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11. 发明授权

US09710730B2 Image registration 有权
公开(公告)号：US09710730B2
公开(公告)日：2017-07-18
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
IPC分类号： G06K9/32 , G06K9/62 , G06T7/35
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.

12. 发明申请

US20110188715A1 Automatic Identification of Image Features 审中-公开
标题翻译：图像特征的自动识别
公开(公告)号：US20110188715A1
公开(公告)日：2011-08-04
申请号：US12697785
申请日：2010-02-01
申请人： Jamie Daniel Joseph Shotton , Antonio Criminisi
发明人： Jamie Daniel Joseph Shotton , Antonio Criminisi
IPC分类号： G06K9/00
CPC分类号： G06K9/00 , G06K9/6282 , G06K2209/051 , G06T7/0012
摘要： Automatic identification of image features is described. In an embodiment, a device automatically identifies organs in a medical image using a decision forest formed of a plurality of distinct, trained decision trees. An image element from the image is applied to each of the trained decision trees to obtain a probability of the image element representing a predefined class of organ. The probabilities from each of the decision trees are aggregated and used to assign an organ classification to the image element. In another embodiment, a method of training a decision tree to identify features in an image is provided. For a selected node in the decision tree, a training image is analyzed at a plurality of locations offset from a selected image element, and one of the offsets is selected based on the results of the analysis and stored in association with the node.
摘要翻译：描述图像特征的自动识别。在一个实施例中，设备使用由多个不同的训练有素的决策树形成的决策树，自动识别医学图像中的器官。将来自图像的图像元素应用于每个经训练的决策树，以获得表示预定类别器官的图像元素的概率。来自每个决策树的概率被聚合并用于将分类器官分类给图像元素。在另一个实施例中，提供了一种训练决策树以识别图像中的特征的方法。对于决策树中的选定节点，在与所选择的图像元素偏移的多个位置处分析训练图像，并且基于分析的结果来选择偏移中的一个，并且与节点相关联地存储。

13. 发明授权

US09519868B2 Semi-supervised random decision forests for machine learning using mahalanobis distance to identify geodesic paths 有权
标题翻译：使用马哈拉诺比斯距离的机器学习的半监督随机决策树来识别测地线
公开(公告)号：US09519868B2
公开(公告)日：2016-12-13
申请号：US13528876
申请日：2012-06-21
申请人： Antonio Criminisi , Jamie Daniel Joseph Shotton
发明人： Antonio Criminisi , Jamie Daniel Joseph Shotton
IPC分类号： G06N99/00 , G06N7/00 , G06N5/02
CPC分类号： G06N99/005 , G06N5/02 , G06N7/005
摘要： Semi-supervised random decision forests for machine learning are described, for example, for interactive image segmentation, medical image analysis, and many other applications. In examples, a random decision forest comprising a plurality of hierarchical data structures is trained using both unlabeled and labeled observations. In examples, a training objective is used which seeks to cluster the observations based on the labels and similarity of the observations. In an example, a transducer assigns labels to the unlabeled observations on the basis of the clusters and certainty information. In an example, an inducer forms a generic clustering function by counting examples of class labels at leaves of the trees in the forest. In an example, an active learning module identifies regions in a feature space from which the observations are drawn using the clusters and certainty information; new observations from the identified regions are used to train the random decision forest.
摘要翻译：描述了用于机器学习的半监督随机决策树，例如用于交互式图像分割，医学图像分析和许多其他应用。在示例中，使用未标记和标记的观察来训练包括多个分级数据结构的随机决策林。在实例中，使用了一个训练目标，其目的是根据观察结果的标签和相似性对观测进行聚类。在一个示例中，传感器基于集群和确定性信息将标签分配给未标记的观察。在一个例子中，诱导者通过计算森林中树的树叶上的类标签的示例来形成通用聚类函数。在一个示例中，主动学习模块识别特征空间中的区域，使用聚类和确定性信息从中绘制观察值; 来自确定地区的新观察用于训练随机决策林。

14. 发明授权

US08954365B2 Density estimation and/or manifold learning 有权
标题翻译：密度估计和/或歧管学习
公开(公告)号：US08954365B2
公开(公告)日：2015-02-10
申请号：US13528866
申请日：2012-06-21
申请人： Antonio Criminisi , Jamie Daniel Joseph Shotton , Ender Konukoglu
发明人： Antonio Criminisi , Jamie Daniel Joseph Shotton , Ender Konukoglu
IPC分类号： G06F17/00 , 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。

15. 发明授权

US08867802B2 Automatic organ localization 有权
标题翻译：自动器官定位
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
IPC分类号： G06K9/00 , G06T7/00
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。

16. 发明申请

US20130343619A1 DENSITY ESTIMATION AND/OR MANIFOLD LEARNING 有权
标题翻译：密度估算和/或差异学习
公开(公告)号：US20130343619A1
公开(公告)日：2013-12-26
申请号：US13528866
申请日：2012-06-21
申请人： Antonio Criminisi , Jamie Daniel Joseph Shotton , Ender Konukoglu
发明人： Antonio Criminisi , Jamie Daniel Joseph Shotton , Ender Konukoglu
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。

17. 发明申请

US20120207359A1 Image Registration 有权
标题翻译：图像注册
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
IPC分类号： G06K9/00 , G06K9/32
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。

