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    PHOTO PROCESSING METHOD AND APPARATUS
    2.
    发明申请
    PHOTO PROCESSING METHOD AND APPARATUS 审中-公开

    公开(公告)号：US20190244027A1

    公开(公告)日：2019-08-08

    申请号：US16386152

    申请日：2019-04-16

    Applicant: Tencent Technology (Shenzhen) Company Limited

    Inventor： Guofu TAN

    IPC: G06K9/00 ,  G06F16/58 ,  G06F16/51 ,  G06K9/62 ,  G06F3/14

    CPC classification number: G06K9/00684 ,  G06F3/14 ,  G06F16/51 ,  G06F16/58 ,  G06K9/00228 ,  G06K9/00241 ,  G06K9/00268 ,  G06K9/00295 ,  G06K9/6215 ,  G06K9/6222 ,  H04N1/00196

    Abstract: The present disclosure discloses a photo processing method and an apparatus for grouping photos into photo albums based on facial recognition results. The method includes: performing face detection on multiple photos, to obtain a face image feature set, each face image feature in the face image feature set corresponding to one of the multiple photos; determining a face-level similarity for each pair of face image features in the face image feature set; determining a photo-level similarity between each pair of photos in the multiple photos in accordance with their associated face-level similarities; generating a photo set for each target photo in the multiple photos, wherein any photo-level similarity between the target photo and another photo in the photo set exceeds a predefined photo-level threshold; and generating a label for each photo set using photographing location and photographing time information associated with the photos in the photo set.

    Approximate order statistics of real numbers in generic data
    6.
    发明授权
    Approximate order statistics of real numbers in generic data 有权

    公开(公告)号：US09645975B2

    公开(公告)日：2017-05-09

    申请号：US14255981

    申请日：2014-04-18

    Applicant: Splunk Inc.

    Inventor： Steve Yu Zhang

    IPC: G06F17/18

    CPC classification number: G06F17/18 ,  G06F7/22 ,  G06F7/483 ,  G06F7/544 ,  G06F17/30536 ,  G06K9/6222

    Abstract: A method, system, and processor-readable storage medium are directed towards calculating approximate order statistics on a collection of real numbers. In one embodiment, the collection of real numbers is processed to create a digest comprising hierarchy of buckets. Each bucket is assigned a real number N having P digits of precision and ordinality O. The hierarchy is defined by grouping buckets into levels, where each level contains all buckets of a given ordinality. Each individual bucket in the hierarchy defines a range of numbers—all numbers that, after being truncated to that bucket's P digits of precision, are equal to that bucket's N. Each bucket additionally maintains a count of how many numbers have fallen within that bucket's range. Approximate order statistics may then be calculated by traversing the hierarchy and performing an operation on some or all of the ranges and counts associated with each bucket.

    BACKGROUND MODEL FOR COMPLEX AND DYNAMIC SCENES
    7.
    发明申请
    BACKGROUND MODEL FOR COMPLEX AND DYNAMIC SCENES 有权
    复杂和动态场景的背景模型

    公开(公告)号：US20160163065A1

    公开(公告)日：2016-06-09

    申请号：US15019759

    申请日：2016-02-09

    Applicant: 9051147 CANADA INC.

    Inventor： Wesley Kenneth COBB ,  Ming-Jung SEOW ,  Tao YANG

    IPC: G06T7/20 ,  G06K9/66 ,  G06K9/00

    CPC classification number: G06T7/20 ,  G06K9/00335 ,  G06K9/00718 ,  G06K9/00771 ,  G06K9/6218 ,  G06K9/6222 ,  G06K9/66 ,  G06K2009/00738 ,  G06T7/254 ,  G06T2207/10016 ,  G06T2207/20084 ,  G06T2207/30196 ,  G06T2207/30232

    Abstract: Systems and methods for viewing a scene depicted in a sequence of video frames and identifying and tracking objects between separate frames of the sequence. Each tracked object is classified based on known categories and a stream of context events associated with the object is generated. A sequence of primitive events based on the stream of context events is generated and stored together, along with detailed data and generalized data related to an event. All of the data is then evaluated to learn patterns of behavior that occur within the scene.

    Abstract translation: 用于观看视频帧序列中描绘的场景的系统和方法，并且在序列的不同帧之间识别和跟踪对象。 基于已知类别对每个被跟踪对象进行分类，并且生成与对象相关联的上下文事件流。 基于上下文事件流的原始事件序列被生成并存储在一起，以及与事件相关的详细数据和通用数据。 然后评估所有数据以了解场景内出现的行为模式。

    Methods for identifying imaging devices and classifying images acquired by unknown imaging devices
    8.
    发明授权
    Methods for identifying imaging devices and classifying images acquired by unknown imaging devices 有权
    用于识别成像装置并对由未知成像装置获取的图像进行分类的方法

    公开(公告)号：US08565529B2

    公开(公告)日：2013-10-22

    申请号：US13201558

    申请日：2010-02-15

    Applicant: Li Chang-Tsun ,  Richard Leary

    Inventor： Li Chang-Tsun ,  Richard Leary

    IPC: G06K9/46 ,  G06K9/40

    CPC classification number: G06K9/40 ,  G06K9/6222

    Abstract: A method of classifying an image taken by an image capture device, the method comprising the steps of: extracting an initial Sensor Noise Pattern (SNP) for the image; enhancing the initial SNP to create an enhanced SNP by applying a correcting model, wherein the correcting model scales the initial SNP by a factor inversely proportional to the signal intensity of the initial SNP; determining a similarity measure between the enhanced SNP for said image with one or more previously calculated enhanced SNPs for one or more different images; and classifying the image in a group of one or more images with similar or identical SNPs based on the determined similarity measure.

    Abstract translation: 一种对由图像捕获装置拍摄的图像进行分类的方法，所述方法包括以下步骤：提取图像的初始传感器噪声模式（SNP）; 增强初始SNP以通过应用校正模型来创建增强型SNP，其中所述校正模型将所述初始SNP按照与所述初始SNP的信号强度成反比的因子进行缩放; 确定所述图像的增强SNP与一个或多个先前计算的一个或多个不同图像的增强型SNP之间的相似性度量; 并且基于所确定的相似性度量，将具有相似或相同SNP的一个或多个图像的组中的图像分类。

    System for anomaly detection
    9.
    发明授权
    System for anomaly detection 有权
    异常检测系统

    公开(公告)号：US08468104B1

    公开(公告)日：2013-06-18

    申请号：US12592837

    申请日：2009-12-02

    Applicant: Suhas E. Chelian ,  Narayan Srinivasa

    Inventor： Suhas E. Chelian ,  Narayan Srinivasa

    IPC: G06F15/18

    CPC classification number: G06K9/6222 ,  G01S13/887

    Abstract: Described is a system for anomaly detection to detect an anomalous object in an image, such as a concealed object beneath a person's clothing. The system is configured to receive, in a processor, at least one streaming peaked curve (R) representative of a difference between an input and a chosen category for a given feature. A degree of match is then generated between the input and the chosen category for all features. Finally, the degree of match is compared against a predetermined anomaly threshold and, if the degree of match exceeds the predetermined anomaly threshold, then the current feature is designated as an anomaly.

    Abstract translation: 描述了用于异常检测的系统，用于检测图像中的异常物体，例如人的衣服下方的隐藏物体。 系统被配置为在处理器中接收代表给定特征的输入和所选类别之间的差异的至少一个流式峰值曲线（R）。 然后在所有功能的输入和所选类别之间生成匹配度。 最后，将匹配度与预定的异常阈值进行比较，并且如果匹配度超过预定的异常阈值，则将当前特征指定为异常。

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