG. 2 is a rear, left and bottom perspective view thereof;
    FIG. 3 is a front view thereof;
    FIG. 4 is a rear view thereof;
    FIG. 5 is a left side view thereof;
    FIG. 6 is a right side view thereof;
    FIG. 7 is a top plan view thereof; and,
    FIG. 8 is a bottom plan view thereof.
    The broken lines shown in the drawings illustrate portions of the drone that form no part of the claimed design.

    Aircraft
    3.
    外观设计
    Aircraft 有权

    公开(公告)号：USD1007367S1

    公开(公告)日：2023-12-12

    申请号：US29903527

    申请日：2023-09-25

    Applicant: Jie Wang

    Designer： Jie Wang

    Abstract: FIG. 1 is a front, right and bottom perspective view of an aircraft, showing my new design;
    FIG. 2 is a rear, left and top perspective view thereof;
    FIG. 3 is a front elevation view thereof;
    FIG. 4 is a rear elevation view thereof;
    FIG. 5 is a left side elevation view thereof;
    FIG. 6 is a right side elevation view thereof;
    FIG. 7 is a top plan view thereof; and,
    FIG. 8 is a bottom plan view thereof.
    The broken lines shown in the drawings illustrate portions of the aircraft that form no part of the claimed design.

    Face mask
    4.
    外观设计
    Face mask 有权

    公开(公告)号：USD929079S1

    公开(公告)日：2021-08-31

    申请号：US29749554

    申请日：2020-09-07

    Applicant: Jie Wang

    Designer： Jie Wang

    Systems, methods, and computer readable media for maintaining packet data protocol (PDP) context while performing data offload
    6.
    发明授权
    Systems, methods, and computer readable media for maintaining packet data protocol (PDP) context while performing data offload 有权
    用于在执行数据卸载时维护分组数据协议（PDP）上下文的系统，方法和计算机可读介质

    公开(公告)号：US08665721B2

    公开(公告)日：2014-03-04

    申请号：US13032613

    申请日：2011-02-22

    Applicant: Gibson Soon Teck Ang ,  Thomas Gschwendtner ,  Jie Wang

    Inventor： Gibson Soon Teck Ang ,  Thomas Gschwendtner ,  Jie Wang

    IPC: H04L12/26 ,  H04H20/71 ,  H04W4/00

    CPC classification number: H04L65/1066 ,  H04L43/10 ,  H04W28/085 ,  H04W76/22 ,  H04W76/25 ,  H04W88/16

    Abstract: Systems, methods, and computer readable media for maintaining packet data protocol (PDP) context while performing data offload are disclosed. According to one aspect, a method for maintaining PDP context while performing data offload includes detecting a data offload condition wherein a UE for which a first network node is maintaining a PDP context is sending or receiving data using a data path that does not include the first network node. While the data offload condition exists, packets are sent from a source other than the UE to the first network node so as to cause the first network node to maintain the PDP context for the UE. In one embodiment, a node interposed between the UE and the first network node periodically sends dummy packets or heart beat packets to the first network node on behalf of the UE, which may include packets that appear to come from the UE.

    Abstract translation: 公开了用于在执行数据卸载时维持分组数据协议（PDP）上下文的系统，方法和计算机可读介质。 根据一个方面，一种用于在执行数据卸载时维护PDP上下文的方法包括：检测数据卸载条件，其中第一网络节点正在维护PDP上下文的UE正在使用不包括第一网络的数据路径来发送或接收数据 网络节点。 在存在数据卸载条件的情况下，将数据包从UE之外的源发送到第一网络节点，以使第一网络节点维持UE的PDP上下文。 在一个实施例中，插入在UE和第一网络节点之间的节点周期性地向代表UE的第一网络节点发送虚拟分组或心跳分组，其可以包括看起来来自UE的分组。

    System and method for face verification using video sequence
    7.
    发明授权
    System and method for face verification using video sequence 有权
    使用视频序列进行面部验证的系统和方法

    公开(公告)号：US08351662B2

    公开(公告)日：2013-01-08

    申请号：US12883931

    申请日：2010-09-16

    Applicant: Jie Wang

    Inventor： Jie Wang

    IPC: G06K9/00

    CPC classification number: G06F21/32 ,  G06K9/00248 ,  G06K9/00281 ,  G06K2009/4666

    Abstract: Face verification is performed using video data. The two main modules are face image capturing and face verification. In face image capturing, good frontal face images are captured from input video data. A frontal face quality score discriminates between frontal and profile faces. In face verification, a local binary pattern histogram is selected as the facial feature descriptor for its high discriminative power and computational efficiency. Chi-Square (χ2) distance between LBP histograms from two face images are then calculated as a face dissimilarity measure. The decision whether or not two images belong to the same person is then made by comparing the corresponding distance with a pre-defined threshold. Given the fact that more than one face images can be captured per person from video data, several feature based and decision based aggregators are applied to combine pair-wise distances to further improve the verification performance.

