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    • 22. 发明授权
    • Image region filling by exemplar-based inpainting
    • 图像区域填充通过基于示例的修复
    • US07551181B2
    • 2009-06-23
    • US11095138
    • 2005-03-30
    • Antonio CriminisiPatrick PerezKentaro ToyamaMichel GangnetAndrew Blake
    • Antonio CriminisiPatrick PerezKentaro ToyamaMichel GangnetAndrew Blake
    • G09G5/00
    • G06T11/001G06T11/40
    • An example-based filling system identifies appropriate filling material to replace a destination region in an image and fills the destination region using this material, thereby alleviating or minimizing the amount of manual editing required to fill a destination region in image. Tiles of image data are borrowed from the proximity of the destination region or some other source to generate new image data to fill in the region. Destination regions may be designated by user input (e.g., selection of an image region by a user) or by other means (e.g., specification of a color or feature to be replaced). In addition, the order in which the destination region is filled by example tiles may be configured to emphasize the continuity of linear structures and composite textures using a type of isophote-driven image-sampling process.
    • 基于示例的填充系统识别适当的填充材料以替换图像中的目的地区域并使用该材料填充目的地区域,从而减少或最小化填充图像中的目的地区域所需的手动编辑量。 从目的地区域或某些其他源的附近借用图像数据块以生成新的图像数据以填充该区域。 目的地区域可以由用户输入(例如,用户选择图像区域)或通过其他方式(例如,要更换的颜色或特征的指定)来指定。 此外,通过示例瓦片填充目的地区域的顺序可以被配置为使用一种类型的等轴驱动图像采样处理来强调线性结构和复合纹理的连续性。
    • 24. 发明申请
    • Camera Calibration
    • 相机校准
    • US20080291282A1
    • 2008-11-27
    • US11751932
    • 2007-05-22
    • Andrew W. FitzgibbonAntonio CriminisiSrikumar Ramalingam
    • Andrew W. FitzgibbonAntonio CriminisiSrikumar Ramalingam
    • H04N17/00
    • H04N17/002G06K9/209
    • Online camera calibration methods have been proposed whereby calibration information is extracted from the images that the system captures during normal operation and is used to continually update system parameters. However, such existing methods do not cope well with structure-poor scenes having little texture and/or 3D structure such as in a home or office environment. By considering camera families (a set of cameras that are manufactured at least partially in a common manner) it is possible to provide calibration methods which are suitable for use with structure-poor scenes. A prior distribution of camera parameters for a family of cameras is estimated and used to obtain accurate calibration results for individual cameras of the camera family even where the calibration is carried out online, in an environment which is structure-poor.
    • 已经提出在线摄像机校准方法,其中从正常操作期间系统捕获的图像中提取校准信息,并用于不断地更新系统参数。 然而,这样的现有方法不能很好地解决具有很少纹理和/或3D结构的结构差的场景,例如在家庭或办公环境中。 通过考虑相机系列(一组至少部分以一般方式制造的相机),可以提供适合与结构不良的场景一起使用的校准方法。 对于一系列相机的相机参数的事先分配被估计并用于获得相机系列的各个照相机的精确校准结果,即使在结构差的环境中在线执行校准。
    • 25. 发明授权
    • Virtual image generation
    • 虚拟图像生成
    • US07257272B2
    • 2007-08-14
    • US10826981
    • 2004-04-16
    • Andrew BlakeAntonio Criminisi
    • Andrew BlakeAntonio Criminisi
    • G06K9/40H04N1/407H04N1/409G06K15/00
    • G06T7/593G06T5/50G06T2207/10012H04N13/15H04N13/279
    • Artifacts are detected in a cyclopean virtual image generated from stereo images. A disparity map is generated from the stereo images. Individual projected images are determined based on the disparity map and the corresponding stereo images. A difference map is computed between the individual projected images to indicate the artifacts. A source patch in the virtual image is defined relative to an artifact. A replacement target patch is generated using a split-patch search technique as a composite of a background exemplar patch and a foreground exemplar patch. Each exemplar patch may be identified from an image patch selected from at least one of the stereo images. The source patch of the virtual image is replaced by the replacement target patch to correct the detected artifact.
