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公开(公告)号：US20070031037A1
公开(公告)日：2007-02-08
申请号：US11195027
申请日：2005-08-02
CPC分类号： G06K9/00234 , G06K9/342 , G06K9/38 , G06K9/4652 , G06T7/11 , G06T7/162 , G06T7/194 , G06T2207/10021 , G06T2207/10024 , G06T2207/20072
摘要： 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.
摘要翻译： 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。
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公开(公告)号：US07991228B2
公开(公告)日：2011-08-02
申请号：US12780857
申请日：2010-05-14
申请人： Andrew Blake , Antonio Criminisi , Geoffrey Cross , Vladimir Kolmogorov , Carsten Curt Eckard Rother
发明人： Andrew Blake , Antonio Criminisi , Geoffrey Cross , Vladimir Kolmogorov , Carsten Curt Eckard Rother
IPC分类号： G06K9/34
CPC分类号： G06K9/00234 , G06K9/342 , G06K9/38 , G06K9/4652 , G06T7/11 , G06T7/162 , G06T7/194 , G06T2207/10021 , G06T2207/10024 , G06T2207/20072
摘要： 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.
摘要翻译： 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。
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公开(公告)号：US20100220921A1
公开(公告)日：2010-09-02
申请号：US12780857
申请日：2010-05-14
申请人： Andrew Blake , Antonio Criminisi , Geoffrey Cross , Vladimir Kolmogorov , Carsten Curt Eckard Rother
发明人： Andrew Blake , Antonio Criminisi , Geoffrey Cross , Vladimir Kolmogorov , Carsten Curt Eckard Rother
IPC分类号： G06K9/00
CPC分类号： G06K9/00234 , G06K9/342 , G06K9/38 , G06K9/4652 , G06T7/11 , G06T7/162 , G06T7/194 , G06T2207/10021 , G06T2207/10024 , G06T2207/20072
摘要： 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.
摘要翻译： 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。
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公开(公告)号：US07720282B2
公开(公告)日：2010-05-18
申请号：US11195027
申请日：2005-08-02
申请人： Andrew Blake , Antonio Criminisi , Geoffrey Cross , Vladimir Kolmogorov , Carsten Curt Eckard Rother
发明人： Andrew Blake , Antonio Criminisi , Geoffrey Cross , Vladimir Kolmogorov , Carsten Curt Eckard Rother
IPC分类号： G06K9/34
CPC分类号： G06K9/00234 , G06K9/342 , G06K9/38 , G06K9/4652 , G06T7/11 , G06T7/162 , G06T7/194 , G06T2207/10021 , G06T2207/10024 , G06T2207/20072
摘要： 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.
摘要翻译： 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。
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公开(公告)号：US08103093B2
公开(公告)日：2012-01-24
申请号：US12689246
申请日：2010-01-19
IPC分类号： G06K9/34
CPC分类号： G06K9/00234 , G06K9/00261 , G06T7/11 , G06T7/143 , G06T7/162 , G06T7/174 , G06T7/254 , G06T2207/10016 , G06T2207/20121
摘要： Segmentation of foreground from background layers in an image may be provided by a segmentation process which may be based on one or more factors including motion, color, contrast, and the like. Color, motion, and optionally contrast information may be probabilistically fused to infer foreground and/or background layers accurately and efficiently. A likelihood of motion vs. non-motion may be automatically learned from training data and then fused with a contrast-sensitive color model. Segmentation may then be solved efficiently by an optimization algorithm such as a graph cut. Motion events in image sequences may be detected without explicit velocity computation.
摘要翻译： 可以通过可以基于一个或多个因素（包括运动，颜色，对比度等）的分割过程来提供来自图像中的背景层的前景分割。 颜色，运动和任选的对比度信息可以被概率地融合到准确和有效地推断前景和/或背景层。 运动与非运动的可能性可以从训练数据自动学习，然后与对比度敏感色彩模型融合。 然后可以通过诸如图形切割的优化算法有效地解决分割。 可以在没有显式速度计算的情况下检测图像序列中的运动事件。
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公开(公告)号：US20100119147A1
公开(公告)日：2010-05-13
申请号：US12689246
申请日：2010-01-19
IPC分类号： G06K9/34
CPC分类号： G06K9/00234 , G06K9/00261 , G06T7/11 , G06T7/143 , G06T7/162 , G06T7/174 , G06T7/254 , G06T2207/10016 , G06T2207/20121
摘要： Segmentation of foreground from background layers in an image may be provided by a segmentation process which may be based on one or more factors including motion, color, contrast, and the like. Color, motion, and optionally contrast information may be probabilistically fused to infer foreground and/or background layers accurately and efficiently. A likelihood of motion vs. non-motion may be automatically learned from training data and then fused with a contrast-sensitive color model. Segmentation may then be solved efficiently by an optimization algorithm such as a graph cut. Motion events in image sequences may be detected without explicit velocity computation.
