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    • 1. 发明授权
    • Method and apparatus for identifying physical features in video
    • 用于识别视频中的物理特征的方法和装置
    • US07567704B2
    • 2009-07-28
    • US11289886
    • 2005-11-30
    • Kwong Wing AuMichael E. BazakosYunqian Ma
    • Kwong Wing AuMichael E. BazakosYunqian Ma
    • G06K9/00G06K9/62G01C3/00G01C5/00G01B11/24G01B11/30H04N7/18H04N9/47H04N5/232
    • G06K9/00771G06K9/46
    • An image is processed by a sensed-feature-based classifier to generate a list of objects assigned to classes. The most prominent objects (those objects whose classification is most likely reliable) are selected for range estimation and interpolation. Based on the range estimation and interpolation, the sensed features are converted to physical features for each object. Next, that subset of objects is then run through a physical-feature-based classifier that re-classifies the objects. Next, the objects and their range estimates are re-run through the processes of range estimation and interpolation, sensed-feature-to-physical-feature conversion, and physical-feature-based classification iteratively to continuously increase the reliability of the classification as well as the range estimation. The iterations are halted when the reliability reaches a predetermined confidence threshold. In a preferred embodiment, a next subset of objects having the next highest prominence in the same image is selected and the entire iterative process is repeated. This set of iterations will include evaluation of both of the first and second subsets of objects. The process can be repeated until all objects have been classified.
    • 图像由基于感测特征的分类器处理以生成分配给类的对象的列表。 选择最突出的对象(那些分类最可靠的对象)用于范围估计和插值。 基于范围估计和内插,感测到的特征被转换为每个对象的物理特征。 接下来,该对象的子集然后通过基于物理特征的分类器来运行,该分类器重新分类对象。 接下来,通过范围估计和插值,感测特征到物理特征转换和基于物理特征的分类的过程重新运行对象及其范围估计,以不断提高分类的可靠性 作为范围估计。 当可靠性达到预定的置信阈值时,迭代停止。 在优选实施例中,选择具有相同图像中的下一个最高突出的对象的下一个子集,并重复整个迭代过程。 这组迭代将包括评估对象的第一和第二子集。 可以重复该过程,直到所有对象都被分类为止。
    • 2. 发明申请
    • Anomaly detection in a video system
    • 视频系统中的异常检测
    • US20080031491A1
    • 2008-02-07
    • US11498923
    • 2006-08-03
    • Yunqian MaMichael E. BazakosKwong Wing Au
    • Yunqian MaMichael E. BazakosKwong Wing Au
    • G06K9/00G06K9/46G06K9/62G06K9/66
    • G06K9/00771G06K9/6284G08B13/19613
    • In an embodiment, a video processor is configured to identify anomalous or abnormal behavior. A hierarchical behavior model based on the features of the complement of the abnormal behavior of interest is developed. For example, if the abnormal behavior is stealing or shoplifting, a model is developed for the actions of normal shopping behavior (i.e., not stealing or not shoplifting). Features are extracted from video data and applied to an artificial intelligence construct such as a dynamic Bayesian network (DBN) to determine if the normal behavior is present in the video data (i.e, the complement of the abnormal behavior). If the DBN indicates that the extracted features depart from the behavior model (the complement of the abnormal behavior), then the presence of the abnormal behavior in the video data may be assumed.
    • 在一个实施例中,视频处理器被配置为识别异常或异常行为。 开发了基于异常行为的补充特征的分层行为模型。 例如,如果异常行为是偷窃或偷窃,则为正常购物行为(即不偷窃或不偷窃)的行为开发出一个模型。 从视频数据提取特征并将其应用于诸如动态贝叶斯网络(DBN)的人造智能结构,以确定视频数据中是否存在正常行为(即,异常行为的补充)。 如果DBN指示提取的特征离开行为模型(异常行为的补充),则可以假设视频数据中的异常行为的存在。
    • 10. 发明授权
    • Three-dimensional multilayer skin texture recognition system and method
    • 三维多层皮肤纹理识别系统及方法
    • US08634596B2
    • 2014-01-21
    • US12972829
    • 2010-12-20
    • Saad J. BedrosKwong Wing Au
    • Saad J. BedrosKwong Wing Au
    • G06K9/00G06K9/46G06K9/66G06K9/52
    • G06K9/00201G06K9/00268G06K2009/4657
    • A three-dimensional multilayer skin texture recognition system and method based on hyperspectral imaging. Three-dimensional facial model associated with an object may be acquired from a three-dimensional image capturing device. A face reconstruction approach may be implemented to reconstruct and rewarp the three-dimensional facial model to a frontal face image. A hyperspectral imager may be employed to extract a micro structure skin signature associated with the skin surface. The micro structure skin signature may be characterized utilizing a weighted subtraction of reflectance at different wavelengths that captures different layers under the skin surface via a multilayer skin texture recognition module. The volumetric skin data associated with the face skin can be classified via a volumetric pattern.
    • 基于高光谱成像的三维多层皮肤纹理识别系统及方法。 可以从三维图像捕获装置获取与对象相关联的三维面部模型。 可以实现面部重建方法以将三维面部模型重新构建并重新平面到正面面部图像。 可以使用高光谱成像器来提取与皮肤表面相关联的微结构皮肤特征。 可以通过经由多层皮肤纹理识别模块在皮肤表面捕获不同层的不同波长的反射率的加权减法来表征微结构皮肤特征。 与脸部皮肤相关的体积皮肤数据可以通过体积图案进行分类。