18. 发明授权

US09053571B2 Generating computer models of 3D objects 有权
标题翻译：生成3D对象的计算机模型
公开(公告)号：US09053571B2
公开(公告)日：2015-06-09
申请号：US13154288
申请日：2011-06-06
申请人： Jamie Daniel Joseph Shotton , Shahram Izadi , Otmar Hilliges , David Kim , David Molyneaux , Pushmeet Kohli , Andrew Fitzgibbon , Stephen Edward Hodges
发明人： Jamie Daniel Joseph Shotton , Shahram Izadi , Otmar Hilliges , David Kim , David Molyneaux , Pushmeet Kohli , Andrew Fitzgibbon , Stephen Edward Hodges
IPC分类号： G06T17/00 , G06T15/00 , G06T17/10 , G06T7/20
CPC分类号： G06T7/251 , G06T17/10 , G06T2200/08 , G06T2207/10028
摘要： Generating computer models of 3D objects is described. In one example, depth images of an object captured by a substantially static depth camera are used to generate the model, which is stored in a memory device in a three-dimensional volume. Portions of the depth image determined to relate to the background are removed to leave a foreground depth image. The position and orientation of the object in the foreground depth image is tracked by comparison to a preceding depth image, and the foreground depth image is integrated into the volume by using the position and orientation to determine where to add data derived from the foreground depth image into the volume. In examples, the object is hand-rotated by a user before the depth camera. Hands that occlude the object are integrated out of the model as they do not move in sync with the object due to re-gripping.
摘要翻译：描述生成3D对象的计算机模型。在一个示例中，使用由基本上静态的深度相机拍摄的对象的深度图像来生成存储在三维体积中的存储器设备中的模型。确定与背景相关的深度图像的部分被去除以留下前景深度图像。通过与前一个深度图像进行比较来跟踪前景深度图像中的对象的位置和方向，并且通过使用位置和方向来将前景深度图像集成到卷中，以确定在哪里添加从前景深度图像导出的数据进入卷。在示例中，该对象在深度相机之前由用户手动旋转。闭合对象的手从模型中集成出来，因为它们不会因为重新抓取而与对象同步移动。

19. 发明授权

US08570320B2 Using a three-dimensional environment model in gameplay 有权
标题翻译：在游戏中使用三维环境模型
公开(公告)号：US08570320B2
公开(公告)日：2013-10-29
申请号：US13017729
申请日：2011-01-31
申请人： 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： 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： G06T17/00 , G09G5/00
CPC分类号： G06F3/011 , A63F13/65 , A63F2300/1087 , A63F2300/6009 , G06T7/20 , G06T17/00 , G06T2207/10016 , G06T2207/10028 , G06T2207/30244
摘要： Use of a 3D environment model in gameplay is described. In an embodiment, a mobile depth camera is used to capture a series of depth images as it is moved around and a dense 3D model of the environment is generated from this series of depth images. This dense 3D model is incorporated within an interactive application, such as a game. The mobile depth camera is then placed in a static position for an interactive phase, which in some examples is gameplay, and the system detects motion of a user within a part of the environment from a second series of depth images captured by the camera. This motion provides a user input to the interactive application, such as a game. In further embodiments, automatic recognition and identification of objects within the 3D model may be performed and these identified objects then change the way that the interactive application operates.
摘要翻译：描述了在游戏中使用3D环境模型。在一个实施例中，移动深度相机被用来捕获一系列深度图像，因为它被移动，并且从该系列深度图像生成环境的密集3D模型。这种密集的3D模型被并入在诸如游戏的交互式应用中。然后将移动深度相机放置在用于交互式相位的静态位置，在一些示例中为游戏画面，并且系统从相机拍摄的第二系列深度图像中检测环境部分内的用户的运动。该运动向诸如游戏的交互式应用提供用户输入。在另外的实施例中，可以执行3D模型内的对象的自动识别和识别，并且这些识别的对象然后改变交互式应用程序的操作方式。

20. 发明申请

US20120194644A1 Mobile Camera Localization Using Depth Maps 有权
标题翻译：使用深度地图的移动摄像机本地化
公开(公告)号：US20120194644A1
公开(公告)日：2012-08-02
申请号：US13017474
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： H04N5/225
CPC分类号： G06T7/20 , G06T7/74 , G06T2207/10016 , G06T2207/10021 , G06T2207/10024 , G06T2207/10028 , G06T2207/30244
摘要： Mobile camera localization using depth maps is described for robotics, immersive gaming, augmented reality and other applications. In an embodiment a mobile depth camera is tracked in an environment at the same time as a 3D model of the environment is formed using the sensed depth data. In an embodiment, when camera tracking fails, this is detected and the camera is relocalized either by using previously gathered keyframes or in other ways. In an embodiment, loop closures are detected in which the mobile camera revisits a location, by comparing features of a current depth map with the 3D model in real time. In embodiments the detected loop closures are used to improve the consistency and accuracy of the 3D model of the environment.
摘要翻译：使用深度图的移动摄像机定位被描述为机器人，沉浸式游戏，增强现实和其他应用。在一个实施例中，移动深度相机在环境中跟踪，同时使用感测的深度数据形成环境的3D模型。在一个实施例中，当相机跟踪失败时，检测到该相机并且通过使用先前收集的关键帧或以其它方式来重新定位相机。在一个实施例中，通过将当前深度图与3D模型的特征实时比较，检测到环路闭合，其中移动摄像机重新访问位置。在实施例中，检测到的环路闭合用于改善环境的3D模型的一致性和准确性。

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