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公开(公告)号：US09619035B2
公开(公告)日：2017-04-11
申请号：US13040487
申请日：2011-03-04
IPC分类号： A63F13/213 , G06F3/01 , G06K9/00
CPC分类号： G06F3/017 , G06K9/00342
摘要： A gesture detection and recognition technique is described. In one example, a sequence of data items relating to the motion of a gesturing user is received. A selected set of data items from the sequence are tested against pre-learned threshold values, to determine a probability of the sequence representing a certain gesture. If the probability is greater than a predetermined value, then the gesture is detected, and an action taken. In examples, the tests are performed by a trained decision tree classifier. In another example, the sequence of data items can be compared to pre-learned templates, and the similarity between them determined. If the similarity for a template exceeds a threshold, a likelihood value associated with a future time for a gesture associated with that template is updated. Then, when the future time is reached, the gesture is detected if the likelihood value is greater than a predefined value.
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公开(公告)号：US08954365B2
公开(公告)日：2015-02-10
申请号：US13528866
申请日：2012-06-21
CPC分类号： G06K9/6232 , G06K9/6219 , G06K9/6226 , G06K9/6252
摘要： Density estimation and/or manifold learning are described, for example, for computer vision, medical image analysis, text document clustering. In various embodiments a density forest is trained using unlabeled data to estimate the data distribution. In embodiments the density forest comprises a plurality of random decision trees each accumulating portions of the training data into clusters at their leaves. In embodiments probability distributions representing the clusters at each tree are aggregated to form a forest density which is an estimate of a probability density function from which the unlabeled data may be generated. A mapping engine may use the clusters at the leaves of the density forest to estimate a mapping function which maps the unlabeled data to a lower dimensional space whilst preserving relative distances or other relationships between the unlabeled data points. A sampling engine may use the density forest to randomly sample data from the forest density.
摘要翻译： 例如，对于计算机视觉，医学图像分析，文本文档聚类来描述密度估计和/或歧管学习。 在各种实施例中，使用未标记的数据来训练密度森林以估计数据分布。 在实施例中，密度森林包括多个随机决策树，每个随机决策树将训练数据的部分在其叶片上聚集成簇。 在实施例中，表示每个树上的聚类的概率分布被聚合以形成森林密度，森林密度是可以从其生成未标记数据的概率密度函数的估计。 映射引擎可以使用密度森林叶片处的簇来估计将未标记数据映射到较低维空间的映射函数，同时保留未标记数据点之间的相对距离或其他关系。 采样引擎可以使用密度森林来从森林密度随机抽取数据。
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公开(公告)号：US08867802B2
公开(公告)日：2014-10-21
申请号：US13090108
申请日：2011-04-19
申请人： Antonio Criminisi , Jamie Daniel Joseph Shotton , Duncan Paul Robertson , Sayan D. Pathak , Steven James White , Khan Mohammed Siddiqui
发明人： Antonio Criminisi , Jamie Daniel Joseph Shotton , Duncan Paul Robertson , Sayan D. Pathak , Steven James White , Khan Mohammed Siddiqui
CPC分类号： G06T7/0048 , G06K2209/051 , G06T7/77 , G06T2207/10072 , G06T2207/20076 , G06T2207/20081 , G06T2207/30004
摘要： Automatic organ localization is described. In an example, an organ in a medical image is localized using one or more trained regression trees. Each image element of the medical image is applied to the trained regression trees to compute probability distributions that relate to a distance from each image element to the organ. At least a subset of the probability distributions are selected and aggregated to calculate a localization estimate for the organ. In another example, the regression trees are trained using training images having a predefined organ location. At each node of the tree, test parameters are generated that determine which subsequent node each training image element is passed to. This is repeated until each image element reaches a leaf node of the tree. A probability distribution is generated and stored at each leaf node, based on the distance from the leaf node's image elements to the organ.
摘要翻译： 描述自动器官定位。 在一个示例中，使用一个或多个经过训练的回归树将医学图像中的器官定位。 将医学图像的每个图像元素应用于经过训练的回归树，以计算与从每个图像元素到器官的距离相关的概率分布。 选择和聚合概率分布的至少一个子集以计算器官的定位估计。 在另一示例中，使用具有预定义的器官位置的训练图像来训练回归树。 在树的每个节点，生成测试参数，确定每个训练图像元素传递到哪个后续节点。 这是重复的，直到每个图像元素到达树的叶节点。 基于从叶节点的图像元素到器官的距离，在每个叶节点处生成并存储概率分布。
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公开(公告)号：US08724906B2
公开(公告)日：2014-05-13
申请号：US13300542
申请日：2011-11-18
申请人： Jamie Daniel Joseph Shotton , Andrew William Fitzgibbon , Jonathan James Taylor , Matthew Darius Cook
发明人： Jamie Daniel Joseph Shotton , Andrew William Fitzgibbon , Jonathan James Taylor , Matthew Darius Cook
CPC分类号： G06K9/00214 , G06T7/75 , G06T17/00
摘要： Computing pose and/or shape of a modifiable entity is described. In various embodiments a model of an entity (such as a human hand, a golf player holding a golf club, an animal, a body organ) is fitted to an image depicting an example of the entity in a particular pose and shape. In examples, an optimization process finds values of pose and/or shape parameters that when applied to the model explain the image data well. In examples the optimization process is influenced by correspondences between image elements and model points obtained from a regression engine where the regression engine may be a random decision forest. For example, the random decision forest may take elements of the image and calculate candidate correspondences between those image elements and model points. In examples the model, pose and correspondences may be used for control of various applications including computer games, medical systems, augmented reality.
