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1. 发明授权

US09626766B2 Depth sensing using an RGB camera 有权
公开(公告)号：US09626766B2
公开(公告)日：2017-04-18
申请号：US14193079
申请日：2014-02-28
申请人： Antonio Criminisi , Duncan Paul Robertson , Peter Kontschieder , Pushmeet Kohli , Henrik Turbell , Adriana Dumitras , Indeera Munasinghe , Jamie Daniel Joseph Shotton
发明人： Antonio Criminisi , Duncan Paul Robertson , Peter Kontschieder , Pushmeet Kohli , Henrik Turbell , Adriana Dumitras , Indeera Munasinghe , Jamie Daniel Joseph Shotton
IPC分类号： G06K9/00 , G06T7/00
CPC分类号： G06T7/50 , G06T2207/10016 , G06T2207/10024 , G06T2207/20072 , G06T2207/20081
摘要： A method of sensing depth using an RGB camera. In an example method, a color image of a scene is received from an RGB camera. The color image is applied to a trained machine learning component which uses features of the image elements to assign all or some of the image elements a depth value which represents the distance between the surface depicted by the image element and the RGB camera. In various examples, the machine learning component comprises one or more entangled geodesic random decision forests.

2. 发明申请

US20130346346A1 SEMI-SUPERVISED RANDOM DECISION FORESTS FOR MACHINE LEARNING 有权
标题翻译：半自动监控机器学习的随机决策林
公开(公告)号：US20130346346A1
公开(公告)日：2013-12-26
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： G06F15/18
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。

3. 发明授权

US08571263B2 Predicting joint positions 有权
标题翻译：预测联合职位
公开(公告)号：US08571263B2
公开(公告)日：2013-10-29
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
IPC分类号： G06K9/00
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。

4. 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.

5. 发明授权

US08638985B2 Human body pose estimation 有权
标题翻译：人体姿势估计
公开(公告)号：US08638985B2
公开(公告)日：2014-01-28
申请号：US13040205
申请日：2011-03-03
申请人： Jamie Daniel Joseph Shotton , Shahram Izadi , Otmar Hilliges , David Kim , David Geoffrey Molyneaux , Matthew Darius Cook , Pushmeet Kohli , Antonio Criminisi , Ross Brook Girshick , Andrew William Fitzgibbon
发明人： Jamie Daniel Joseph Shotton , Shahram Izadi , Otmar Hilliges , David Kim , David Geoffrey Molyneaux , Matthew Darius Cook , Pushmeet Kohli , Antonio Criminisi , Ross Brook Girshick , Andrew William Fitzgibbon
IPC分类号： G06K9/00
CPC分类号： G06K9/00369
摘要： Techniques for human body pose estimation are disclosed herein. Images such as depth images, silhouette images, or volumetric images may be generated and pixels or voxels of the images may be identified. The techniques may process the pixels or voxels to determine a probability that each pixel or voxel is associated with a segment of a body captured in the image or to determine a three-dimensional representation for each pixel or voxel that is associated with a location on a canonical body. These probabilities or three-dimensional representations may then be utilized along with the images to construct a posed model of the body captured in the image.
摘要翻译：本文公开了人体姿势估计技术。可以生成诸如深度图像，剪影图像或体积图像的图像，并且可以识别图像的像素或体素。这些技术可以处理像素或体素以确定每个像素或体素与在图像中捕获的身体的片段相关联的概率，或者确定与每个像素或体素上的位置相关联的每个像素或体素的三维表示规范身体。然后可以将这些概率或三维表示与图像一起使用以构建在图像中捕获的身体的姿态模型。

6. 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。

7. 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。

8. 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。

9. 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。

10. 发明申请

US20120239174A1 Predicting Joint Positions 有权
标题翻译：预测联合位置
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
IPC分类号： G06F19/00 , G06K9/62 , G06K9/68
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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