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    • 71. 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。
    • 72. 发明授权
    • Online camera calibration
    • 在线相机校准
    • US07671891B2
    • 2010-03-02
    • US11751932
    • 2007-05-22
    • Andrew FitzgibbonAntonio CriminisiSrikumar Ramalingam
    • Andrew 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。
    • 73. 发明授权
    • Virtual camera translation
    • 虚拟相机翻译
    • US07570803B2
    • 2009-08-04
    • US10763453
    • 2004-01-23
    • Antonio CriminisiAndrew BlakePhilip H. S. TorrJamie Shotton
    • Antonio CriminisiAndrew BlakePhilip H. S. TorrJamie Shotton
    • G06K9/00
    • H04N7/144G06T7/30G06T7/593G06T7/97H04N7/147
    • A multi-layer graph for dense stereo dynamic programming can improve synthesis of cyclopean virtual images by distinguishing between stereo disparities causes by occlusion and disparities caused by non-fronto-parallel surfaces. In addition, cyclopean virtual image processing may be combined with simulation of three-dimensional translation of a virtual camera to assist in aligning the user's gaze with the virtual camera. Such translation may include without limitation one or more of the following: horizontal (e.g., left and right) translation of the virtual camera, vertical translation (e.g., up and down) of the virtual camera, and axial translation (e.g., toward the subject and away from the subject) of the virtual camera.
    • 用于密集立体动态规划的多层图可以通过区分由闭塞引起的立体差异和由非前平行表面引起的差异来改善环形虚拟图像的合成。 此外,环形虚拟图像处理可以与虚拟相机的三维平移的仿真结合起来,以帮助将用户的注视与虚拟相机对准。 这样的翻译可以包括但不限于以下的一个或多个:虚拟相机的水平(例如,左和右)平移,虚拟相机的垂直平移(例如,上下)以及轴向平移(例如朝向主体 并远离主题)的虚拟相机。
    • 74. 发明申请
    • Recognizing Hand Poses and/or Object Classes
    • 识别手势和/或对象类
    • US20080317331A1
    • 2008-12-25
    • US11765264
    • 2007-06-19
    • John WinnAntonio CriminisiAnkur AgarwalThomas Deselaers
    • John WinnAntonio CriminisiAnkur AgarwalThomas Deselaers
    • G06K9/00
    • 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。 对随机决策林进行训练,以便能够识别手姿势和物体,并且还可以选择地确定那些手姿势是否触及或不接触显示表面。 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。
    • 75. 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。