摘要翻译： 描述可修改实体的计算姿势和/或形状。 在各种实施例中，将实体（诸如人的手，持有高尔夫球杆，高尔夫球杆，动物，身体器官的高尔夫球手）的模型安装在描绘特定姿势和形状的实体的示例的图像上。 在示例中，优化过程找到姿态和/或形状参数的值，当应用于模型时，可以很好地解释图像数据。 在示例中，优化过程受图像元素和从回归引擎获得的模型点之间的对应性的影响，回归引擎可以是随机决策树。 例如，随机决策树可以采用图像的元素，并计算这些图像元素和模型点之间的候选对应关系。 在示例中，模型，姿态和对应可以用于控制各种应用，包括计算机游戏，医疗系统，增强现实。
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公开(公告)号：US20130343619A1
公开(公告)日：2013-12-26
申请号：US13528866
申请日：2012-06-21
IPC分类号： G06K9/62
CPC分类号： G06K9/6232 , G06K9/6219 , G06K9/6226 , G06K9/6252
摘要： Density estimation and/or manifold learning are described, for example, for computer vision, medical image analysis, text document clustering. In various embodiments a density forest is trained using unlabeled data to estimate the data distribution. In embodiments the density forest comprises a plurality of random decision trees each accumulating portions of the training data into clusters at their leaves. In embodiments probability distributions representing the clusters at each tree are aggregated to form a forest density which is an estimate of a probability density function from which the unlabeled data may be generated. A mapping engine may use the clusters at the leaves of the density forest to estimate a mapping function which maps the unlabeled data to a lower dimensional space whilst preserving relative distances or other relationships between the unlabeled data points. A sampling engine may use the density forest to randomly sample data from the forest density.
摘要翻译： 例如，对于计算机视觉，医学图像分析，文本文档聚类来描述密度估计和/或歧管学习。 在各种实施例中，使用未标记的数据来训练密度森林以估计数据分布。 在实施例中，密度森林包括多个随机决策树，每个随机决策树将训练数据的部分在其叶片上累积成簇。 在实施例中，表示每个树上的聚类的概率分布被聚合以形成森林密度，森林密度是可以从其生成未标记数据的概率密度函数的估计。 映射引擎可以使用密度森林叶片处的簇来估计将未标记数据映射到较低维空间的映射函数，同时保留未标记数据点之间的相对距离或其他关系。 采样引擎可以使用密度森林来从森林密度随机抽取数据。
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公开(公告)号：US08401242B2
公开(公告)日：2013-03-19
申请号：US13017587
申请日：2011-01-31
申请人： Richard Newcombe , Shahram Izadi , David Molyneaux , Otmar Hilliges , David Kim , Jamie Daniel Joseph Shotton , Pushmeet Kohli , Andrew Fitzgibbon , Stephen Edward Hodges , David Alexander Butler
发明人： Richard Newcombe , Shahram Izadi , David Molyneaux , Otmar Hilliges , David Kim , Jamie Daniel Joseph Shotton , Pushmeet Kohli , Andrew Fitzgibbon , Stephen Edward Hodges , David Alexander Butler
IPC分类号： G06K9/00
CPC分类号： A63F13/00 , A63F13/06 , A63F2300/1087 , A63F2300/69 , G06K9/00 , G06K9/00664 , G06T7/251 , G06T7/30 , G06T2207/10028 , G06T2207/10048 , G06T2207/30244
摘要： Real-time camera tracking using depth maps is described. In an embodiment depth map frames are captured by a mobile depth camera at over 20 frames per second and used to dynamically update in real-time a set of registration parameters which specify how the mobile depth camera has moved. In examples the real-time camera tracking output is used for computer game applications and robotics. In an example, an iterative closest point process is used with projective data association and a point-to-plane error metric in order to compute the updated registration parameters. In an example, a graphics processing unit (GPU) implementation is used to optimize the error metric in real-time. In some embodiments, a dense 3D model of the mobile camera environment is used.