摘要翻译： 可以通过可以基于一个或多个因素（包括运动，颜色，对比度等）的分割过程来提供来自图像中的背景层的前景分割。 颜色，运动和任选的对比度信息可以被概率地融合到准确和有效地推断前景和/或背景层。 运动与非运动的可能性可以从训练数据自动学习，然后与对比度敏感色彩模型融合。 然后可以通过诸如图形切割的优化算法有效地解决分割。 可以在没有显式速度计算的情况下检测图像序列中的运动事件
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公开(公告)号：US07676081B2
公开(公告)日：2010-03-09
申请号：US11252017
申请日：2005-10-17
IPC分类号： G06K9/34
CPC分类号： G06K9/00234 , G06K9/00261 , G06T7/11 , G06T7/143 , G06T7/162 , G06T7/174 , G06T7/254 , G06T2207/10016 , G06T2207/20121
摘要： Segmentation of foreground from background layers in an image may be provided by a segmentation process which may be based on one or more factors including motion, color, contrast, and the like. Color, motion, and optionally contrast information may be probabilistically fused to infer foreground and/or background layers accurately and efficiently. A likelihood of motion vs. non-motion may be automatically learned from training data and then fused with a contrast-sensitive color model. Segmentation may then be solved efficiently by an optimization algorithm such as a graph cut.
摘要翻译： 可以通过可以基于一个或多个因素（包括运动，颜色，对比度等）的分割过程来提供来自图像中的背景层的前景分割。 颜色，运动和任选的对比度信息可以被概率地融合到准确和有效地推断前景和/或背景层。 运动与非运动的可能性可以从训练数据自动学习，然后与对比度敏感色彩模型融合。 然后可以通过诸如图形切割的优化算法有效地解决分割。
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公开(公告)号：US20060285747A1
公开(公告)日：2006-12-21
申请号：US11252017
申请日：2005-10-17
IPC分类号： G06K9/34
CPC分类号： G06K9/00234 , G06K9/00261 , G06T7/11 , G06T7/143 , G06T7/162 , G06T7/174 , G06T7/254 , G06T2207/10016 , G06T2207/20121
摘要： Segmentation of foreground from background layers in an image may be provided by a segmentation process which may be based on one or more factors including motion, color, contrast, and the like. Color, motion, and optionally contrast information may be probabilistically fused to infer foreground and/or background layers accurately and efficiently. A likelihood of motion vs. non-motion may be automatically learned from training data and then fused with a contrast-sensitive color model. Segmentation may then be solved efficiently by an optimization algorithm such as a graph cut.
摘要翻译： 可以通过可以基于一个或多个因素（包括运动，颜色，对比度等）的分割过程来提供来自图像中的背景层的前景分割。 颜色，运动和任选的对比度信息可以被概率地融合到准确和有效地推断前景和/或背景层。 运动与非运动的可能性可以从训练数据自动学习，然后与对比度敏感色彩模型融合。 然后可以通过诸如图形切割的优化算法有效地解决分割。
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公开(公告)号：US07512262B2
公开(公告)日：2009-03-31
申请号：US11066946
申请日：2005-02-25
申请人： Antonio Criminisi , Andrew Blake , Geoffrey Cross
发明人： Antonio Criminisi , Andrew Blake , Geoffrey Cross
IPC分类号： G06K9/00
CPC分类号： G06K9/00241
摘要： Images of the same scene from multiple cameras may be use to generate a stereo disparity map. At least a portion of the stereo disparity map may be compared to a kernel image to detect and/or determine the location of an object in the disparity map. The kernel image is an array of pixel values which represent the stereo disparity of an object to be located, more particularly, the kernel image indicates the 3-dimensional surface shape of the object to be located from a point of view. The disparity map containing the located object may be process to manipulate the display of the stereo-based image and/or an input image. For example, the display of the image may be cropped and/or zoomed, areas of the image that are not the located object may be modified, an object such as an emoticon or smart-emoticon may be virtually inserted into the three dimensions of the image and may interact with the object, the location of the object in the image may localize further searches, presence of the located object in the image may indicate selected storing of the image and/or image indexing, and/or the located object in the image may be used as a non-standard input device to a computing system.
摘要翻译： 可以使用来自多个摄像机的相同场景的图像来生成立体视差图。 可以将立体视差图的至少一部分与核心图像进行比较，以检测和/或确定视差图中对象的位置。 内核图像是表示要被定位的对象的立体视差的像素值的阵列，更具体地说，内核图像表示从一个角度来定位的对象的三维表面形状。 包含定位对象的视差图可以是处理基于立体图像和/或输入图像的显示的处理。 例如，可以裁剪和/或缩放图像的显示，可以修改不是定位对象的图像区域，诸如表情符号或智能表情符号的对象可以被虚拟地插入到 图像并且可以与对象交互，图像中的对象的位置可以定位进一步的搜索，在图像中定位的对象的存在可以指示选择的图像和/或图像索引的存储和/或定位的对象在 图像可以用作计算系统的非标准输入设备。
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公开(公告)号：US20060193509A1
公开(公告)日：2006-08-31
申请号：US11066946
申请日：2005-02-25
申请人： Antonio Criminisi , Andrew Blake , Geoffrey Cross
发明人： Antonio Criminisi , Andrew Blake , Geoffrey Cross
IPC分类号： G06K9/00
CPC分类号： G06K9/00241
摘要： Images of the same scene from multiple cameras may be use to generate a stereo disparity map. At least a portion of the stereo disparity map may be compared to a kernel image to detect and/or determine the location of an object in the disparity map. The kernel image is an array of pixel values which represent the stereo disparity of an object to be located, more particularly, the kernel image indicates the 3-dimensional surface shape of the object to be located from a point of view. The disparity map containing the located object may be process to manipulate the display of the stereo-based image and/or an input image. For example, the display of the image may be cropped and/or zoomed, areas of the image that are not the located object may be modified, an object such as an emoticon or smart-emoticon may be virtually inserted into the three dimensions of the image and may interact with the object, the location of the object in the image may localize further searches, presence of the located object in the image may indicate selected storing of the image and/or image indexing, and/or the located object in the image may be used as a non-standard input device to a computing system.
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