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    • 3. 发明申请
    • DETERMINING FLUID LEAKAGE VOLUME IN PIPELINES
    • 确定流体中的流体泄漏体积
    • US20140229124A1
    • 2014-08-14
    • US14128640
    • 2012-03-31
    • Felipe AlbertaoYing ChenJin HuangChunhua TianHao WangJing Xiao
    • Felipe AlbertaoYing ChenJin HuangChunhua TianHao WangJing Xiao
    • G01F1/34
    • F17D5/06F17D5/02G01F1/34G01F22/02G01M3/2815
    • A method and an apparatus for determining leakage volume of fluid in transportation pipelines are provided. The method comprises: obtaining the negative pressure wave signals detected by at least two pressure sensors arranged on the pipeline; determining the pressure signal at the leakage location based on the negative pressure wave signals; determining the leakage rate during a leakage period based on the pressure signal at the leakage location according to a leakage model; and determining the leakage volume of the fluid in the pipeline based on the leakage rate and the leakage period. The apparatus provided corresponds to the method described above. By using the method and apparatus described above, the leakage volume of the transportation pipelines can be obtained to help understand the leakage profile of the pipelines and thus reduce losses.
    • 提供了一种用于确定运输管道中的流体泄漏体积的方法和装置。 该方法包括:获得由布置在管道上的至少两个压力传感器检测的负压波信号; 基于负压波信号确定泄漏位置处的压力信号; 根据泄漏模型,根据泄漏位置处的压力信号确定泄漏期间的泄漏率; 并基于泄漏率和泄漏周期来确定管道中流体的泄漏体积。 所提供的装置对应于上述方法。 通过使用上述方法和装置,可以获得运输管线的泄漏量,以帮助了解管道的泄漏特征,从而减少损失。
    • 5. 发明授权
    • Adaptive threshold for object detection
    • 物体检测的自适应阈值
    • US08948522B2
    • 2015-02-03
    • US13198412
    • 2011-08-04
    • Yuanyuan DingJing Xiao
    • Yuanyuan DingJing Xiao
    • G06K9/62G06K9/46
    • G06K9/624G06K9/4642G06K9/6269
    • Systems and methods for developing and using adaptive threshold values for different input images for object detection are disclosed. In embodiments, detector response histogram-based systems and methods train models for predicting optimal threshold values for different images. In embodiments, when training the model, an optimal threshold value for an image is defined as the value that maximizes the reduction of false positive image patches while preserving as many true positive image patches as possible. Once trained, the model may be used to set different threshold values for different images by inputting a detector response histogram for the image patches of an image into the model to determine a threshold value for detection.
    • 公开了用于开发和使用用于对象检测的不同输入图像的自适应阈值的系统和方法。 在实施例中,基于检测器响应直方图的系统和方法训练用于预测不同图像的最佳阈值的模型。 在实施例中,当训练模型时,图像的最佳阈值被定义为最大化假阳性图像斑块的减少的值,同时保留尽可能多的真正的正图像斑块。 一旦被训练,该模型可以用于通过将图像的图像块的检测器响应直方图输入到模型中来确定用于检测的阈值来为不同的图像设置不同的阈值。
    • 6. 发明授权
    • Multiview face content creation
    • Multiview面对内容创作
    • US08933928B2
    • 2015-01-13
    • US13303044
    • 2011-11-22
    • Derek ShiellJing Xiao
    • Derek ShiellJing Xiao
    • G06T15/00G06T17/00G06T15/20
    • G06T17/00G06T15/205
    • New views of a 2D image are generated by identifying an object class within the image, such as through a face detector. The face is then fitted to a model face by means of an AAM, and the results extended to a fitted 3D polygon mesh face. A boundary perimeter with predefined anchor points and a predefined triangulation with the 3D polygon mesh is defined a predefined depth distance from the depth center of known landmarks within the 3D polygon mesh face. By rotating the 3D polygon mesh face relative to the boundary perimeter, which may follow the perimeter of the input image, new views of the input image are generated.
    • 通过识别图像内的对象类(例如通过面部检测器)来生成2D图像的新视图。 然后通过AAM将面部安装到模型面上,并将结果扩展到拟合的3D多边形网格面。 定义了与3D多边形网格预定义三角剖分的边界周界,与3D多边形网格面内的已知界标深度中心的预定深度距离。 通过旋转3D多边形网格面相对于可能跟随输入图像的周边的边界周界,生成输入图像的新视图。
    • 7. 发明授权
    • Continuous linear dynamic systems
    • 连续线性动态系统
    • US08917907B2
    • 2014-12-23
    • US13406011
    • 2012-02-27
    • Jinjun WangJing Xiao
    • Jinjun WangJing Xiao
    • G06K9/00G06K9/62
    • G06K9/00765G06K9/00335G06K9/6297
    • Aspects of the present invention include systems and methods for segmentation and recognition of action primitives. In embodiments, a framework, referred to as the Continuous Linear Dynamic System (CLDS), comprises two sets of Linear Dynamic System (LDS) models, one to model the dynamics of individual primitive actions and the other to model the transitions between actions. In embodiments, the inference process estimates the best decomposition of the whole sequence into continuous alternating between the two set of models, using an approximate Viterbi algorithm. In this way, both action type and action boundary may be accurately recognized.
    • 本发明的方面包括用于分割和识别动作原语的系统和方法。 在实施例中,被称为连续线性动态系统(CLDS)的框架包括两组线性动态系统(LDS)模型,其中一个模型用于对各个原始动作的动力学进行建模,另一组模型对动作之间的转换进行建模。 在实施例中,推理过程使用近似维特比算法来估计整个序列的最佳分解到两组模型之间的连续交替。 以这种方式,可以准确地识别动作类型和动作边界。
    • 9. 发明授权
    • Identifying text pixels in scanned images
    • 识别扫描图像中的文本像素
    • US08606010B2
    • 2013-12-10
    • US13051223
    • 2011-03-18
    • Jing Xiao
    • Jing Xiao
    • G06K9/34
    • G06K9/6257G06K2209/01
    • A processor and method make use of multiple weak classifiers to construct a single strong classifier to identify regions that contain text within an input image document. The weak classifiers are grouped by their computing cost from low to median to high, and each weak classifier is assigned a weight value based on its ability to accurately identify text regions. A level 1 classifier is constructed by selecting weak classifiers from the low group, a level 2 classifier is constructed by selecting weak classifiers from the low and median groups, and a level 3 classifier is constructed by selecting weak classifiers from the low, median and high groups. Regions that the level 1 classifier identifies as containing text are submitted to the level 2 classifier, and regions that the level 2 classifier identifies as containing text are submitted to the level 3 classifier.
    • 处理器和方法利用多个弱分类器来构造单个强分类器来识别包含输入图像文档内的文本的区域。 弱分类器的计算成本从低到中等到高,每个弱分类器都基于其准确识别文本区域的能力来分配权重值。 通过从低组中选择弱分类器构建1级分类器,通过从低和中值组中选择弱分类器构建2级分类器,通过从低,中,高选择弱分类器构建3级分类器 团体 1级分类器识别为包含文本的区域被提交给2级分类器,2级分类器识别为包含文本的区域被提交到3级分类器。