摘要翻译： 描述使用深度图的实时相机跟踪。 在一个实施例中，深度图帧由移动深度相机以每秒20帧的速度捕获，并且用于实时动态地更新一组注册参数，这些参数指定移动深度相机已经移动。 在实例中，实时相机跟踪输出用于计算机游戏应用和机器人。 在一个例子中，迭代最近点处理与投影数据关联和点到平面误差度量一起使用，以便计算更新的注册参数。 在一个例子中，使用图形处理单元（GPU）实现来实时优化误差度量。 在一些实施例中，使用移动照相机环境的密集3D模型。
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公开(公告)号：US20130028476A1
公开(公告)日：2013-01-31
申请号：US13615269
申请日：2012-09-13
申请人： Robert Matthew Craig , Tommer Leyvand , Craig Peeper , Momim M. Al-Ghosien , Matt Bronder , Oliver Williams , Ryan M. Geiss , Jamie Daniel Joseph Shotton , Johnny Lee , Mark Finocchio
发明人： Robert Matthew Craig , Tommer Leyvand , Craig Peeper , Momim M. Al-Ghosien , Matt Bronder , Oliver Williams , Ryan M. Geiss , Jamie Daniel Joseph Shotton , Johnny Lee , Mark Finocchio
IPC分类号： G06K9/62
CPC分类号： G06K9/00335 , A63F13/428 , A63F2300/1006 , A63F2300/1093 , G06F3/011 , G06F3/033 , G06K9/00369 , G06T17/10 , G06T19/00
摘要： A method of tracking a target includes receiving from a source a depth image of a scene including the human subject. The depth image includes a depth for each of a plurality of pixels. The method further includes identifying pixels of the depth image that belong to the human subject and deriving from the identified pixels of the depth image one or more machine readable data structures representing the human subject as a body model including a plurality of shapes.
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公开(公告)号：US20120239174A1
公开(公告)日：2012-09-20
申请号：US13050858
申请日：2011-03-17
申请人： Jamie Daniel Joseph Shotton , Pushmeet Kohli , Ross Brook Girshick , Andrew Fitzgibbon , Antonio Criminisi
发明人： Jamie Daniel Joseph Shotton , Pushmeet Kohli , Ross Brook Girshick , Andrew Fitzgibbon , Antonio Criminisi
CPC分类号： G06F3/017 , G06K9/00362 , G06N5/025
摘要： 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.
摘要翻译： 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。
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公开(公告)号：US20120225719A1
公开(公告)日：2012-09-06
申请号：US13040487
申请日：2011-03-04
CPC分类号： G06F3/017 , G06K9/00342
摘要： A gesture detection and recognition technique is described. In one example, a sequence of data items relating to the motion of a gesturing user is received. A selected set of data items from the sequence are tested against pre-learned threshold values, to determine a probability of the sequence representing a certain gesture. If the probability is greater than a predetermined value, then the gesture is detected, and an action taken. In examples, the tests are performed by a trained decision tree classifier. In another example, the sequence of data items can be compared to pre-learned templates, and the similarity between them determined. If the similarity for a template exceeds a threshold, a likelihood value associated with a future time for a gesture associated with that template is updated. Then, when the future time is reached, the gesture is detected if the likelihood value is greater than a predefined value.
摘要翻译： 描述手势检测和识别技术。 在一个示例中，接收与手势用户的运动相关的数据项的序列。 根据预先学习的阈值测试来自序列的所选择的一组数据项，以确定表示某个手势的序列的概率。 如果概率大于预定值，则检测到手势，并采取动作。 在示例中，测试由经过训练的决策树分类器执行。 在另一个例子中，可以将数据项的序列与预先学习的模板进行比较，并确定它们之间的相似性。 如果模板的相似度超过阈值，则更新与与该模板相关联的手势的未来时间相关联的似然值。 然后，当达到未来时间时，如果似然值大于预定值，则检测手势。
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公开(公告)号：US20120207359A1
公开(公告)日：2012-08-16
申请号：US13025500
申请日：2011-02-11
申请人： Ender Konukoglu , Sayan Pathak , Khan Mohammad Siddiqui , Antonio Criminisi , Steven White , Jamie Daniel Joseph Shotton , Duncan Paul Robertson
发明人： Ender Konukoglu , Sayan Pathak , Khan Mohammad Siddiqui , Antonio Criminisi , Steven White , Jamie Daniel Joseph Shotton , Duncan Paul Robertson
CPC分类号： G06K9/6277 , G06K9/6282 , G06T7/35 , G06T2207/10072 , G06T2207/10116 , G06T2207/10132 , G06T2207/20072 , G06T2207/20076 , G06T2207/20081 , G06T2207/30004
摘要： Image registration is described. In an embodiment an image registration system executes automatic registration of images, for example medical images. In an example, semantic information is computed for each of the images to be registered comprising information about the types of objects in the images and the certainty of that information. In an example a mapping is found to register the images which takes into account the intensities of the image elements as well as the semantic information in a manner which is weighted by the certainty of that semantic information. For example, the semantic information is computed by estimating posterior distributions for the locations of anatomical structures by using a regression forest and transforming the posterior distributions into a probability map. In an example the mapping is found as a global point of inflection of an energy function, the energy function having a term related to the semantic information.
摘要翻译： 描述图像注册。 在一个实施例中，图像注册系统执行图像的自动注册，例如医学图像。 在一个示例中，为要注册的每个图像计算语义信息，包括关于图像中的对象的类型和该信息的确定性的信息。 在一个示例中，发现映射以以该语义信息的确定性加权的方式注册考虑了图像元素的强度以及语义信息的图像。 例如，通过使用回归森林估计解剖结构的位置的后验分布并将后验分布变换为概率图来计算语义信息。 在一个示例中，映射被发现为能量函数的拐点的全局点，能量函数具有与语义信息相关的术语。
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