    Abstract translation: 使用视频数据进行脸部验证。 两个主要模块是面部图像捕获和面部验证。 在脸部图像捕获中，从输入视频数据中捕获良好的正面脸部图像。 正面和面部面部之间的正面面积评分。 在面部验证中，选择局部二进制图案直方图作为面部特征描述符，用于其高辨识力和计算效率。 然后将来自两张脸部图像的LBP直方图之间的χ2距离（χ2）计算为脸部不相似度。 然后通过将对应的距离与预定义的阈值进行比较来确定两个图像是否属于同一个人。 鉴于每个人可以从视频数据中捕获多于一张的脸部图像，因此应用几种基于特征和基于决策的聚合器来组合成对距离以进一步提高验证性能。

    Automatic face recognition
    8.
    发明授权
    Automatic face recognition 有权
    自动人脸识别

    公开(公告)号：US08224042B2

    公开(公告)日：2012-07-17

    申请号：US12402761

    申请日：2009-03-12

    Applicant: Jie Wang

    Inventor： Jie Wang

    IPC: G06K9/00

    CPC classification number: G06K9/00281 ,  G06K9/00275 ,  G06T7/73 ,  G06T2207/30201

    Abstract: Automatic face recognition. In a first example embodiment, a method for automatic face recognition includes several acts. First, a face pattern and two eye patterns are detected. Then, the face pattern is normalized. Next, the normalized face pattern is transformed into a normalized face feature vector of Gabor feature representations. Then, a difference image vector is calculated. Next, the difference image vector is projected to a lower-dimensional intra-subject subspace extracted from a pre-collected training face database. Then, a square function is applied to each component of the projection. Next, a weighted summation of the squared projection is calculated. Then, the previous four acts are repeated for each normalized gallery image feature vector. Finally, the face pattern in the probe digital image is classified as belonging to the gallery image with the highest calculated weighted summation where the highest calculated weighted summation is above a predefined threshold.

    Abstract translation: 自动人脸识别。 在第一示例性实施例中，一种用于自动面部识别的方法包括若干动作。 首先，检测到脸部图案和两个眼睛图案。 然后，脸部图案被归一化。 接下来，归一化面部图案被转换成Gabor特征表示的归一化面部特征向量。 然后，计算差分图像矢量。 接下来，将差分图像矢量投影到从预先收集的训练面部数据库提取的较低维度的被检体内子空间。 然后，将方形函数应用于投影的每个分量。 接下来，计算平方投影的加权和。 然后，对于每个标准化的画廊图像特征向量重复前面的四个动作。 最后，探测数字图像中的面部图案被归类为属于具有最高计算加权求和的画廊图像，其中最高计算加权求和高于预定阈值。

    Mouth Removal Method For Red-Eye Detection And Correction
    9.
    发明申请
    Mouth Removal Method For Red-Eye Detection And Correction 有权
    用于红眼检测和校正的口腔清除方法

    公开(公告)号：US20110194759A1

    公开(公告)日：2011-08-11

    申请号：US12704314

    申请日：2010-02-11

    Applicant: Susan Yang ,  Jie Wang

    Inventor： Susan Yang ,  Jie Wang

    IPC: G06K9/34 ,  G06K9/48

    CPC classification number: G06K9/00234 ,  G06K9/4652

    Abstract: An input image (e.g. a digital RGB color image) is subjected to an eye classifier that is targeted at discriminating a complete eye pattern from any non-eye patterns. The red-eye candidate list with associated bounding boxes that are generated by the red-eye classifier are received. The bounding rectangles are subjected to object segmentation. A connected component labeling procedure is then applied to obtain one or more red regions. The largest red region is then chosen for feature extraction. A number of features are then extracted from this region. Then these features are used to determine if the particular candidate red-eye object is a mouth.

    Abstract translation: 将输入图像（例如，数字RGB彩色图像）经受眼睛分类器，其目标是从任何非眼图图案区分完整的眼图。 接收由红眼分类器生成的具有关联边界框的红眼候选列表。 边界矩形经受对象分割。 然后应用连接的组件标签过程以获得一个或多个红色区域。 然后选择最大的红色区域进行特征提取。 然后从该区域提取许多特征。 然后，这些特征用于确定特定的候选红眼物体是否是嘴。

    Automatic Red-Eye Object Classification In Digital Images Using A Boosting-Based Framework
    10.
    发明申请
    Automatic Red-Eye Object Classification In Digital Images Using A Boosting-Based Framework 有权
    使用基于Boosting的框架的数字图像中的自动红眼对象分类

    公开(公告)号：US20110081079A1

    公开(公告)日：2011-04-07

    申请号：US12575298

    申请日：2009-10-07

    Applicant: Jie Wang ,  Rastislav Lukac

    Inventor： Jie Wang ,  Rastislav Lukac

    IPC: G06K9/00

    CPC classification number: G06K9/00604 ,  G06K9/6257

    Abstract: Automatic red-eye object classification in digital images using a boosting-based framework. In a first example embodiment, a method for classifying a candidate red-eye object in a digital photographic image includes several acts. First, a candidate red-eye object in a digital photographic image is selected. Next, a search scale set and a search region for the candidate red-eye object where an eye object may reside is determined. Then, the number of subwindows that satisfy an AdaBoost classifier is determined. This number is denoted as a vote. Next, the maximum size of the subwindows that satisfy the AdaBoost classifier is determined. Then, a normalized threshold is calculated by multiplying a predetermined constant threshold by the calculated maximum size. Next, the vote is compared with the normalized threshold. Finally, the candidate red-eye object is transformed into a true red-eye object if the vote is greater than the normalized threshold.

    Abstract translation: 使用基于boosting的框架在数字图像中自动红眼对象分类。 在第一示例性实施例中，用于对数字摄影图像中的候选红眼物体进行分类的方法包括若干动作。 首先，选择数字摄影图像中的候选红眼物体。 接下来，确定眼睛对象可能驻留的候选红眼对象的搜索比例集和搜索区域。 然后，确定满足AdaBoost分类器的子窗口的数量。 这个数字表示为投票。 接下来，确定满足AdaBoost分类器的子窗口的最大大小。 然后，通过将预定的常数阈值乘以所计算的最大尺寸来计算归一化阈值。 接下来，将投票与标准化阈值进行比较。 最后，如果投票大于归一化阈值，则将候选红眼物体转换成真正的红眼物体。

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