    • 在从立体图像生成的环形虚拟图像中检测人造物。 从立体图像生成视差图。 基于视差图和对应的立体图像确定个体投影图像。 在各个投影图像之间计算差异图,以指示伪像。 虚拟映像中的源补丁是相对于工件定义的。 使用分割补丁搜索技术来生成替换目标补丁作为背景示例补丁和前景示例补丁的组合。 可以从选自至少一个立体图像的图像补丁来识别每个示例性补丁。 虚拟映像的源修补程序将替换为替换目标修补程序,以更正检测到的伪像。
    • 27. 发明授权
    • Predicting joint positions
    • 预测联合职位
    • US08571263B2
    • 2013-10-29
    • US13050858
    • 2011-03-17
    • Jamie Daniel Joseph ShottonPushmeet KohliRoss Brook GirshickAndrew FitzgibbonAntonio Criminisi
    • Jamie Daniel Joseph ShottonPushmeet KohliRoss Brook GirshickAndrew FitzgibbonAntonio Criminisi
    • G06K9/00
    • G06F3/017G06K9/00362G06N5/025
    • Predicting joint positions is described, for example, to find joint positions of humans or animals (or parts thereof) in an image to control a computer game or for other applications. In an embodiment image elements of a depth image make joint position votes so that for example, an image element depicting part of a torso may vote for a position of a neck joint, a left knee joint and a right knee joint. A random decision forest may be trained to enable image elements to vote for the positions of one or more joints and the training process may use training images of bodies with specified joint positions. In an example a joint position vote is expressed as a vector representing a distance and a direction of a joint position from an image element making the vote. The random decision forest may be trained using a mixture of objectives.
    • 例如,描述关节位置的描述是为了在图像中找到人或动物(或其部分)的联合位置,以控制计算机游戏或用于其他应用。 在一个实施例中,深度图像的图像元素进行联合位置投票,使得例如描绘躯干的一部分的图像元素可以投射颈部关节,左膝关节和右膝关节的位置。 可以对随机决策林进行训练,以使图像元素能够对一个或多个关节的位置进行投票,并且训练过程可以使用具有指定关节位置的身体的训练图像。 在一个例子中,联合立场表决被表示为表示从投票的图像元素的联合位置的距离和方向的向量。 可以使用目标混合来训练随机决策林。
    • 29. 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。
    • 30. 发明授权
    • Recognizing hand poses and/or object classes
    • 识别手姿势和/或对象类
    • US08103109B2
    • 2012-01-24
    • US11765264
    • 2007-06-19
    • John WinnAntonio CriminisiAnkur AgarwalThomas Deselaers
    • John WinnAntonio CriminisiAnkur AgarwalThomas Deselaers
    • G06K9/62
    • G06K9/00355G06F3/017G06F3/0425G06K9/6282
    • There is a need to provide simple, accurate, fast and computationally inexpensive methods of object and hand pose recognition for many applications. For example, to enable a user to make use of his or her hands to drive an application either displayed on a tablet screen or projected onto a table top. There is also a need to be able to discriminate accurately between events when a user's hand or digit touches such a display from events when a user's hand or digit hovers just above that display. A random decision forest is trained to enable recognition of hand poses and objects and optionally also whether those hand poses are touching or not touching a display surface. The random decision forest uses image features such as appearance, shape and optionally stereo image features. In some cases, the training process is cost aware. The resulting recognition system is operable in real-time.
    • 需要为许多应用提供简单,准确,快速和计算上便宜的对象和手姿态识别方法。 例如,为了使用户能够利用他或她的手来驱动显示在平板电脑屏幕上或投影到桌面上的应用程序。 当用户的手或数字在该显示器的正上方移动时,当用户的手或数字触发这样的显示时,还需要能够精确地区分事件之间的事件。 训练随机决策林以识别手姿势和物体,并且还可以选择性地确定那些手姿势是触摸还是不接触显示表面。 随机决策林使用图像特征,如外观,形状和可选的立体图像特征。 在某些情况下,培训过程是意识到成本。 所得到的识别系统可以实时操作。