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    • 1. 发明申请
    • Stereo image segmentation
    • 立体图像分割
    • US20070031037A1
    • 2007-02-08
    • US11195027
    • 2005-08-02
    • Andrew BlakeAntonio CriminisiGeoffrey CrossVladimir KolmogorovCarsten Rother
    • Andrew BlakeAntonio CriminisiGeoffrey CrossVladimir KolmogorovCarsten Rother
    • G06K9/34G06K9/00
    • 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. 发明申请
    • Image Blending
    • 图像混合
    • US20090129700A1
    • 2009-05-21
    • US11997033
    • 2006-07-28
    • Carsten RotherVladimir KolmogorovAndrew Blake
    • Carsten RotherVladimir KolmogorovAndrew Blake
    • G06K9/36
    • G06T11/00
    • Previously, Poisson blending has been used for image blending including cloning an object onto a target background and blending pairs of source images together. Such Poisson blending works well in many situations. However, whilst this method is always workable, we have found that discolorations sometimes occur. We realized that these discolorations occur when the gradient of the source image is preserved too insistently, at the expense of preserving object and background color. In some situations object outlines become smeared or blurred. We develop a color preservation term and a fragility measure to address these problems. This gives a user additional control to obtain smooth compositions and reduce discoloration artifacts.
    • 以前,Poisson混合已被用于图像混合,包括将对象克隆到目标背景上,并将一组源图像混合在一起。 这种泊松混合在许多情况下运作良好。 然而,虽然这种方法总是可行的,但我们发现有时会发生变色。 我们意识到,当源图像的梯度太保守地保留对象和背景色的代价时,会发生这些变色。 在某些情况下,对象轮廓变得模糊或模糊。 我们开发一个保色术语和一个脆弱的措施来解决这些问题。 这给予用户额外的控制以获得平滑的组合物并减少变色伪影。
    • 3. 发明授权
    • Image blending
    • 图像混合
    • US08019177B2
    • 2011-09-13
    • US11997033
    • 2006-07-28
    • Carsten RotherVladimir KolmogorovAndrew Blake
    • Carsten RotherVladimir KolmogorovAndrew Blake
    • G06K9/36
    • G06T11/00
    • Previously, Poisson blending has been used for image blending including cloning an object onto a target background and blending pairs of source images together. Such Poisson blending works well in many situations. However, whilst this method is always workable, we have found that discolorations sometimes occur. We realized that these discolorations occur when the gradient of the source image is preserved too insistently, at the expense of preserving object and background color. In some situations object outlines become smeared or blurred. We develop a color preservation term and a fragility measure to address these problems. This gives a user additional control to obtain smooth compositions and reduce discoloration artifacts.
    • 以前,Poisson混合已被用于图像混合,包括将对象克隆到目标背景上,并将一组源图像混合在一起。 这种泊松混合在许多情况下运作良好。 然而,虽然这种方法总是可行的,但我们发现有时会发生变色。 我们意识到,当源图像的梯度太保守地保留对象和背景色的代价时,会发生这些变色。 在某些情况下,对象轮廓变得模糊或模糊。 我们开发一个保色术语和一个脆弱的措施来解决这些问题。 这给予用户额外的控制以获得平滑的组合物并减少变色伪影。
    • 4. 发明申请
    • Image Segmentation Using Star-Convexity Constraints
    • 使用星形凸度约束的图像分割
    • US20110274352A1
    • 2011-11-10
    • US12776082
    • 2010-05-07
    • Andrew BlakeVarun GulshanCarsten RotherAntonio Criminisi
    • Andrew BlakeVarun GulshanCarsten RotherAntonio Criminisi
    • G06K9/34
    • G06T7/11G06T7/194G06T2207/20101G06T2207/20168
    • Image segmentation using star-convexity constraints is described. In an example, user input specifies positions of one or more star centers in a foreground to be segmented from a background of an image. In embodiments, an energy function is used to express the problem of segmenting the image and that energy function incorporates a star-convexity constraint which limits the number of possible solutions. For example, the star-convexity constraint may be that, for any point p inside the foreground, all points on a shortest path (which may be geodesic or Euclidean) between the nearest star center and p also lie inside the foreground. In some examples continuous star centers such as lines are used. In embodiments a user may iteratively edit the star centers by adding brush strokes to the image in order to progressively change the star-convexity constraints and obtain an accurate segmentation.
    • 描述了使用星形凸度约束的图像分割。 在一个示例中,用户输入指定要从图像的背景分割的前景中的一个或多个星形中心的位置。 在实施例中,能量函数用于表示分割图像的问题,并且能量函数包含限制可能解决方案数量的星形 - 凸度约束。 例如,星凸约束可以是,对于前景中的任何点p,最近的星中心和p之间的最短路径上的所有点(可以是测地线或欧几里德)也位于前景内。 在一些示例中,使用诸如线的连续星形中心。 在实施例中,用户可以通过向图像中添加画笔笔触来迭代地编辑星形中心,以逐渐改变星形凸度约束并获得准确的分割。
    • 5. 发明申请
    • Image tapestry
    • 图像挂毯
    • US20060104542A1
    • 2006-05-18
    • US11213080
    • 2005-08-26
    • Andrew BlakeCarsten RotherSanjiv KumarVladimir Kolmogorov
    • Andrew BlakeCarsten 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。
    • 6. 发明授权
    • Image segmentation using star-convexity constraints
    • 使用星形凸度约束的图像分割
    • US08498481B2
    • 2013-07-30
    • US12776082
    • 2010-05-07
    • Andrew BlakeVarun GulshanCarsten RotherAntonio Criminisi
    • Andrew BlakeVarun GulshanCarsten RotherAntonio Criminisi
    • G06K9/34
    • G06T7/11G06T7/194G06T2207/20101G06T2207/20168
    • Image segmentation using star-convexity constraints is described. In an example, user input specifies positions of one or more star centers in a foreground to be segmented from a background of an image. In embodiments, an energy function is used to express the problem of segmenting the image and that energy function incorporates a star-convexity constraint which limits the number of possible solutions. For example, the star-convexity constraint may be that, for any point p inside the foreground, all points on a shortest path (which may be geodesic or Euclidean) between the nearest star center and p also lie inside the foreground. In some examples continuous star centers such as lines are used. In embodiments a user may iteratively edit the star centers by adding brush strokes to the image in order to progressively change the star-convexity constraints and obtain an accurate segmentation.
    • 描述了使用星形凸度约束的图像分割。 在一个示例中,用户输入指定要从图像的背景分割的前景中的一个或多个星形中心的位置。 在实施例中,能量函数用于表示分割图像的问题,并且能量函数包含限制可能解决方案数量的星形 - 凸度约束。 例如,星凸约束可以是,对于前景中的任何点p,最近的星中心和p之间的最短路径上的所有点(可以是测地线或欧几里德)也位于前景内。 在一些示例中,使用诸如线的连续星形中心。 在实施例中,用户可以通过向图像中添加画笔笔触来迭代地编辑星形中心,以逐渐改变星形凸度约束并获得准确的分割。