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    • 1. 发明授权
    • Recovering dis-occluded areas using temporal information integration
    • 使用时间信息集成恢复被遮挡的区域
    • US09031357B2
    • 2015-05-12
    • US13463934
    • 2012-05-04
    • Philip Andrew ChouCha ZhangZhengyou ZhangShujie Liu
    • Philip Andrew ChouCha ZhangZhengyou ZhangShujie Liu
    • G06K9/32G06T7/00
    • G06K9/32G06T7/593
    • A temporal information integration dis-occlusion system and method for using historical data to reconstruct a virtual view containing an occluded area. Embodiments of the system and method use temporal information of the scene captured previously to obtain a total history. This total history is warped onto information captured by a camera at a current time in order to help reconstruct the dis-occluded areas. The historical data (or frames) from the total history match only a portion of the frames contained in the captured information. This warping yields warped history information. Warping is performed by using one of two embodiments to match points in an estimation of the current information to points in the captured information. Next, regions of current information are split using a classifier. The warped history information and the captured information then are merged to obtain an estimate for the current information and the reconstructed virtual view.
    • 一种用于使用历史数据重建包含遮挡区域的虚拟视图的时间信息整合遮挡系统和方法。 系统和方法的实施例使用先前捕获的场景的时间信息来获得总历史。 这个总历史在当前时间由相机拍摄的信息扭曲,以帮助重建被遮挡的区域。 来自总历史记录的历史数据(或帧)仅匹配捕获信息中包含的帧的一部分。 这种扭曲产生扭曲的历史信息。 通过使用两个实施例中的一个实现扭曲,以将当前信息的估计中的点与捕获的信息中的点进行匹配。 接下来,使用分类器分割当前信息的区域。 然后将翘曲的历史信息和捕获的信息合并,以获得当前信息和重建的虚拟视图的估计。
    • 2. 发明申请
    • RECOVERING DIS-OCCLUDED AREAS USING TEMPORAL INFORMATION INTEGRATION
    • 使用时间信息整合恢复分散区域
    • US20130294710A1
    • 2013-11-07
    • US13463934
    • 2012-05-04
    • Philip Andrew ChouCha ZhangZhengyou ZhangShujie Liu
    • Philip Andrew ChouCha ZhangZhengyou ZhangShujie Liu
    • G06K9/32
    • G06K9/32G06T7/593
    • A temporal information integration dis-occlusion system and method for using historical data to reconstruct a virtual view containing an occluded area. Embodiments of the system and method use temporal information of the scene captured previously to obtain a total history. This total history is warped onto information captured by a camera at a current time in order to help reconstruct the dis-occluded areas. The historical data (or frames) from the total history match only a portion of the frames contained in the captured information. This warping yields warped history information. Warping is performed by using one of two embodiments to match points in an estimation of the current information to points in the captured information. Next, regions of current information are split using a classifier. The warped history information and the captured information then are merged to obtain an estimate for the current information and the reconstructed virtual view.
    • 一种用于使用历史数据重建包含遮挡区域的虚拟视图的时间信息整合遮挡系统和方法。 系统和方法的实施例使用先前捕获的场景的时间信息来获得总历史。 这个总历史在当前时间由相机拍摄的信息扭曲,以帮助重建被遮挡的区域。 来自总历史记录的历史数据(或帧)仅匹配捕获信息中包含的帧的一部分。 这种扭曲产生扭曲的历史信息。 通过使用两个实施例中的一个实现扭曲,以将当前信息的估计中的点与捕获的信息中的点进行匹配。 接下来,使用分类器分割当前信息的区域。 然后将翘曲的历史信息和捕获的信息合并,以获得当前信息和重建的虚拟视图的估计。
    • 3. 发明授权
    • Multiple category learning for training classifiers
    • 训练分类器的多类学习
    • US08401979B2
    • 2013-03-19
    • US12618799
    • 2009-11-16
    • Cha ZhangZhengyou Zhang
    • Cha ZhangZhengyou Zhang
    • G06F15/18
    • G06N99/005
    • Described is multiple category learning to jointly train a plurality of classifiers in an iterative manner. Each training iteration associates an adaptive label with each training example, in which during the iterations, the adaptive label of any example is able to be changed by the subsequent reclassification. In this manner, any mislabeled training example is corrected by the classifiers during training. The training may use a probabilistic multiple category boosting algorithm that maintains probability data provided by the classifiers, or a winner-take-all multiple category boosting algorithm selects the adaptive label based upon the highest probability classification. The multiple category boosting training system may be coupled to a multiple instance learning mechanism to obtain the training examples. The trained classifiers may be used as weak classifiers that provide a label used to select a deep classifier for further classification, e.g., to provide a multi-view object detector.
    • 描述了多类学习,以迭代的方式联合训练多个分类器。 每个训练迭代将自适应标签与每个训练示例相关联,其中在迭代期间,任何示例的自适应标签能够由随后的重新分类改变。 以这种方式,任何错误标记的训练示例在训练期间由分类器校正。 训练可以使用维护由分类器提供的概率数据的概率多类别提升算法,或者获胜者全部多类别增强算法基于最高概率分类来选择自适应标签。 多类别增强训练系统可以耦合到多实例学习机制以获得训练示例。 经训练的分类器可以用作弱分类器,其提供用于选择用于进一步分类的深分类器的标签,例如提供多视图对象检测器。
    • 10. 发明授权
    • Learning image enhancement
    • 学习图像增强
    • US08175382B2
    • 2012-05-08
    • US11801620
    • 2007-05-10
    • Zicheng LiuCha ZhangZhengyou Zhang
    • Zicheng LiuCha ZhangZhengyou Zhang
    • G06K9/00
    • G06K9/00234H04N1/62H04N1/628
    • Image enhancement techniques are described to enhance an image in accordance with a set of training images. In an implementation, an image color tone map is generated for a facial region included in an image. The image color tone map may be normalized to a color tone map for a set of training images so that the image color tone map matches the map for the training images. The normalized color tone map may be applied to the image to enhance the in-question image. In further implementations, the procedure may be updated when the average color intensity in non-facial regions differs from an accumulated mean by a threshold amount.
    • 描述图像增强技术以根据一组训练图像来增强图像。 在实现中,为包括在图像中的面部区域生成图像色调映射。 图像色调图可以被归一化为用于一组训练图像的色调图,使得图像色调图匹配训练图像的图。 归一化色调图可以应用于图像以增强问题图像。 在进一步的实施中,当非面部区域中的平均颜色强度与积累的平均值不同阈值量时,可以更新该过程。