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    • 1. 发明申请
    • STEREO IMAGE SEGMENTATION
    • 立体图像分割
    • US20100220921A1
    • 2010-09-02
    • US12780857
    • 2010-05-14
    • Andrew BlakeAntonio CriminisiGeoffrey CrossVladimir KolmogorovCarsten Curt Eckard Rother
    • Andrew BlakeAntonio CriminisiGeoffrey CrossVladimir KolmogorovCarsten Curt Eckard Rother
    • G06K9/00
    • G06K9/00234G06K9/342G06K9/38G06K9/4652G06T7/11G06T7/162G06T7/194G06T2207/10021G06T2207/10024G06T2207/20072
    • Real-time segmentation of foreground from background layers in binocular video sequences may be provided by a segmentation process which may be based on one or more factors including likelihoods for stereo-matching, color, and optionally contrast, which may be fused to infer foreground and/or background layers accurately and efficiently. In one example, the stereo image may be segmented into foreground, background, and/or occluded regions using stereo disparities. The stereo-match likelihood may be fused with a contrast sensitive color model that is initialized or learned from training data. Segmentation may then be solved by an optimization algorithm such as dynamic programming or graph cut. In a second example, the stereo-match likelihood may be marginalized over foreground and background hypotheses, and fused with a contrast-sensitive color model that is initialized or learned from training data. Segmentation may then be solved by an optimization algorithm such as a binary graph cut.
    • 可以通过分割过程来提供来自双目视频序列中的背景层的前景的实时分割,分割过程可以基于一个或多个因素,包括立体匹配,颜色和可选对比的可能性,其可以融合到推断前景和 /或背景层准确高效。 在一个示例中,立体图像可以使用立体声差异被分割成前景,背景和/或遮挡区域。 立体匹配似然率可以与从训练数据初始化或学习的对比度敏感颜色模型融合。 然后可以通过诸如动态规划或图形切割的优化算法来解决分割。 在第二个例子中,立体匹配似然度在前景和背景假设上可能被边缘化,并且与从训练数据初始化或学习的对比度敏感颜色模型融合。 然后可以通过诸如二进制图切割的优化算法来解决分割。
    • 2. 发明授权
    • Stereo image segmentation
    • 立体图像分割
    • US07991228B2
    • 2011-08-02
    • US12780857
    • 2010-05-14
    • Andrew BlakeAntonio CriminisiGeoffrey CrossVladimir KolmogorovCarsten Curt Eckard Rother
    • Andrew BlakeAntonio CriminisiGeoffrey CrossVladimir KolmogorovCarsten Curt Eckard Rother
    • G06K9/34
    • G06K9/00234G06K9/342G06K9/38G06K9/4652G06T7/11G06T7/162G06T7/194G06T2207/10021G06T2207/10024G06T2207/20072
    • Real-time segmentation of foreground from background layers in binocular video sequences may be provided by a segmentation process which may be based on one or more factors including likelihoods for stereo-matching, color, and optionally contrast, which may be fused to infer foreground and/or background layers accurately and efficiently. In one example, the stereo image may be segmented into foreground, background, and/or occluded regions using stereo disparities. The stereo-match likelihood may be fused with a contrast sensitive color model that is initialized or learned from training data. Segmentation may then be solved by an optimization algorithm such as dynamic programming or graph cut. In a second example, the stereo-match likelihood may be marginalized over foreground and background hypotheses, and fused with a contrast-sensitive color model that is initialized or learned from training data. Segmentation may then be solved by an optimization algorithm such as a binary graph cut.
    • 可以通过分割过程来提供来自双目视频序列中的背景层的前景的实时分割,分割过程可以基于一个或多个因素,包括立体匹配,颜色和可选对比的可能性,其可以融合到推断前景和 /或背景层准确高效。 在一个示例中,立体图像可以使用立体声差异被分割成前景,背景和/或遮挡区域。 立体匹配似然率可以与从训练数据初始化或学习的对比度敏感颜色模型融合。 然后可以通过诸如动态规划或图形切割的优化算法来解决分割。 在第二个例子中,立体匹配似然度在前景和背景假设上可能被边缘化,并且与从训练数据初始化或学习的对比度敏感颜色模型融合。 然后可以通过诸如二进制图切割的优化算法来解决分割。
    • 3. 发明授权
    • Stereo image segmentation
    • 立体图像分割
    • US07720282B2
    • 2010-05-18
    • US11195027
    • 2005-08-02
    • Andrew BlakeAntonio CriminisiGeoffrey CrossVladimir KolmogorovCarsten Curt Eckard Rother
    • Andrew BlakeAntonio CriminisiGeoffrey CrossVladimir KolmogorovCarsten Curt Eckard Rother
    • G06K9/34
    • G06K9/00234G06K9/342G06K9/38G06K9/4652G06T7/11G06T7/162G06T7/194G06T2207/10021G06T2207/10024G06T2207/20072
    • Real-time segmentation of foreground from background layers in binocular video sequences may be provided by a segmentation process which may be based on one or more factors including likelihoods for stereo-matching, color, and optionally contrast, which may be fused to infer foreground and/or background layers accurately and efficiently. In one example, the stereo image may be segmented into foreground, background, and/or occluded regions using stereo disparities. The stereo-match likelihood may be fused with a contrast sensitive color model that is initialized or learned from training data. Segmentation may then be solved by an optimization algorithm such as dynamic programming or graph cut. In a second example, the stereo-match likelihood may be marginalized over foreground and background hypotheses, and fused with a contrast-sensitive color model that is initialized or learned from training data. Segmentation may then be solved by an optimization algorithm such as a binary graph cut.
    • 可以通过分割过程来提供来自双目视频序列中的背景层的前景的实时分割,分割过程可以基于一个或多个因素,包括立体匹配,颜色和可选对比的可能性,其可以融合到推断前景和 /或背景层准确高效。 在一个示例中,立体图像可以使用立体声差异被分割成前景,背景和/或遮挡区域。 立体匹配似然率可以与从训练数据初始化或学习的对比度敏感颜色模型融合。 然后可以通过诸如动态规划或图形切割的优化算法来解决分割。 在第二个例子中,立体匹配似然度在前景和背景假设上可能被边缘化,并且与从训练数据初始化或学习的对比度敏感颜色模型融合。 然后可以通过诸如二进制图切割的优化算法来解决分段。
    • 5. 发明授权
    • Image tapestry
    • 图像挂毯
    • US07653261B2
    • 2010-01-26
    • US11213080
    • 2005-08-26
    • Andrew BlakeCarsten Curt Eckard RotherSanjiv KumarVladimir Kolmogorov
    • Andrew BlakeCarsten Curt Eckard RotherSanjiv KumarVladimir Kolmogorov
    • G06K9/36
    • G06K9/469G06T11/60
    • An output image formed from at least a portion of one or more input images may be automatically synthesized as a tapestry image. To determine which portion or region of each input image will be used in the image tapestry, the regions of each image may be labeled by one of a plurality of labels. The multi-class labeling problem of creating the tapestry may be resolved such that each region in the tapestry is constructed from one or more salient input image regions that are selected and placed such that neighboring blocks in the tapestry satisfy spatial compatibility. This solution may be formulated using a Markov Random Field and the resulting tapestry energy function may be optimized in any suitable manner. To optimize the tapestry energy function, an expansion move algorithm for energy functions may be generated to apply to non-metric hard and/or soft constraints.
    • 由一个或多个输入图像的至少一部分形成的输出图像可以自动合成为挂毯图像。 为了确定在图像挂毯中将使用每个输入图像的哪个部分或区域,每个图像的区域可以由多个标签之一标记。 可以解决创建挂毯的多类标签问题,使得挂毯中的每个区域由选择和放置的一个或多个显着输入图像区域构成,使得挂毯中的相邻块满足空间兼容性。 该解决方案可以使用马尔科夫随机场来形成,并且所得到的挂毯能量函数可以以任何合适的方式进行优化。 为了优化挂毯能量函数,可以产生用于能量函数的扩展移动算法以应用于非度量硬和/或软约束。
    • 7. 发明授权
    • Image segmentation using reduced foreground training data
    • 使用减少的前景训练数据的图像分割
    • US08422769B2
    • 2013-04-16
    • US12718321
    • 2010-03-05
    • Carsten Curt Eckard RotherToby SharpAndrew BlakeVladimir Kolmogorov
    • Carsten Curt Eckard RotherToby SharpAndrew BlakeVladimir Kolmogorov
    • G06K9/62
    • G06K9/34G06K9/62
    • Methods of image segmentation using reduced foreground training data are described. In an embodiment, the foreground and background training data for use in segmentation of an image is determined by optimization of a modified energy function. The modified energy function is the energy function used in image segmentation with an additional term comprising a scalar value. The optimization is performed for different values of the scalar to produce multiple initial segmentations and one of these segmentations is selected based on pre-defined criteria. The training data is then used in segmenting the image. In other embodiments further methods are described: one places an ellipse inside the user-defined bounding box to define the background training data and another uses a comparison of properties of neighboring image elements, where one is outside the user-defined bounding box, to reduce the foreground training data.
    • 描述使用减少的前景训练数据的图像分割方法。 在一个实施例中,用于图像分割的前景和背景训练数据通过改进的能量函数的优化来确定。 修改的能量函数是在图像分割中使用的能量函数,附加项包括标量值。 对标量的不同值执行优化以产生多个初始分段,并且基于预定义的标准来选择这些分段之一。 然后训练数据用于分割图像。 在其他实施例中,描述了进一步的方法:一个将椭圆放置在用户定义的边界框内以定义背景训练数据,另一个使用相邻图像元素的属性的比较,其中一个在用户定义的界限框之外,以减少 前台训练数据。
    • 8. 发明申请
    • Image Segmentation Using Reduced Foreground Training Data
    • 使用减少的前景训练数据的图像分割
    • US20110216965A1
    • 2011-09-08
    • US12718321
    • 2010-03-05
    • Carsten Curt Eckard RotherToby SharpAndrew BlakeVladimir Kolmogorov
    • Carsten Curt Eckard RotherToby SharpAndrew BlakeVladimir Kolmogorov
    • G06K9/62
    • G06K9/34G06K9/62
    • Methods of image segmentation using reduced foreground training data are described. In an embodiment, the foreground and background training data for use in segmentation of an image is determined by optimization of a modified energy function. The modified energy function is the energy function used in image segmentation with an additional term comprising a scalar value. The optimization is performed for different values of the scalar to produce multiple initial segmentations and one of these segmentations is selected based on pre-defined criteria. The training data is then used in segmenting the image. In other embodiments further methods are described: one places an ellipse inside the user-defined bounding box to define the background training data and another uses a comparison of properties of neighboring image elements, where one is outside the user-defined bounding box, to reduce the foreground training data.
    • 描述使用减少的前景训练数据的图像分割方法。 在一个实施例中,用于图像分割的前景和背景训练数据通过改进的能量函数的优化来确定。 修改的能量函数是在图像分割中使用的能量函数,附加项包括标量值。 对标量的不同值执行优化以产生多个初始分段,并且基于预定义的标准来选择这些分段之一。 然后训练数据用于分割图像。 在其他实施例中,描述了进一步的方法:一个将椭圆放置在用户定义的边界框内以定义背景训练数据,另一个使用相邻图像元素的属性的比较,其中一个在用户定义的界限框之外,以减少 前台训练数据。